5 basic skills in problem solving related to technology

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A guide to problem-solving techniques, steps, and skills

You might associate problem-solving with the math exercises that a seven-year-old would do at school. But problem-solving isn’t just about math — it’s a crucial skill that helps everyone make better decisions in everyday life or work.

Problem-solving involves finding effective solutions to address complex challenges, in any context they may arise.

Unfortunately, structured and systematic problem-solving methods aren’t commonly taught. Instead, when solving a problem, PMs tend to rely heavily on intuition. While for simple issues this might work well, solving a complex problem with a straightforward solution is often ineffective and can even create more problems.

In this article, you’ll learn a framework for approaching problem-solving, alongside how you can improve your problem-solving skills.

The 7 steps to problem-solving

When it comes to problem-solving there are seven key steps that you should follow: define the problem, disaggregate, prioritize problem branches, create an analysis plan, conduct analysis, synthesis, and communication.

1. Define the problem

Problem-solving begins with a clear understanding of the issue at hand. Without a well-defined problem statement, confusion and misunderstandings can hinder progress. It’s crucial to ensure that the problem statement is outcome-focused, specific, measurable whenever possible, and time-bound.

Additionally, aligning the problem definition with relevant stakeholders and decision-makers is essential to ensure efforts are directed towards addressing the actual problem rather than side issues.

2. Disaggregate

Complex issues often require deeper analysis. Instead of tackling the entire problem at once, the next step is to break it down into smaller, more manageable components.

Various types of logic trees (also known as issue trees or decision trees) can be used to break down the problem. At each stage where new branches are created, it’s important for them to be “MECE” – mutually exclusive and collectively exhaustive. This process of breaking down continues until manageable components are identified, allowing for individual examination.

The decomposition of the problem demands looking at the problem from various perspectives. That is why collaboration within a team often yields more valuable results, as diverse viewpoints lead to a richer pool of ideas and solutions.

3. Prioritize problem branches

The next step involves prioritization. Not all branches of the problem tree have the same impact, so it’s important to understand the significance of each and focus attention on the most impactful areas. Prioritizing helps streamline efforts and minimize the time required to solve the problem.

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4. Create an analysis plan

For prioritized components, you may need to conduct in-depth analysis. Before proceeding, a work plan is created for data gathering and analysis. If work is conducted within a team, having a plan provides guidance on what needs to be achieved, who is responsible for which tasks, and the timelines involved.

5. Conduct analysis

Data gathering and analysis are central to the problem-solving process. It’s a good practice to set time limits for this phase to prevent excessive time spent on perfecting details. You can employ heuristics and rule-of-thumb reasoning to improve efficiency and direct efforts towards the most impactful work.

6. Synthesis

After each individual branch component has been researched, the problem isn’t solved yet. The next step is synthesizing the data logically to address the initial question. The synthesis process and the logical relationship between the individual branch results depend on the logic tree used.

7. Communication

The last step is communicating the story and the solution of the problem to the stakeholders and decision-makers. Clear effective communication is necessary to build trust in the solution and facilitates understanding among all parties involved. It ensures that stakeholders grasp the intricacies of the problem and the proposed solution, leading to informed decision-making.

Exploring problem-solving in various contexts

While problem-solving has traditionally been associated with fields like engineering and science, today it has become a fundamental skill for individuals across all professions. In fact, problem-solving consistently ranks as one of the top skills required by employers.

Problem-solving techniques can be applied in diverse contexts:

• Individuals — What career path should I choose? Where should I live? These are examples of simple and common personal challenges that require effective problem-solving skills
• Organizations — Businesses also face many decisions that are not trivial to answer. Should we expand into new markets this year? How can we enhance the quality of our product development? Will our office accommodate the upcoming year’s growth in terms of capacity?
• Societal issues — The biggest world challenges are also complex problems that can be addressed with the same technique. How can we minimize the impact of climate change? How do we fight cancer?

Despite the variation in domains and contexts, the fundamental approach to solving these questions remains the same. It starts with gaining a clear understanding of the problem, followed by decomposition, conducting analysis of the decomposed branches, and synthesizing it into a result that answers the initial problem.

Real-world examples of problem-solving

Let’s now explore some examples where we can apply the problem solving framework.

Problem: In the production of electronic devices, you observe an increasing number of defects. How can you reduce the error rate and improve the quality?

Before delving into analysis, you can deprioritize branches that you already have information for or ones you deem less important. For instance, while transportation delays may occur, the resulting material degradation is likely negligible. For other branches, additional research and data gathering may be necessary.

Once results are obtained, synthesis is crucial to address the core question: How can you decrease the defect rate?

While all factors listed may play a role, their significance varies. Your task is to prioritize effectively. Through data analysis, you may discover that altering the equipment would bring the most substantial positive outcome. However, executing a solution isn’t always straightforward. In prioritizing, you should consider both the potential impact and the level of effort needed for implementation.

By evaluating impact and effort, you can systematically prioritize areas for improvement, focusing on those with high impact and requiring minimal effort to address. This approach ensures efficient allocation of resources towards improvements that offer the greatest return on investment.

Problem : What should be my next job role?

When breaking down this problem, you need to consider various factors that are important for your future happiness in the role. This includes aspects like the company culture, our interest in the work itself, and the lifestyle that you can afford with the role.

However, not all factors carry the same weight for us. To make sense of the results, we can assign a weight factor to each branch. For instance, passion for the job role may have a weight factor of 1, while interest in the industry may have a weight factor of 0.5, because that is less important for you.

By applying these weights to a specific role and summing the values, you can have an estimate of how suitable that role is for you. Moreover, you can compare two roles and make an informed decision based on these weighted indicators.

Key problem-solving skills

This framework provides the foundation and guidance needed to effectively solve problems. However, successfully applying this framework requires the following:

• Creativity — During the decomposition phase, it’s essential to approach the problem from various perspectives and think outside the box to generate innovative ideas for breaking down the problem tree
• Decision-making — Throughout the process, decisions must be made, even when full confidence is lacking. Employing rules of thumb to simplify analysis or selecting one tree cut over another requires decisiveness and comfort with choices made
• Analytical skills — Analytical and research skills are necessary for the phase following decomposition, involving data gathering and analysis on selected tree branches
• Teamwork — Collaboration and teamwork are crucial when working within a team setting. Solving problems effectively often requires collective effort and shared responsibility
• Communication — Clear and structured communication is essential to convey the problem solution to stakeholders and decision-makers and build trust

How to enhance your problem-solving skills

Problem-solving requires practice and a certain mindset. The more you practice, the easier it becomes. Here are some strategies to enhance your skills:

• Practice structured thinking in your daily life — Break down problems or questions into manageable parts. You don’t need to go through the entire problem-solving process and conduct detailed analysis. When conveying a message, simplify the conversation by breaking the message into smaller, more understandable segments
• Regularly challenging yourself with games and puzzles — Solving puzzles, riddles, or strategy games can boost your problem-solving skills and cognitive agility.
• Engage with individuals from diverse backgrounds and viewpoints — Conversing with people who offer different perspectives provides fresh insights and alternative solutions to problems. This boosts creativity and helps in approaching challenges from new angles

Final thoughts

Problem-solving extends far beyond mathematics or scientific fields; it’s a critical skill for making informed decisions in every area of life and work. The seven-step framework presented here provides a systematic approach to problem-solving, relevant across various domains.

Now, consider this: What’s one question currently on your mind? Grab a piece of paper and try to apply the problem-solving framework. You might uncover fresh insights you hadn’t considered before.

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20 Powerful Problem-Solving Techniques for the Modern Workplace: A Comprehensive Guide

September 23rd, 2024

Here’s a fact…

Organizations that are great at solving problems are about 3.5 times more likely to grow their income faster than other companies!

But what’s so crucial about problem-solving that makes such a big impact?

What is Problem-Solving?

Problem-solving is about finding and fixing issues that stop a company from reaching its goals.

Being good at solving problems is important for businesses to do well and for people to move up in their careers.

Companies that are great at solving problems can:

• Get more work done with less waste
• Make customers happier
• Come up with new ideas
• Change quickly when the market changes

For people, getting better at solving problems can help them:

• Move up faster in their job
• Enjoy their work more
• Make better choices
• Become better leaders

The Evolution of Problem-solving Techniques

Traditional problem-solving approaches often relied on linear thinking and standardized processes . While these methods still have their place, contemporary problem-solving techniques have evolved to meet the demands of our complex, interconnected business world.

Modern problem-solving techniques emphasize:

• Systems thinking
• Cross-functional collaboration
• Data-driven decision-making
• Rapid prototyping and iteration

Adapting to fast-paced, digital environments requires a blend of traditional wisdom and innovative approaches. For instance, while the core principles of Six Sigma remain relevant, we now apply them in conjunction with agile methodologies and digital tools to solve problems more efficiently.

Key Skills for Effective Problem-solving

To excel in problem-solving, professionals need to develop a diverse skill set:

• Analytical thinking : The ability to break down complex issues into manageable components and identify root causes.
• Creativity : Generating innovative solutions and thinking outside the box.
• Communication : Clearly articulating problems and solutions to stakeholders at all levels.
• Adaptability : Remaining flexible and open to new approaches as situations evolve.

By honing these skills and applying the right problem-solving techniques, you can tackle even the most challenging business issues with confidence.

Ready to enhance your problem-solving skills? Get started with our Lean Six Sigma Green Belt training covers essential techniques like Root Cause Analysis and Process Mapping. Enroll now to boost your analytical and creative problem-solving abilities.

The Fundamental Problem-Solving Process

Whether you’re troubleshooting a manufacturing issue or optimizing a business process , the fundamental problem-solving procedure remains the same. Let me walk you through the key problem-solving steps that I’ve successfully implemented across various industries.

Identifying and Defining the Problem

The first and most crucial step in any problem-solving technique is accurately identifying and defining the problem . In my experience, many organizations rush to solutions without fully understanding the root cause of their issues . To avoid this pitfall, I recommend using these root-cause analysis techniques:

• The 5 Whys : This simple yet powerful method involves asking “Why?” five times to dig deeper into the problem’s origin.
• Fishbone Diagram : Also known as the Ishikawa diagram , this visual tool helps identify potential causes of a problem across different categories.

Once you’ve identified the root cause, it’s essential to frame a clear problem statement. This statement should be specific, measurable, and actionable.

For example, instead of saying “Customer satisfaction is low”, a better problem statement would be “Customer satisfaction scores have decreased by 15% in the past quarter, primarily due to longer response times in our customer service department”.

Gathering and Analyzing Relevant Information

After defining the problem, the next step in the problem-solving procedure is to collect and analyze relevant data . In my work with companies like GE and HP, I’ve found that data-driven decision-making is crucial for effective problem-solving. Here are some data collection methods and analytical tools I frequently use:

• Surveys and interviews
• Process mapping
• Statistical analysis (e.g., regression analysis, hypothesis testing )
• Pareto charts to identify the most significant factors

Generating Potential Solutions

With a clear understanding of the problem and relevant data in hand, it’s time to generate potential solutions. This is where creative problem-solving techniques come into play. I often employ a mix of individual and group ideation techniques, such as:

• Brainstorming sessions
• Mind mapping
• SCAMPER technique (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse)
• Nominal Group Technique for team-based idea generation

Evaluating and Selecting the Best Solution

Once you have a list of potential solutions, it’s crucial to evaluate them systematically. In my workshops, I teach various decision-making frameworks , including:

• Decision matrices
• Cost-benefit analysis
• SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)

It’s also essential to conduct a thorough risk assessment of each potential solution. This helps in identifying and mitigating any potential negative consequences before implementation.

Implementing and Monitoring the Solution

The final step in the problem-solving process is implementation and monitoring. This involves:

• Developing a detailed action plan with clear responsibilities and timelines
• Implementing the solution on a small scale ( pilot test ) when possible
• Establishing key performance indicators (KPIs) to measure the solution’s effectiveness
• Regularly monitoring and adjusting the solution as needed

Individual Problem-Solving Techniques

From optimizing manufacturing processes to streamlining business operations, I’ve learned that having a diverse toolkit of problem-solving techniques is crucial for success. In this section, I’ll share some of the most effective individual problem-solving techniques I’ve used and taught in my workshops worldwide.

Analytical Techniques

• SWOT Analysis SWOT Analysis is a versatile problem-solving technique that I frequently use when helping organizations identify strategic opportunities. It involves analyzing Strengths, Weaknesses, Opportunities, and Threats . For example, when I worked with a major tech company to improve their product development process, we used SWOT to identify internal capabilities and external market factors that could impact their innovation strategy.
• Pareto Analysis Also known as the 80/20 rule , Pareto Analysis is a powerful tool for prioritizing problems . I’ve found it particularly useful in manufacturing environments. During a project with a leading automotive supplier, we used Pareto Analysis to identify that 80% of their quality issues stemmed from just 20% of their processes, allowing us to focus our improvement efforts effectively.
• 5 Whys The 5 Whys is a simple yet profound technique for root cause analysis . By asking “why” five times, you can dig deeper into the underlying causes of a problem. I once used this method with a healthcare provider to uncover the root cause of patient wait times, which led to a 30% reduction in delays.

Creative Techniques

• Mind Mapping Mind Mapping is one of my favorite creative problem-solving techniques. It’s a visual tool that helps organize thoughts and ideas around a central concept. When working with a software company to improve its customer support process, we used mind mapping to brainstorm and categorize potential solutions, leading to a more holistic approach to customer satisfaction.
• Reverse Brainstorming This technique involves reversing the problem statement to generate new perspectives. Instead of asking “How can we improve product quality?”, we ask “How can we make the product worse?” This often leads to surprising insights. I’ve successfully used this method in workshops to help teams break out of conventional thinking patterns.
• SCAMPER Method SCAMPER (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse) is a versatile creative problem-solving technique . When consulting for a consumer goods company, we used SCAMPER to redesign a product line, resulting in innovative features that boosted sales by 15%.

Decision-Making Techniques

• Decision Matrix A Decision Matrix helps evaluate and prioritize options based on weighted criteria . I’ve found this particularly useful when working with executive teams to make complex strategic decisions. For instance, when helping a telecommunications company choose between expansion strategies, we used a decision matrix to objectively assess each option against key business objectives.
• Pros and Cons Analysis While simple, a thorough Pros and Cons Analysis can be incredibly effective. I often use this technique as a starting point in my problem-solving workshops to help teams quickly assess potential solutions before diving deeper.
• Cost-Benefit Analysis In my experience, a rigorous Cost-Benefit Analysis is crucial for justifying improvement initiatives to stakeholders. When working with a government agency to streamline its operations, we used this technique to demonstrate the long-term financial benefits of process improvements, securing buy-in for a major transformation project.

Case Study: Revolutionizing Inventory Management

A few years ago, I worked with a large electronics manufacturer facing significant inventory management challenges . Here’s how we applied multiple techniques to solve their problem:

• We started with a SWOT Analysis to understand their current inventory management system’s strengths and weaknesses.
• Using Pareto Analysis , we identified that 80% of their excess inventory issues were related to just 20% of their product lines.
• We applied the 5 Whys technique to dig into the root causes of overstocking in these key product lines.
• To generate innovative solutions, we used Mind Mapping and the SCAMPER method in brainstorming sessions with cross-functional teams.
• Finally, we employed a Decision Matrix to evaluate and select the most promising solutions, followed by a detailed Cost-Benefit Analysis to justify the implementation.

The result? The company reduced excess inventory by 40% within six months, leading to significant cost savings and improved cash flow.

Team-Based Problem-Solving Techniques

I’ve seen firsthand how team-based problem-solving techniques can unlock innovative solutions and drive transformative change.

Collaborative Techniques

• Brainstorming Brainstorming remains one of the most popular problem-solving techniques in the workplace . The key to effective brainstorming is creating an environment where all ideas are welcomed and judgment is suspended. For example, during a project with a major automotive manufacturer, a brainstorming session led to a novel approach to supply chain optimization, resulting in a 15% reduction in lead times.
• Nominal Group Technique The Nominal Group Technique is a structured brainstorming method that I often use when working with diverse teams. This technique involves individual idea generation followed by group discussion and voting. I found this particularly effective when helping a healthcare provider redesign their patient intake process. By giving equal voice to frontline staff and administrators, we developed a solution that improved patient satisfaction scores by 30%.
• Delphi Method For complex problems requiring expert input, the Delphi Method is one of my go-to problem-solving strategies. This technique involves multiple rounds of anonymous questionnaires and feedback. I’ve successfully employed this method in long-term strategic planning for various organizations. In one instance, we used the Delphi Method to help a technology company forecast future market trends, leading to a successful product diversification strategy .

Structured Problem-Solving Approaches

• Six Thinking Hats Edward de Bono’s Six Thinking Hats is a powerful technique for looking at problems from multiple perspectives . I’ve integrated this method into many of my Six Sigma workshops. When working with a financial services firm to improve their risk assessment process, we used the Six Thinking Hats approach to ensure we considered emotional, creative, and critical viewpoints, resulting in a more robust risk management framework.
• Design Thinking Design Thinking is an iterative problem-solving process that I’ve found particularly useful for customer-centric challenges. During a project with a major e-commerce platform, we employed Design Thinking to reimagine their user experience. By empathizing with users, defining pain points, ideating solutions, prototyping, and testing, we developed an interface that increased user engagement by 25%.
• Lean Problem-Solving Rooted in the Toyota Production System, Lean Problem-Solving focuses on eliminating waste and improving efficiency . I’ve applied this methodology extensively in manufacturing environments. For instance, when working with a consumer electronics manufacturer, we used Lean Problem-Solving techniques to streamline their production line, resulting in a 20% increase in throughput and significant cost savings.

Conflict Resolution Techniques

• Win-Win Approach The Win-Win Approach is crucial for resolving conflicts in team problem-solving scenarios. I always emphasize this technique in my leadership workshops. During a merger between two competing departments at a large corporation, we used the Win-Win Approach to find solutions that benefited both parties, leading to a smoother integration and improved overall performance.
• Compromise and Negotiation Effective compromise and negotiation skills are essential in team-based problem-solving . I’ve coached numerous executives on these techniques. In one instance, when mediating a dispute between a company and its suppliers, our negotiation approach led to a mutually beneficial agreement that strengthened the supply chain and reduced costs for both parties.
• Mediation As a neutral third party, mediation can be a powerful tool for resolving team conflicts. I’ve often played the role of mediator in complex organizational disputes. For example, when resolving a conflict between marketing and product development teams at a software company, our mediation process not only solved the immediate issue but also established better communication channels for future collaboration.

Want to master advanced problem-solving methods for complex organizational challenges? Our Lean Six Sigma Black Belt program delves deep into statistical tools and leadership techniques.

Industry-Specific Problem-Solving Applications

What I’ve learned is that while the core principles of problem-solving remain consistent, their application can vary significantly depending on the industry context. Let’s talk about some industry-specific problem-solving techniques that I’ve found particularly effective in my consulting work.

Manufacturing and Operations

• Six Sigma Six Sigma is a data-driven problem-solving technique that I’ve implemented extensively in manufacturing environments. During my consulting time, we used Six Sigma to reduce defects in a production line by 99.99%, resulting in millions of dollars in savings. The DMAIC (Define, Measure, Analyze, Improve, Control) framework of Six Sigma provides a structured approach to identifying and solving complex manufacturing problems.
• Kaizen Kaizen, or continuous improvement , is another powerful problem-solving technique in manufacturing. I’ve facilitated numerous Kaizen events, including one at a major automotive parts supplier where we reduced setup times by 50%. The key to Kaizen’s success is its focus on small, incremental improvements that add up to significant gains over time.

Technology and Software Development

• Agile Methodologies In the fast-paced world of tech, Agile methodologies have revolutionized problem-solving. When working with a leading software company, we implemented Scrum, an Agile framework , to improve their product development process. This resulted in a 30% reduction in time-to-market for new features and increased customer satisfaction.
• A/B Testing A/B testing is a problem-solving technique I often recommend for digital products. In a project with an e-commerce platform, we used A/B testing to optimize their checkout process, leading to a 15% increase in conversion rates. This method allows for data-driven decision-making in real-time , which is crucial in the rapidly evolving tech landscape.
• Root Cause Analysis (RCA) In healthcare, patient safety is paramount, making Root Cause Analysis a critical problem-solving technique. I once worked with a large hospital to implement RCA in their medication error reporting system. This led to a 40% reduction in medication errors over six months by identifying and addressing systemic issues.
• Plan-Do-Study-Act (PDSA) Cycle The PDSA cycle is another effective problem-solving technique in healthcare . When helping a clinic improve its patient wait times, we used PDSA to test and refine various interventions. This iterative approach allowed us to reduce average wait times by 25% while ensuring that the changes didn’t negatively impact patient care quality.

Finance and Business Strategy

• Scenario Planning In the volatile world of finance, scenario planning is a crucial problem-solving technique. I’ve used this method with several financial institutions to prepare for potential market disruptions. For instance, we helped a regional bank develop robust contingency plans for various economic scenarios, which proved invaluable during the 2008 financial crisis.
• Porter’s Five Forces Porter’s Five Forces is a strategic problem-solving framework I often employ when working on business strategy issues. In a project with a retail chain facing increasing competition, we used this model to analyze the competitive landscape and identify new market opportunities, leading to a successful expansion strategy.

Case Study: Revolutionizing Manufacturing with Industry 4.0

I worked with a large manufacturing company that was struggling with efficiency and quality issues. Here’s how we applied multiple techniques to solve their problems:

• We started with a Six Sigma DMAIC project to identify the root causes of quality issues.
• Implemented Kaizen events to drive continuous improvement on the shop floor.
• Utilized Agile methodologies to develop a custom IoT solution for real-time monitoring of production lines.
• Employed A/B testing to optimize the user interface of the monitoring system for maximum operator efficiency.

The result? A 40% reduction in defect rates, a 25% improvement in overall equipment effectiveness, and a successful transition into Industry 4.0 practices.

By understanding and applying these industry-specific problem-solving techniques, you can tackle the unique challenges in your field more effectively. Remember, the key is to adapt these methods to your specific context and combine them when necessary for optimal results.

Problem-solving in Remote and Digital Environments

I’ve witnessed firsthand the dramatic shift toward remote and digital work environments. This transition has brought new challenges to the problem-solving landscape and opened up exciting opportunities for innovation.

Challenges of Virtual Problem-Solving

• Communication Barriers One of the biggest hurdles I’ve encountered in remote problem-solving is the lack of face-to-face interaction. Non-verbal cues, crucial in understanding team dynamics, are often lost in virtual settings. During a project with a global team, we had to work extra hard to ensure clear communication across different time zones and cultures.
• Collaboration Limitations Virtual environments can sometimes hinder spontaneous collaboration . The casual “water cooler” conversations that often spark innovative ideas are less frequent. In a project, we had to deliberately create virtual spaces for informal interactions to maintain team creativity and cohesion.

Digital Tools for Remote Problem-Solving

• Virtual Whiteboards I’ve found virtual whiteboards to be indispensable for remote problem-solving. Tools like Miro or MURAL allow teams to visualize problems and solutions collaboratively. In a Six Sigma workshop I conducted for a government institution, we used a virtual whiteboard to create a detailed fishbone diagram, which helped identify the root causes of a complex process issue.
• Online Collaboration Platforms Platforms like Microsoft Teams or Slack have become central to remote problem-solving efforts . We used these tools to create dedicated channels for different aspects of our problem-solving process, from data analysis to solution brainstorming.

Techniques for Effective Virtual Brainstorming

• Silent Brainstorming Silent brainstorming has become one of my favorite techniques for virtual environments. It involves having team members independently write down ideas before sharing them. This method helps overcome the challenge of dominant voices in virtual meetings and ensures all ideas are heard. I recently used this technique which resulted in a 30% increase in the number of ideas generated compared to traditional verbal brainstorming.
• Round-Robin Ideation Round-robin ideation is another effective virtual problem-solving technique . Each team member takes turns presenting an idea, ensuring equal participation. In a project we used this method to tackle a complex supply chain issue, resulting in a diverse range of solutions that we might not have uncovered in a less structured format.

Tips for Effective Remote Problem-Solving

• Establish clear communication protocols
• Use visual aids and collaborative tools
• Schedule regular check-ins and informal virtual meetings
• Encourage active participation from all team members
• Be mindful of time zones and cultural differences
• Utilize asynchronous communication when appropriate
• Invest in reliable technology and provide the necessary training

By adapting our problem-solving techniques to remote and digital environments, we can overcome the challenges and harness the unique advantages of virtual collaboration . In my experience, remote problem-solving can lead to more diverse perspectives and innovative solutions when done right.

Looking to implement effective problem-solving strategies across your entire organization? Our Lean Six Sigma Champion Leadership program equips executives with the skills to drive continuous improvement and foster a culture of problem-solving.

Emerging Trends and Technologies in Problem-Solving

The emergence of new technologies has revolutionized how we approach challenges, offering unprecedented opportunities for efficiency and innovation.

Data-Driven Problem-Solving

• Big Data Analytics The explosion of big data has transformed problem-solving techniques in the business. During a recent project with a major retailer, we leveraged big data analytics to optimize their supply chain. By analyzing vast amounts of historical sales data, weather patterns, and social media trends, we developed a predictive model that reduced stock-outs by 35% while minimizing excess inventory.
• Predictive Modeling Predictive modeling has become one of the best problem-solving techniques in my toolkit. In a project with a telecommunications company, we used predictive modeling to anticipate network outages before they occurred. This proactive approach allowed the company to reduce downtime by 50%, significantly improving customer satisfaction.

AI and Machine Learning in Problem-Solving

• Pattern Recognition AI-powered pattern recognition has dramatically enhanced our ability to identify complex problems. In a recent manufacturing project, we implemented an AI system that could detect subtle anomalies in product quality that human inspectors often miss. This led to a 40% reduction in defect rates and substantial cost savings.
• Automated Decision-Making Automated decision-making systems are revolutionizing how we solve routine problems. For instance, in a project with a financial services firm, we developed an AI-driven system for credit approval. This not only sped up the process but also improved the accuracy of credit decisions by 25%.

Augmented and Virtual Reality Applications

• Simulations for Complex Problem-Solving Augmented Reality (AR) and Virtual Reality (VR) have opened up new frontiers in problem-solving, especially for complex systems. In a recent aerospace project, we used VR simulations to troubleshoot engine design issues. This allowed engineers to visualize and interact with 3D models, leading to faster problem identification and more innovative solutions.
• Virtual Collaboration Environments VR is also transforming how teams collaborate on problem-solving . In a global project for a tech giant, we used a virtual collaboration environment to bring together experts from different continents. This immersive experience facilitated better communication and idea sharing, resulting in more creative solutions to complex technical challenges.

Emerging Technologies in Problem-Solving

• Big Data Analytics
• Predictive Modeling
• AI-Powered Pattern Recognition
• Automated Decision-Making Systems
• Augmented Reality Simulations
• Virtual Reality Collaboration Environments
• Quantum Computing for Complex Calculations
• Internet of Things (IoT) for Real-Time Data Collection
• Blockchain for Transparent Problem Tracking
• Natural Language Processing for Sentiment Analysis

These emerging technologies are not just tools; they’re reshaping the very nature of problem-solving in business. As a Six Sigma practitioner, I’ve found that integrating these technologies with traditional problem-solving methods can lead to breakthrough solutions.

For instance, in a recent project with a semiconductor manufacturer, we combined Six Sigma’s DMAIC methodology with AI-driven predictive modeling . This hybrid approach allowed us to not only solve current yield issues but also predict and prevent future problems, resulting in a sustained 20% improvement in overall yield.

As we look to the future, the key to effective problem-solving will be the ability to seamlessly blend human expertise with these advanced technologies. The most successful problem solvers will be those who can harness the power of AI, VR, and big data while still applying critical thinking and creativity.

Developing and Improving Problem-Solving Skills

I can confidently say that problem-solving is not just a skill—it’s a mindset that can be continuously developed and refined . Cultivating strong problem-solving skills can transform careers and drive organizational success.

Let’s look at strategies for developing and improving your problem-solving abilities , drawing from my experiences training thousands of professionals worldwide.

Continuous Learning and Practice

• Problem-Solving Exercises and Games One of the most effective ways to enhance your problem-solving techniques is through regular practice. I often recommend brain teasers and logic puzzles to my workshop participants. For instance, during a training session, we used the “ Nine Dots Puzzle ” to illustrate the importance of thinking outside the box. These exercises help sharpen your analytical skills and encourage creative thinking.
• Application Opportunities Nothing beats real-world experience when it comes to honing your problem-solving strategies . I always encourage my clients to seek out challenging projects within their organizations. I mentored junior engineers by involving them in complex process improvement initiatives . This hands-on experience allowed them to apply various problem-solving techniques in a practical setting, accelerating their learning curve.

Cultivating a Problem-Solving Mindset

• Embracing Challenges The best problem solvers I’ve worked with, from startups to Fortune 500 companies, share one common trait: they view problems as opportunities rather than obstacles. In a recent project with a healthcare provider, we reframed a patient care issue as a chance to innovate their service delivery model. This shift in perspective led to a breakthrough solution that improved patient satisfaction scores by 40%.
• Learning from Failures Failure is an inevitable part of the problem-solving process . What sets great problem solvers apart is their ability to learn from these setbacks. I recall a project where our initial solution didn’t yield the expected results. Instead of getting discouraged, we conducted a thorough post-mortem analysis , which led to insights that ultimately drove the project’s success.

Building a Diverse Skill Set

• Cross-Functional Knowledge The most effective problem solvers are those with a broad base of knowledge. Throughout my career, I’ve consistently encouraged professionals to step outside their comfort zones. For example, I once advised a finance professional to shadow the manufacturing team. This cross-functional exposure enhanced her ability to solve interdepartmental issues, leading to more holistic solutions.
• Emotional Intelligence Technical skills are crucial, but emotional intelligence is equally important in problem-solving, especially in team settings. During a workshop, we incorporated exercises to improve empathy and communication skills. This focus on emotional intelligence led to more collaborative problem-solving sessions and better team outcomes.

Challenge : Put Your Skills to the Test

I challenge you to take on a problem in your workplace using a technique you’ve never tried before. Perhaps use the “ 5 Whys ” to dig into a recurring issue, or apply the SCAMPER method to innovate a product or process. Share your experience in the comments —I’d love to hear about your results!

Tips for Improving Problem-Solving Skills

• Practice regularly with puzzles and brain teasers
• Seek out challenging projects at work
• Reframe problems as opportunities for innovation
• Conduct post-mortem analyses on failed attempts
• Gain exposure to different departments and functions
• Develop emotional intelligence through targeted exercises
• Stay updated on industry trends and emerging technologies
• Participate in problem-solving workshops and seminars
• Mentor others to reinforce your skills
• Reflect on your problem-solving process and continuously refine it

Remember, becoming an expert problem solver is a journey, not a destination. As the business landscape evolves, so too must our problem-solving techniques.

By committing to continuous improvement and embracing new challenges, you’ll not only solve the problems of today but be prepared for the challenges of tomorrow.

Going Ahead

We’ve covered a wide range of problem-solving techniques, from the analytical rigor of Six Sigma to the creative approaches of design thinking.

We’ve explored how these methods can be applied across various industries and adapted for remote environments. We’ve also looked at emerging trends, showing how AI and big data are reshaping the landscape of problem-solving.

Key takeaways:

• The importance of a structured problem-solving process
• The power of combining analytical and creative techniques
• The value of team-based approaches in complex problem-solving
• The potential of data-driven and AI-enhanced problem-solving methods
• The necessity of continuously developing your problem-solving skills

Remember, the most effective problem solvers are those who can adapt their approach to the unique challenges they face. Whether you’re troubleshooting a manufacturing issue, optimizing a business process , or tackling a global supply chain challenge, the techniques we’ve discussed provide a robust toolkit for success.

As you move forward in your career, I encourage you to implement these problem-solving techniques in your daily work. Start with small challenges and gradually apply these methods to more complex problems. Share your learnings with your team and create a culture of continuous improvement in your organization.

The ability to solve problems effectively is more than just a skill—it’s a competitive advantage in today’s rapidly changing business landscape. By honing your problem-solving abilities , you’re not just preparing for the challenges of today, but positioning yourself as a leader for the challenges of tomorrow.

Remember, every problem is an opportunity in disguise. Happy problem-solving!

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Critical thinking and problem solving with technology.

Brief Summary: Critical thinking and problem solving is a crucial skill in a technical world that can immediately be applied to academics and careers. A highly skilled individual in this competency can choose the appropriate tool to accomplish a task, easily switch between tools, has a basic understanding of different file types, and can troubleshoot technology when it’s not working properly. They can also differentiate between true information and falsified information online and has basic proficiency in data gathering, processing and interpretation. 

Learners with proficient skills in critical thinking and problem solving should be able to: 

• Troubleshoot computers and mobile devices when issues arise, like restarting the device and checking if it requires a software or operating system update 
• Move across tools to complete a task (for example, adding PowerPoint slides into a note taking app for annotation) 
• Differentiate between legitimate and falsified information online 
• Understand basic file types and know when to use them (for example, the difference between .doc and .pdf files) 

Market/Employer Trends: Employers indicate value in employee ability to problem solve using technology, particularly related to drawing information from data to identify and solve challenges. Further, knowing how to leverage technology tools to see a problem, break it down into manageable pieces, and work toward solving is of important value. Employers expect new employees to be able to navigate across common toolsets, making decisions to use the right tool for the right task.  

Self-Evaluation: 

Key questions for reflection: 

• How comfortable are you when technology doesn’t work the way you expect?  
• Do you know basic troubleshooting skills to solve tech issues?  
• Do you know the key indicators of whether information you read online is reliable? 

Strong digital skills in this area could appear as: 

• Updating your computer after encountering a problem and resolving the issue 
• Discerning legitimate news sources from illegitimate ones to successfully meet goals 
• Converting a PowerPoint presentation into a PDF for easy access for peers who can’t use PowerPoint 
• Taking notes on a phone and seamlessly completing them on a computer

Ways to Upskill: 

Ready to grow your strength in this competency? Try: 

• Reviewing University Libraries’ resources on research and information literacy  
• Read about troubleshooting in college in the Learner Technology Handbook 
• Registering for ESEPSY 1359: Critical Thinking and Collaboration in Online Learning  

Educator Tips to Support Digital Skills: 

• Create an assignment in Carmen prompting students to find legitimate peer-reviewed research  
• Provide links to information literacy resources on research-related assignments or projects for student review 
• Develop assignments that require using more than one tech tool to accomplish a single task 

What Are Problem-Solving Skills? Definition and Examples

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Forage puts students first. Our blog articles are written independently by our editorial team. They have not been paid for or sponsored by our partners. See our full  editorial guidelines .

Why do employers hire employees? To help them solve problems. Whether you’re a financial analyst deciding where to invest your firm’s money, or a marketer trying to figure out which channel to direct your efforts, companies hire people to help them find solutions. Problem-solving is an essential and marketable soft skill in the workplace. 

So, how can you improve your problem-solving and show employers you have this valuable skill? In this guide, we’ll cover:

Problem-Solving Skills Definition

Why are problem-solving skills important, problem-solving skills examples, how to include problem-solving skills in a job application, how to improve problem-solving skills, problem-solving: the bottom line.

Problem-solving skills are the ability to identify problems, brainstorm and analyze answers, and implement the best solutions. An employee with good problem-solving skills is both a self-starter and a collaborative teammate; they are proactive in understanding the root of a problem and work with others to consider a wide range of solutions before deciding how to move forward. 

Examples of using problem-solving skills in the workplace include:

• Researching patterns to understand why revenue decreased last quarter
• Experimenting with a new marketing channel to increase website sign-ups
• Brainstorming content types to share with potential customers
• Testing calls to action to see which ones drive the most product sales
• Implementing a new workflow to automate a team process and increase productivity

Problem-solving skills are the most sought-after soft skill of 2022. In fact, 86% of employers look for problem-solving skills on student resumes, according to the National Association of Colleges and Employers Job Outlook 2022 survey . 

It’s unsurprising why employers are looking for this skill: companies will always need people to help them find solutions to their problems. Someone proactive and successful at problem-solving is valuable to any team.

“Employers are looking for employees who can make decisions independently, especially with the prevalence of remote/hybrid work and the need to communicate asynchronously,” Eric Mochnacz, senior HR consultant at Red Clover, says. “Employers want to see individuals who can make well-informed decisions that mitigate risk, and they can do so without suffering from analysis paralysis.”

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Problem-solving includes three main parts: identifying the problem, analyzing possible solutions, and deciding on the best course of action.

>>MORE: Discover the right career for you based on your skills with a career aptitude test .

Research is the first step of problem-solving because it helps you understand the context of a problem. Researching a problem enables you to learn why the problem is happening. For example, is revenue down because of a new sales tactic? Or because of seasonality? Is there a problem with who the sales team is reaching out to? 

Research broadens your scope to all possible reasons why the problem could be happening. Then once you figure it out, it helps you narrow your scope to start solving it. 

Analysis is the next step of problem-solving. Now that you’ve identified the problem, analytical skills help you look at what potential solutions there might be.

“The goal of analysis isn’t to solve a problem, actually — it’s to better understand it because that’s where the real solution will be found,” Gretchen Skalka, owner of Career Insights Consulting, says. “Looking at a problem through the lens of impartiality is the only way to get a true understanding of it from all angles.”

Decision-Making

Once you’ve figured out where the problem is coming from and what solutions are, it’s time to decide on the best way to go forth. Decision-making skills help you determine what resources are available, what a feasible action plan entails, and what solution is likely to lead to success.

On a Resume

Employers looking for problem-solving skills might include the word “problem-solving” or other synonyms like “ critical thinking ” or “analytical skills” in the job description.

“I would add ‘buzzwords’ you can find from the job descriptions or LinkedIn endorsements section to filter into your resume to comply with the ATS,” Matthew Warzel, CPRW resume writer, advises. Warzel recommends including these skills on your resume but warns to “leave the soft skills as adjectives in the summary section. That is the only place soft skills should be mentioned.”

On the other hand, you can list hard skills separately in a skills section on your resume .

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In a Cover Letter or an Interview

Explaining your problem-solving skills in an interview can seem daunting. You’re required to expand on your process — how you identified a problem, analyzed potential solutions, and made a choice. As long as you can explain your approach, it’s okay if that solution didn’t come from a professional work experience.

“Young professionals shortchange themselves by thinking only paid-for solutions matter to employers,” Skalka says. “People at the genesis of their careers don’t have a wealth of professional experience to pull from, but they do have relevant experience to share.”

Aaron Case, career counselor and CPRW at Resume Genius, agrees and encourages early professionals to share this skill. “If you don’t have any relevant work experience yet, you can still highlight your problem-solving skills in your cover letter,” he says. “Just showcase examples of problems you solved while completing your degree, working at internships, or volunteering. You can even pull examples from completely unrelated part-time jobs, as long as you make it clear how your problem-solving ability transfers to your new line of work.”

Learn How to Identify Problems

Problem-solving doesn’t just require finding solutions to problems that are already there. It’s also about being proactive when something isn’t working as you hoped it would. Practice questioning and getting curious about processes and activities in your everyday life. What could you improve? What would you do if you had more resources for this process? If you had fewer? Challenge yourself to challenge the world around you.

Think Digitally

“Employers in the modern workplace value digital problem-solving skills, like being able to find a technology solution to a traditional issue,” Case says. “For example, when I first started working as a marketing writer, my department didn’t have the budget to hire a professional voice actor for marketing video voiceovers. But I found a perfect solution to the problem with an AI voiceover service that cost a fraction of the price of an actor.”

Being comfortable with new technology — even ones you haven’t used before — is a valuable skill in an increasingly hybrid and remote world. Don’t be afraid to research new and innovative technologies to help automate processes or find a more efficient technological solution.

Collaborate

Problem-solving isn’t done in a silo, and it shouldn’t be. Use your collaboration skills to gather multiple perspectives, help eliminate bias, and listen to alternative solutions. Ask others where they think the problem is coming from and what solutions would help them with your workflow. From there, try to compromise on a solution that can benefit everyone.

If we’ve learned anything from the past few years, it’s that the world of work is constantly changing — which means it’s crucial to know how to adapt . Be comfortable narrowing down a solution, then changing your direction when a colleague provides a new piece of information. Challenge yourself to get out of your comfort zone, whether with your personal routine or trying a new system at work.

Put Yourself in the Middle of Tough Moments

Just like adapting requires you to challenge your routine and tradition, good problem-solving requires you to put yourself in challenging situations — especially ones where you don’t have relevant experience or expertise to find a solution. Because you won’t know how to tackle the problem, you’ll learn new problem-solving skills and how to navigate new challenges. Ask your manager or a peer if you can help them work on a complicated problem, and be proactive about asking them questions along the way.

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Companies always need people to help them find solutions — especially proactive employees who have practical analytical skills and can collaborate to decide the best way to move forward. Whether or not you have experience solving problems in a professional workplace, illustrate your problem-solving skills by describing your research, analysis, and decision-making process — and make it clear that you’re the solution to the employer’s current problems. 

Image Credit: Christina Morillo / Pexels 

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What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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What is Problem Solving? (Steps, Techniques, Examples)

By Status.net Editorial Team on May 7, 2023 — 5 minutes to read

What Is Problem Solving?

Definition and importance.

Problem solving is the process of finding solutions to obstacles or challenges you encounter in your life or work. It is a crucial skill that allows you to tackle complex situations, adapt to changes, and overcome difficulties with ease. Mastering this ability will contribute to both your personal and professional growth, leading to more successful outcomes and better decision-making.

Problem-Solving Steps

The problem-solving process typically includes the following steps:

• Identify the issue : Recognize the problem that needs to be solved.
• Analyze the situation : Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present.
• Generate potential solutions : Brainstorm a list of possible solutions to the issue, without immediately judging or evaluating them.
• Evaluate options : Weigh the pros and cons of each potential solution, considering factors such as feasibility, effectiveness, and potential risks.
• Select the best solution : Choose the option that best addresses the problem and aligns with your objectives.
• Implement the solution : Put the selected solution into action and monitor the results to ensure it resolves the issue.
• Review and learn : Reflect on the problem-solving process, identify any improvements or adjustments that can be made, and apply these learnings to future situations.

Defining the Problem

To start tackling a problem, first, identify and understand it. Analyzing the issue thoroughly helps to clarify its scope and nature. Ask questions to gather information and consider the problem from various angles. Some strategies to define the problem include:

• Brainstorming with others
• Asking the 5 Ws and 1 H (Who, What, When, Where, Why, and How)
• Analyzing cause and effect
• Creating a problem statement

Generating Solutions

Once the problem is clearly understood, brainstorm possible solutions. Think creatively and keep an open mind, as well as considering lessons from past experiences. Consider:

• Creating a list of potential ideas to solve the problem
• Grouping and categorizing similar solutions
• Prioritizing potential solutions based on feasibility, cost, and resources required
• Involving others to share diverse opinions and inputs

Evaluating and Selecting Solutions

Evaluate each potential solution, weighing its pros and cons. To facilitate decision-making, use techniques such as:

• SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)
• Decision-making matrices
• Pros and cons lists
• Risk assessments

After evaluating, choose the most suitable solution based on effectiveness, cost, and time constraints.

Implementing and Monitoring the Solution

Implement the chosen solution and monitor its progress. Key actions include:

• Communicating the solution to relevant parties
• Setting timelines and milestones
• Assigning tasks and responsibilities
• Monitoring the solution and making adjustments as necessary
• Evaluating the effectiveness of the solution after implementation

Utilize feedback from stakeholders and consider potential improvements. Remember that problem-solving is an ongoing process that can always be refined and enhanced.

Problem-Solving Techniques

During each step, you may find it helpful to utilize various problem-solving techniques, such as:

• Brainstorming : A free-flowing, open-minded session where ideas are generated and listed without judgment, to encourage creativity and innovative thinking.
• Root cause analysis : A method that explores the underlying causes of a problem to find the most effective solution rather than addressing superficial symptoms.
• SWOT analysis : A tool used to evaluate the strengths, weaknesses, opportunities, and threats related to a problem or decision, providing a comprehensive view of the situation.
• Mind mapping : A visual technique that uses diagrams to organize and connect ideas, helping to identify patterns, relationships, and possible solutions.

Brainstorming

When facing a problem, start by conducting a brainstorming session. Gather your team and encourage an open discussion where everyone contributes ideas, no matter how outlandish they may seem. This helps you:

• Generate a diverse range of solutions
• Encourage all team members to participate
• Foster creative thinking

When brainstorming, remember to:

• Reserve judgment until the session is over
• Encourage wild ideas
• Combine and improve upon ideas

Root Cause Analysis

For effective problem-solving, identifying the root cause of the issue at hand is crucial. Try these methods:

• 5 Whys : Ask “why” five times to get to the underlying cause.
• Fishbone Diagram : Create a diagram representing the problem and break it down into categories of potential causes.
• Pareto Analysis : Determine the few most significant causes underlying the majority of problems.

SWOT Analysis

SWOT analysis helps you examine the Strengths, Weaknesses, Opportunities, and Threats related to your problem. To perform a SWOT analysis:

• List your problem’s strengths, such as relevant resources or strong partnerships.
• Identify its weaknesses, such as knowledge gaps or limited resources.
• Explore opportunities, like trends or new technologies, that could help solve the problem.
• Recognize potential threats, like competition or regulatory barriers.

SWOT analysis aids in understanding the internal and external factors affecting the problem, which can help guide your solution.

Mind Mapping

A mind map is a visual representation of your problem and potential solutions. It enables you to organize information in a structured and intuitive manner. To create a mind map:

• Write the problem in the center of a blank page.
• Draw branches from the central problem to related sub-problems or contributing factors.
• Add more branches to represent potential solutions or further ideas.

Mind mapping allows you to visually see connections between ideas and promotes creativity in problem-solving.

Examples of Problem Solving in Various Contexts

In the business world, you might encounter problems related to finances, operations, or communication. Applying problem-solving skills in these situations could look like:

• Identifying areas of improvement in your company’s financial performance and implementing cost-saving measures
• Resolving internal conflicts among team members by listening and understanding different perspectives, then proposing and negotiating solutions
• Streamlining a process for better productivity by removing redundancies, automating tasks, or re-allocating resources

In educational contexts, problem-solving can be seen in various aspects, such as:

• Addressing a gap in students’ understanding by employing diverse teaching methods to cater to different learning styles
• Developing a strategy for successful time management to balance academic responsibilities and extracurricular activities
• Seeking resources and support to provide equal opportunities for learners with special needs or disabilities

Everyday life is full of challenges that require problem-solving skills. Some examples include:

• Overcoming a personal obstacle, such as improving your fitness level, by establishing achievable goals, measuring progress, and adjusting your approach accordingly
• Navigating a new environment or city by researching your surroundings, asking for directions, or using technology like GPS to guide you
• Dealing with a sudden change, like a change in your work schedule, by assessing the situation, identifying potential impacts, and adapting your plans to accommodate the change.
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The 5 steps of the solving problem process

August 17, 2023 by MindManager Blog

Whether you run a business, manage a team, or work in an industry where change is the norm, it may feel like something is always going wrong. Thankfully, becoming proficient in the problem solving process can alleviate a great deal of the stress that business issues can create.

Understanding the right way to solve problems not only takes the guesswork out of how to deal with difficult, unexpected, or complex situations, it can lead to more effective long-term solutions.

In this article, we’ll walk you through the 5 steps of problem solving, and help you explore a few examples of problem solving scenarios where you can see the problem solving process in action before putting it to work.

Understanding the problem solving process

When something isn’t working, it’s important to understand what’s at the root of the problem so you can fix it and prevent it from happening again. That’s why resolving difficult or complex issues works best when you apply proven business problem solving tools and techniques – from soft skills, to software.

The problem solving process typically includes:

• Pinpointing what’s broken by gathering data and consulting with team members.
• Figuring out why it’s not working by mapping out and troubleshooting the problem.
• Deciding on the most effective way to fix it by brainstorming and then implementing a solution.

While skills like active listening, collaboration, and leadership play an important role in problem solving, tools like visual mapping software make it easier to define and share problem solving objectives, play out various solutions, and even put the best fit to work.

Before you can take your first step toward solving a problem, you need to have a clear idea of what the issue is and the outcome you want to achieve by resolving it.

For example, if your company currently manufactures 50 widgets a day, but you’ve started processing orders for 75 widgets a day, you could simply say you have a production deficit.

However, the problem solving process will prove far more valuable if you define the start and end point by clarifying that production is running short by 25 widgets a day, and you need to increase daily production by 50%.

Once you know where you’re at and where you need to end up, these five steps will take you from Point A to Point B:

• Figure out what’s causing the problem . You may need to gather knowledge and evaluate input from different documents, departments, and personnel to isolate the factors that are contributing to your problem. Knowledge visualization software like MindManager can help.
• Come up with a few viable solutions . Since hitting on exactly the right solution – right away – can be tough, brainstorming with your team and mapping out various scenarios is the best way to move forward. If your first strategy doesn’t pan out, you’ll have others on tap you can turn to.
• Choose the best option . Decision-making skills, and software that lets you lay out process relationships, priorities, and criteria, are invaluable for selecting the most promising solution. Whether it’s you or someone higher up making that choice, it should include weighing costs, time commitments, and any implementation hurdles.
• Put your chosen solution to work . Before implementing your fix of choice, you should make key personnel aware of changes that might affect their daily workflow, and set up benchmarks that will make it easy to see if your solution is working.
• Evaluate your outcome . Now comes the moment of truth: did the solution you implemented solve your problem? Do your benchmarks show you achieved the outcome you wanted? If so, congratulations! If not, you’ll need to tweak your solution to meet your problem solving goal.

In practice, you might not hit a home-run with every solution you execute. But the beauty of a repeatable process like problem solving is that you can carry out steps 4 and 5 again by drawing from the brainstorm options you documented during step 2.

Examples of problem solving scenarios

The best way to get a sense of how the problem solving process works before you try it for yourself is to work through some simple scenarios.

Here are three examples of how you can apply business problem solving techniques to common workplace challenges.

Scenario #1: Manufacturing

Building on our original manufacturing example, you determine that your company is consistently short producing 25 widgets a day and needs to increase daily production by 50%.

Since you’d like to gather data and input from both your manufacturing and sales order departments, you schedule a brainstorming session to discover the root cause of the shortage.

After examining four key production areas – machines, materials, methods, and management – you determine the cause of the problem: the material used to manufacture your widgets can only be fed into your equipment once the machinery warms up to a specific temperature for the day.

Your team comes up with three possible solutions.

• Leave your machinery running 24 hours so it’s always at temperature.
• Invest in equipment that heats up faster.
• Find an alternate material for your widgets.

After weighing the expense of the first two solutions, and conducting some online research, you decide that switching to a comparable but less expensive material that can be worked at a lower temperature is your best option.

You implement your plan, monitor your widget quality and output over the following week, and declare your solution a success when daily production increases by 100%.

Scenario #2: Service Delivery

Business training is booming and you’ve had to onboard new staff over the past month. Now you learn that several clients have expressed concern about the quality of your recent training sessions.

After speaking with both clients and staff, you discover there are actually two distinct factors contributing to your quality problem:

• The additional conference room you’ve leased to accommodate your expanding training sessions has terrible acoustics
• The AV equipment you’ve purchased to accommodate your expanding workforce is on back-order – and your new hires have been making do without

You could look for a new conference room or re-schedule upcoming training sessions until after your new equipment arrives. But your team collaboratively determines that the best way to mitigate both issues at once is by temporarily renting the high-quality sound and visual system they need.

Using benchmarks that include several weeks of feedback from session attendees, and random session spot-checks you conduct personally, you conclude the solution has worked.

Scenario #3: Marketing

You’ve invested heavily in product marketing, but still can’t meet your sales goals. Specifically, you missed your revenue target by 30% last year and would like to meet that same target this year.

After collecting and examining reams of information from your sales and accounting departments, you sit down with your marketing team to figure out what’s hindering your success in the marketplace.

Determining that your product isn’t competitively priced, you map out two viable solutions.

• Hire a third-party specialist to conduct a detailed market analysis.
• Drop the price of your product to undercut competitors.

Since you’re in a hurry for results, you decide to immediately reduce the price of your product and market it accordingly.

When revenue figures for the following quarter show sales have declined even further – and marketing surveys show potential customers are doubting the quality of your product – you revert back to your original pricing, revisit your problem solving process, and implement the market analysis solution instead.

With the valuable information you gain, you finally arrive at just the right product price for your target market and sales begin to pick up. Although you miss your revenue target again this year, you meet it by the second quarter of the following year.

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The Comprehensive List of Important Digital Skills for Students

by Lcom Team | Nov 29, 2022 | Blogs

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The digital skills gap impacts Americans of all ages. If lacking basic digital skills, students will face challenges not only in school, but in the professional world as well. Students require digital skills to perform research and produce projects in school, as they navigate a digital world for community building and social interactions and as they enter the workforce.

Digital readiness is the broad phrase used to encompass the digital skills needed to use technology in useful, meaningful and innovative ways. Though “digital readiness” is broad and ambiguous, there are concrete digital skills that are foundational to this concept. In the remainder of this article, we will unpack and explore these.

Which Digital Skills Do Students Need?

The digital skills students need tend to fall under five basic categories. These include operation and application, or the ability to select and use appropriate tools for certain tasks and goals; inquiry and innovation, which includes the ability to collect, organize and visualize information using technology programs; problem solving & critical thinking, or the ability to use technology to uncover and apply solutions; online communication and research; and digital citizenship, or the ability to use technology in a safe and ethical way.

In the list below, we detail the digital skills students need in these five primary categories.

1. Keyboarding Effectively

Students should have proficiency in keyboarding to optimize expediency and fluency when using technology.

• Apply optimal posture and ergonomic strategies such as correct hand and body positions and smooth and rhythmic keystrokes.
• Demonstrate proper touch keyboarding techniques and correct hand placement.
• Gain proficiency, accuracy and speed in touch keyboarding.
• Identify, explore and understand the layout of common QWERTY keyboards.
• Use common hotkeys and shortcut key combinations.

2. Understanding Computer Fundamentals

Computer fundamentals include mastering basic computing functions and hardware. This important digital skill helps students identify and use basic technology resources to accomplish a variety of tasks.

• Turning on a computer and logging in.
• Using a mouse.
• Recognizing basic computer icons.
• Saving documents and changing file sizes.
• Understanding computer and network storage.
• Creating, organizing and manipulating shortcuts.
• Identifying and employing basic features of an operating system.
• Creating and maintaining files and folders.
• Practice responsible usage and care when using electronic devices and troubleshoot problems with hardware and software using available resources.
• Model the basic infrastructure of networks and how networks allow for online research, communication and collaboration.
• Make informed choices among technology systems, resources and services in a variety of contexts.
• Demonstrate an understanding of how changes in technology impact the workplace and society.
• Identify and assess the capabilities and limitations of emerging technologies.
• Utilize technology to facilitate personalized and interactive learning.

3. Utilizing Technology & Productivity Tools

Using technology, students should have the digital skills to enhance learning, increase productivity and promote creativity.

• Assess the appropriateness of software applications to accomplish a defined task.
• Collaborate in constructing technology-enhanced models, preparing publications and producing other creative works.
• Use a variety of electronic formats (e.g., web publishing, oral presentations, journals and multimedia presentations) to summarize and communicate results.

4. Understanding Spreadsheets & Databases

Students should have the digital skills to understand that spreadsheets, databases and other similar digital tools are used to collect, organize, process, analyze and visualize real-world data. They should have a basic understanding of using, creating and editing these documents.

5. Using Word Processing

Students should be able to use word processing software to translate information into organized, effective documents that serve specific purposes.

• Create, edit and publish documents that demonstrate effective formatting (e.g. font, color, orientation, alignment, margins, spacing) for specific audiences.
• Create documents for specific purposes including content for a web page, resumes, business letters and multi-page papers with citations for school assignments.
• Leverage intermediate features in word processing applications such as tabs, indents, headers and footers, end notes, bulleting and numbering and tables.
• Use a word processor as a tool to enhance learning, increase productivity and promote communication and collaboration.
• Strategize visual design to emphasize key information and improve readability with formatting features such as columns, tables and styles, as well as the use of images and other graphic elements.
• Proofread and edit writing using available resources including spell check, grammar and autocorrect, while also understanding the limitations of these tools.
• Collaborate with peers and leverage functionality like comments and track changes.
• Leverage a word processor as a part of the problem-solving process to construct technology-enhanced models, prepare publications and produce other creative works.

6. Creating Presentations

Students create linear and non-linear presentations tailored to specific audiences that present research, tell a story, or exchange ideas using slideshow software and applications.

• Evaluate for organization, content, formatting and appropriateness of citations to maximize accuracy and design.
• Implement a process to practice, polish and add notes to strengthen the delivery and dissemination of information.
• Apply basic design elements including font, color, alignment, white space and layouts, as well as develop templatized layouts, to improve slideshow content.
• Make strategic use of visual and audio elements, such as graphics, audio effects, transitions, animations and video components, to add interest and express meaning.
• Design presentations with specific audiences in mind.
• Use teacher developed guidelines to evaluate multimedia presentations for organization, content, design, presentation and appropriateness of citations.

7. Create Effective Multimedia

Another important skill is understanding, utilizing and creating multimedia. Students should be able to communicate ideas visually and graphically using appropriate digital tools and applications.

• Create and edit files in various formats, including audio, video, moving images, text and graphics.
• Demonstrate how the use of various techniques and effects (e.g., editing, music, color, rhetorical devices) can be used to convey meaning in media.
• Demonstrate an understanding of basic design principles and strategies to increase the effectiveness of a digital product as viewed by different audiences and in different contexts (print, web, screen and monitor).
• Create original works, responsibly repurpose and remix digital resources and incorporate various files into new creative multimedia works.

8. Create & Use Visual Mapping

Students should be able to use digital skills to plan and create visual digital products that express thoughts, illustrate complex processes and share stories in a sequential manner.

• Gather and organize information.
• Visually present information for specific audiences.
• Create, edit and publish thoughts and ideas visually.
• Brainstorm; capture ideas, understanding and information; and explore complex concepts visually.

9. Create & Use Spreadsheets Effectively

Another important digital skill students should have is the ability to create, read, utilize and edit spreadsheets effectively, as well as understanding the available functionalities of a spreadsheet.

• Identify and explain terms and concepts related to spreadsheets (e.g., cell, column, row, values, labels, chart graph).
• Text formatting features (e.g., merge cells, wrap text, font, color, alignment).
• Advanced formatting (e.g., reposition columns and rows add and name worksheets).
• Data entry (e.g., auto fill, import and export functionality).
• Various number formats (e.g., scientific notations, percentages, exponents).
• Mathematical symbols (e.g., + add, ‐ minus, *multiply, /divide, ^ exponents).
• Functions of a spreadsheet application (e.g., sort, filter, find).
• Computing methods and formulas (e.g., sums and averages).
• Create data visualizations for specific audiences and purposes by assessing the most appropriate type of chart to represent given data.
• Collect real-world data and analyze the results to draw conclusions, recognize patterns and relationships in the data and make predictions.

10. Use & Produce Databases

• Identify and navigate common examples of databases from everyday life (e.g., library catalogs, school records, contact directories and search directories).
• Use effective search strategies for locating and retrieving electronic information in common databases (e.g., using Boolean logic and filters).
• Plan, create, modify and edit fields and records in a database.
• Use the sort, filter and query tools to produce reports to share and analyze information.  

11. Performing Computational Thinking

Students should have the digital skills to wield technology resources for problem solving, critical thinking and informed decision making.

• Demonstrate a disposition amenable to open-ended problem solving (e.g., perseverance, creativity, patience and adaptability).
• Understand that a problem can have many solutions and that solutions can be adapted or modified to solve similar problems using modeling, simulation, creating prototypes and by refining solutions after testing.
• Determine what is known and what needs to be known regarding a problem and develop a problem statement in order to solve a problem or complete a task.
• Identify complex, interdisciplinary and real-world problems that can be solved computationally.
• Demonstrate that solutions to complex problems require collaboration, interdisciplinary understanding and systems thinking.
• Decompose complex real-world problems into manageable subproblems that could integrate existing solutions or procedures.
• Create and interpret visual representations such as flowcharts and diagrams to organize data, find patterns, make predictions or test solutions.
• Collect data or identify relevant data sets, use digital tools to analyze them, and represent data in various ways to facilitate problem-solving and decision-making.

12. Understand & Use Algorithmic Thinking

• Use algorithmic thinking to identify algorithms in everyday life.
• Determine how algorithms can be used to accomplish tasks and solve problems.
• Understand how automation works and use algorithmic thinking to develop a sequence of steps to create and test automated solutions.

13. Understand Basic Coding Processes

Students plan the development of a computational artifact using basic coding skills that include reflection on and modification of the process as well as understanding key features, time and resource constraints, user needs and expectations.

• Define an algorithm as a sequence of defined steps or instructions to be followed and identify how algorithms relate to computer programming and allow for automation.
• Develop and execute an algorithm that includes sequencing, loops and conditionals to accomplish a task with or without a computing device.
• Systematically test algorithms to identify and correct errors, including those involving operators, conditionals, parallelism and repetition.
• Construct basic programs that include sequencing, events, loops, conditionals, functions and variables
• Evaluate existing technological functionalities and incorporate them into new designs.
• Abstract common features from a set of interrelated processes or complex phenomena and create modules and develop points of interaction that can apply to multiple situations and reduce complexity.
• Evaluate and refine a computational artifact multiple times to enhance its performance, reliability, usability and accessibility.
• Describe, justify and document computational processes and solutions using appropriate terminology consistent with the intended audience and purpose.
• Solicit and incorporate feedback from and provide constructive feedback to team members and other stakeholders.
• Include the unique perspectives of others and reflect on one’s own perspectives when designing and developing computational products.

14. Using the Internet Effectively, Safely, & Responsibility for Online Communication, Collaboration & Research

• Facilitate communication, research and collaboration with digital tools.
• Describe and practice “etiquette” when communicating and sharing information online.
• Compose, send and organize e-mail messages with and without attachments.
• Explain the differences among various search engines and how they rank results.
• Use search strategies to acquire and organize media and digital content through information sourcing.
• Evaluate resources for validity, accuracy, relevance and credibility.
• Analyze and explain how media and technology can be used to distort, exaggerate and misrepresent information.
• Recognize the ethical and legal implications of plagiarism of copyrighted materials.
• Use web 2.0 tools (e.g., online discussions, blogs and wikis) to gather information and publish digital media.
• Create, share and utilize collaborative workspaces, documents or other digital tools for asynchronous and synchronous collaboration with remote learners.
• Reflect on their responsibilities and rights as creators in the online spaces where they consume, create and share information.

15. Understand & Maximize Online Safety

• Understand how to be safe and make responsible and ethical decisions online and in a digital world.
• Recognize and protect against the potential risks and dangers associated with online communication and participation in online communities (e.g., discussion groups, blogs and social networking sites).
• Understand the importance of communicating and reporting inappropriate content and illegal activities in a digital society.
• Identify and understand the positive and negative effects of digital technologies and devices and how technology can impact all aspects of life and society.
• Recognize online threats to privacy and practice effective strategies to secure and protect personal data from data-collection technologies and malicious software.
• Manage online information and use strategies, like creating strong passwords, to keep it secure from online risks.
• Practice self-reflection and consider how sharing online can impact themselves and others.
• Understand the role an online identity plays and the permanence of choices and decisions when interacting online and cultivate a positive digital identity.
• Identify cyberbullying and describe strategies to deal with such a situation.

Final Thoughts

The list of skills students should have is extensive–and regularly grows based on new technologies and digital advances. Teachers and administrators can help teach these essential digital skills through programs such as Learning.com’s EasyTech program. Learn more about EasyTech and its comprehensive digital literacy program by clicking the button below.

Learning.com Team

Staff Writers

Founded in 1999, Learning.com provides educators with solutions to prepare their students with critical digital skills. Our web-based curriculum for grades K-12 engages students as they learn keyboarding, online safety, applied productivity tools, computational thinking, coding and more.

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What is Problem Solving?

Quality Glossary Definition: Problem solving

Problem solving is the act of defining a problem; determining the cause of the problem; identifying, prioritizing, and selecting alternatives for a solution; and implementing a solution.

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Problem Solving Chart

The Problem-Solving Process

In order to effectively manage and run a successful organization, leadership must guide their employees and develop problem-solving techniques. Finding a suitable solution for issues can be accomplished by following the basic four-step problem-solving process and methodology outlined below.

1. Define the problem

Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes .

The sections below help explain key problem-solving steps. These steps support the involvement of interested parties, the use of factual information, comparison of expectations to reality, and a focus on root causes of a problem. You should begin by:

• Reviewing and documenting how processes currently work (i.e., who does what, with what information, using what tools, communicating with what organizations and individuals, in what time frame, using what format).
• Evaluating the possible impact of new tools and revised policies in the development of your "what should be" model.

2. Generate alternative solutions

Postpone the selection of one solution until several problem-solving alternatives have been proposed. Considering multiple alternatives can significantly enhance the value of your ideal solution. Once you have decided on the "what should be" model, this target standard becomes the basis for developing a road map for investigating alternatives. Brainstorming and team problem-solving techniques are both useful tools in this stage of problem solving.

Many alternative solutions to the problem should be generated before final evaluation. A common mistake in problem solving is that alternatives are evaluated as they are proposed, so the first acceptable solution is chosen, even if it’s not the best fit. If we focus on trying to get the results we want, we miss the potential for learning something new that will allow for real improvement in the problem-solving process.

3. Evaluate and select an alternative

Skilled problem solvers use a series of considerations when selecting the best alternative. They consider the extent to which:

• A particular alternative will solve the problem without causing other unanticipated problems.
• All the individuals involved will accept the alternative.
• Implementation of the alternative is likely.
• The alternative fits within the organizational constraints.

4. Implement and follow up on the solution

Leaders may be called upon to direct others to implement the solution, "sell" the solution, or facilitate the implementation with the help of others. Involving others in the implementation is an effective way to gain buy-in and support and minimize resistance to subsequent changes.

Regardless of how the solution is rolled out, feedback channels should be built into the implementation. This allows for continuous monitoring and testing of actual events against expectations. Problem solving, and the techniques used to gain clarity, are most effective if the solution remains in place and is updated to respond to future changes.

You can also search articles , case studies , and publications  for problem solving resources.

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One Good Idea: Some Sage Advice ( Quality Progress ) The person with the problem just wants it to go away quickly, and the problem-solvers also want to resolve it in as little time as possible because they have other responsibilities. Whatever the urgency, effective problem-solvers have the self-discipline to develop a complete description of the problem.

Diagnostic Quality Problem Solving: A Conceptual Framework And Six Strategies  ( Quality Management Journal ) This paper contributes a conceptual framework for the generic process of diagnosis in quality problem solving by identifying its activities and how they are related.

Weathering The Storm ( Quality Progress ) Even in the most contentious circumstances, this approach describes how to sustain customer-supplier relationships during high-stakes problem solving situations to actually enhance customer-supplier relationships.

The Right Questions ( Quality Progress ) All problem solving begins with a problem description. Make the most of problem solving by asking effective questions.

Solving the Problem ( Quality Progress ) Brush up on your problem-solving skills and address the primary issues with these seven methods.

Refreshing Louisville Metro’s Problem-Solving System  ( Journal for Quality and Participation ) Organization-wide transformation can be tricky, especially when it comes to sustaining any progress made over time. In Louisville Metro, a government organization based in Kentucky, many strategies were used to enact and sustain meaningful transformation.

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Adapted from The Executive Guide to Improvement and Change , ASQ Quality Press.

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Technological problem solving: an investigation of differences associated with levels of task success

• Open access
• Published: 02 June 2021
• Volume 32 , pages 1725–1753, ( 2022 )

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• David Morrison-Love   ORCID: orcid.org/0000-0002-9009-4738 1  

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Research into technological problem solving has shown it to exist in a range of forms and draw upon different processes and knowledge types. This paper adds to this understanding by identifying procedural and epistemic differences in relation to task performance for pupils solving a well-defined technological problem. The study is theoretically grounded in a transformative epistemology of technology education. 50 pupils in small groups worked through a cantilever problem, the most and least successful solutions to which were identified using a Delphi technique. Time-interval photography, verbal interactions, observations and supplementary data formed a composite representation of activity which was analysed with successively less contrasting groups to isolate sustained differences. Analyses revealed key differences in three areas. First, more successful groups used better and more proactive process-management strategies including use of planning, role and task allocation with lower levels of group tension. Second, they made greater use of reflection in which knowledge associated with the technological solution was explicitly verblised. This was defined as ‘analytical reflection’ and reveals aspects of pupils’ qualitative technical knowledge. Third, higher-performing groups exhibited greater levels of tacit-procedural knowledge within their solutions. There was also evidence that less successful groups were less affected by competition and not as comprehensive in translating prior conceptual learning into their tangible technological solutions. Overall findings suggest that proactive management, and making contextual and technical connections, are important for pupils solving well-defined technological problems. This understanding can be used to support classroom pedagogies that help pupils learn to problem solve more effectively.

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Introduction

Problem solving is an activity, a context and a dominant pedagogical frame for Technology Education. It constitutes a central method and a critical skill through which school pupils learn about and become proficient in technology (Custer et al., 2001 ). Research has, among other things, been able to identify and investigate sets of intellectual and cognitive processes (Buckley et al., 2019 ; Haupt, 2018 ; Johnson, 1997 ; Sung & Kelly, 2019 ) and shown there to be conceptual, procedural, relational and harder-to-get-to forms of ‘technological knowledge’ involved when pupils develop technological solutions (de Vries, 2005 ; McCormick, 1997 , 2004 ; Rauscher, 2011 ). Some authors argue that technological problem solving (and design) is a situated activity (Jackson & Strimel, 2018 ; Murphy & McCormick, 1997 ; Liddament, 1996 ), but with social and context-independent processes also playing an important role (e.g. Jones, 1997 ; Winkelmann & Hacker, 2011 ). Within and across this vista, there has been strong interest in more open-ended, creative and design-based problem-solving (Lewis, 2005 , 2009 ), which Xu et al. ( 2019 ) notes became particularly prominent after 2006. These studies have helped to understand some of the challenges and pedagogies of design (Gómez Puente et al., 2013 ; Lavonen et al., 2002 ; Mioduser & Dagan, 2007 ; Mawson, 2003 ) including those that mitigate effects such as cognitive fixation (e.g. McLellan & Nicholl, 2011 ). Problem solving, it seems, is a pervasive idea in technology education research and policy. Middleton ( 2009 ) notes that problem solving is found in almost all international technology education curricula.

The pace, nature and complexity of contemporary societal challenges make it more critical than ever that technology classrooms prepare people who can think through and respond to technological problems effectively. It requires that we strengthen our understanding in ways that will ultimately be powerful for shaping classroom learning. One way of contributing to this is to learn more about the differences between learners who are more and less successful at technological problem solving. Studies that share a comparative perspective and/or a focus upon task success are relatively few. Doornekamp ( 2001 ) compared pupils (circa 13 years old) who solved technological problems using weakly structured instructional materials with those using strongly structured materials. It was shown that the latter led to statistically significant improvements in the quality of the technical solutions. More recently, Bartholomew & Strimel ( 2018 ) were able to show that, for open-ended problem solving, there was no significant relationship between prior experience and folio creation, but that more in-school school experience of open-ended problem solving corresponded to higher ranked solutions.

This paper contributes to this work by reporting on a study that compares groups of pupils during technological problem solving in order to identify areas of difference and the factors associated with more successful outcomes. Specifically, it addresses the question: ‘In terms of intellectual processes and knowledge, what are the differences in the modi operandi between groups of pupils that produced more and less successful technological solutions to a well-defined problem?’ Theoretically grounded in a transformative epistemology of technology education (Morrison-Love, 2017 ), the study identifies prominent procedural and epistemic differences in pupils’ thinking and technical solutions. Groups of pupils engaged with a structures problem requiring them to develop a cantilever bridge system which would facilitate safe travel across a body of water.

The paper begins by setting out the theoretical basis and conceptual framework for investigation before describing the comparative methodological and analytical approaches that were adopted. Following an analysis and presentation of key findings, conclusion and implications are discussed.

A theoretical basis for the study of technological problem solving

Despite there being no single comprehensive paradigm for technological problem solving, a theoretical grounding and conceptual framework necessary for this study are presented. At the theoretical level, this study is based upon a ‘transformative epistemology’ for technology education (Morrison-Love, 2017 ). From this, a ternary conceptual framework based upon mode, epistemology and process is developed to support study design and initiate data analysis.

A transformative epistemology for technology education (Morrison-Love, ibid) proposes that pupils’ technological knowledge and capability arises from the ontological transformation of their technical solution from ‘perdurant’ (more conceptual, mutable, less well-defined, partial) in the early stages, to ‘endurant’ (comprehensive, tangible, stable over time) upon completion. It proposes that technical outcomes exist in material and tangible forms and that to be technological (rather than, for example, social, cultural or aesthetic) these must somehow enhance human capabilities in their intended systems of use. For this study, the ideas of transformative epistemology support problem solving in which pupils build technological knowledge by iteratively moving from concept to tangible, material solution. Moreover, it means pupils are successful in this when their solutions or prototypes: (1) enhance existing human capabilities in some way, and (2) are sufficiently developed to be stable over time, beyond the problem-solving activity that created it.

A conceptual framework for technological problem solving

A ternary conceptual framework (Fig. 1 ) of mode, process and epistemology was developed from the literature in which the knowledge and cognitive/intellectual processes used by pupils are enacted in the ‘process application block’. This is like the ‘problem space’ described in a model proposed by Mioduser ( 1998 ). Collectively, the goal of creating a physical artefact, the solution itself, the epistemic and procedural dimensions reflect the four dimensions of technology identified by Custer ( 1995 ).

‘A conceptual framework for technological problem solving’

Mode and forms dimension

Although problem solving may be ‘technological’, several classifications of both problem type and problem solving are found in the literature. Ill-defined and well-defined problems build upon the earlier work of information processing and cognitive psychology (see Jonassen, 1997 ). Typically, these two forms reflect different extents to which the outcome is specified to the solver at the outset. Ill-defined problems are strongly associated with design and creativity, and Twyford and Järvinen ( 2000 ) suggest that these more open briefs promote greater social interaction and use by pupils of prior knowledge and experience. Additionally, two forms of troubleshooting were identified in the literature: emergent troubleshooting and discrete troubleshooting. MacPherson ( 1998 ) argues that ‘troubleshooting’ constitutes a particular subset of technological problem solving—something earlier recognised by McCade ( 1990 ), who views it as the identification and overcoming of problems encountered during the production or use of a technical solution. In this study, emergent troubleshooting occurs in the process of developing solutions in response to emergent problems (McCormick, 1994 ). Discrete troubleshooting is a process in which significant technical understanding is applied in a structured way (Schaafstal et al., 2000 ) to resolve something about an existing artefact.

Intellectual and cognitive process dimension

Studies often conceptualise cognitive processes discretely rather than hierarchically, and different studies employ different process sets. Williams ( 2000 ), identifies evaluation, communication, modelling, generating ideas, research and investigation, producing and documenting as important to technological problem solving, while DeLuca ( 1991 ) identifies troubleshooting, the scientific process, the design process, research and development, and project management. There are also studies that employ specific, or more established, coding schemes for sets of intellectual and cognitive processes. A detailed analysis of these is given Grubbs et al. ( 2018 ), although the extent to which a particular process remains discrete or could form a sub-process of another remains problematic. In DeLuca’s ( 1991 ) break down for example, to what extent are research and investigation part of design and does this depend on the scale at which we conceptualise different processes?

Regardless of the processes a study defines, it is typically understood that pupils apply them in iterative or cyclic fashion. This is reflected across several models from Argyle’s ( 1972 ) ‘Motor Skill Process Model’ (perception-translation-motor response) through to those of Miodusre and Kipperman ( 2002 ) and Scrivener et al. ( 2002 ) (evaluation-modification cycles) which pertain specifically to technology education. All these models bridge pupils’ conceptual-internal representations with their practical-external representations as they move towards an ontologically endurant solution and this is captured by the ‘Re-Application/Transformation Loop’ of the conceptual framework. Given that little is known about where differences might lie, the process set identified by Halfin ( 1973 ) was adopted due to its rigour and the breadth of thinking it encompasses. This set was validated for technology classrooms by Hill and Wicklein ( 1999 ) and used successfully by other studies of pupils technological thinking including Hill ( 1997 ), Kelley ( 2008 ) and Strimel ( 2014 ).

Epistemology dimension

The nature and sources of knowledge play a critical role for pupils when solving technological problems, but these remain far from straightforward to conceptualise. McCormick ( 1997 ) observes that the activity of technology education, and its body of content, can be thought of as ‘procedural knowledge’ and ‘conceptual knowledge’ respectively. Vincenti ( 1990 ), in the context of Engineering, makes the case for descriptive knowledge (things as they currently are) and prescriptive knowledge (of that with is required to meet a desired state) but also recognises knowledge can take on implicit, or tacit forms relating to an individual’s skill, judgement, and practice (Polanyi, 1967 ; Schön, 1992 ; Sternberg, 1999 ; Welch, 1998 ). Arguably, moving from concept to physical solution will demand from pupils a certain level of practical skill and judgement, and Morgan ( 2008 ) observes that procedural knowledge which is explicit in the early stages becomes increasingly implicit with mastery. Notably, in addition to conceptual, procedural and tacit forms of knowledge, there is also evidence that knowledge of principles plays a role. Distinct from impoverished notions of technology as ‘applied science’, Rophol ( 1997 ) shows that it is often technological principles, laws and maxims that are applied during problem solving rather than scientific ones. Frey ( 1989 ) makes similar observations and sees this form of knowledge arising largely from practice. In this study, knowledge of principles involves knowledge of a relationship between things. It is not constrained to those that are represented scientifically.

The conceptual framework finally accounts for pupils’ sources of knowledge during problem solving, building principally on a design knowledge framework of media, community and domain presented by Erkip et al. ( 1997 ). In this study, media includes task information, representations and materials; community includes teachers and peers, and domain relates to prior technological knowledge from within technology lessons and prior personal knowledge from out with technology lessons. Finally, the developing solution is itself recognised a source of knowledge that pupils iteratively engage with and reflect upon, even when it appears that limited progress in being made (Hamel & Elshout, 2000 ).

Methodology

The research question in this study is concerned with differences in the knowledge and intellectual processes used by pupils in moving from a perdurant to an endurant technical solution. From an exploratory stance, this elicits a dualistic activity system involving pupils’ subjective constructions of reality as well as the resultant tangible and more objective material solution. The study does not aim to investigate pupils’ own subjective constructions from an emic perspective, but rather seeks to determine the nature and occurrences of any differences during observable real-time problem-solving activity. As such, content rather than thematic analysis was used (Elo & Kyngäs, 2008 ; Vaismoradi et al., 2013 ) with concurrent data collection to build a composite representation of reality (Gall et al., 2003 , p.14). Complementary data provided insights into study effects, the classrooms and contexts within which problem-solving took place.

This study assumes that should differences exist, these will be discernible in the inferred cognitive processes, external material transformations, interactions and verbalisation (even though this tends to diminish as activity becomes more practical). Absolute and objective observation is not possible. This study also accepts that data gathering and analysis are influenced by theory, researcher fallibility and bias which will be explicitly accounted for as far as possible. Finally, while the conceptual framework provides an analytical starting point, it should not preclude the capture of differences that may lie elsewhere in the data including, for example, process that lie out with those identified by Halfin ( 1973 ).

Participants, selection and grouping

To support transferability, a representative spread of pupils from low, medium and high socio-economic backgrounds took part in this study. Purposeful, four-stage criterion sampling was used (Gall et al., 2003 , p.179). Stage one identified six schools at each socio-economic level from all Scottish secondary schools that presented pupils for one or more technology subjects with the Scottish Qualifications Authority. This was done using socio-economic data from the Scottish Area Deprivation Index, the Carstairs Index and pupil eligibility for subsidised meals. Secondary school catchment areas were used to account for pupil demographics as accurately as possible. All eighteen schools were subsequently ranked with one median drawn from low, medium and high bands of socio-economic deprivation (School 1: Low, School 2: Medium, School 3: High).

One class in each school was then selected from the second year of study prior to pupils making specific subject choices to minimise variations in curricular experience. In total, 3 class teachers and 50 pupils (20 female, 30 male) aged between 12 and 13 years old took part in the study. The group rather than the individual was defined as unit of study to centralise verbal interaction.

None of the pupils participating in this study had experience of group approaches such as co-operative learning and it was likely that groups might experience participation effects including inter-group conflict and interaction effects (Harkins, 1987 ; Sherif et al., 1961 ), social loafing (Salomon & Globerson, 1989 ), free-rider (Strong & Anderson, 1990 ) and status differential effects (Rowell, 2002 ). Relevant also to this study is evidence suggesting that gender effects can take place in untrained groups undertaking practical/material manipulation activities. To maximise interaction between group members and the material solution, thirteen single sex groups averaging four pupils were formed in order to: (1) minimise the marginalisation of girls with boys’ tendency to monopolise materials and apparatus in groups (Conwell et al., 1993 ; Whyte, 1984 ); (2) recognise boys’ tendency to respond more readily to other boys (Webb, 1984 ) and, (3) maximise girls’ opportunities to interact which is seen to erode in mixed groups (Parker & Rennie, 2002 ; Rennie & Parker, 1987 ). Hong et al. ( 2012 ) examines such gender differences in detail specifically within the context of technological problem solving. Teacher participation in group allocation minimised inter-group conflict and interaction effects although groups still experienced naturally fluctuating attrition from pupil absences (School 1 = 17.6%; School 2 = 2.5% and School 3 = 8.8%).

Identification of most and least successful solutions

The research question requires differences to be identified in terms of levels of success. The overall trustworthiness of any differences therefore depends upon the credible identification of the most and least successful solutions from the thirteen produced. Wholly objective assessment of the pupils’ solutions is not possible, and material imperfections in different solutions negated reliable physical testing across the three classes. Moreover, because the researcher earlier observed pupils while problem solving, neutrality of researcher judgement in establishing a rank order of group solutions was equally problematic. Everton and Green ( 1986 ) identify this biasing risk between and early and later stages of research as a form of contamination.

To address these limitations, a Delphi technique was design using the work of Gordon ( 1994 ), Rowe and Wright ( 1999 ) and Yousuf ( 2007 ). This was undertaken anonymously prior to any analysis and, in conjunction with the results of physical testing, enabled the four most successful and four least successful solutions to be confidently identified independently of the researcher. A panel of eight secondary school teachers was convened from schools out with the study. All panel members had expertise in teaching structures with no dependent relationships or conflicts of interest. Following Delphi training, and a threshold level of 75%, the four most and four least successful solutions on outcome alone were identified after two rounds. Qualitative content validity checks confirmed that panel judgements fell within the scope of the accessible information. 37/43 reasons given were ‘High’, with six considered ‘Medium’ because the reasoning was partially speculative. When triangulated with additional evidence from physical testing, two cohorts of four groups were identified and paired to form four dyads (Table 1 ).

Study design

As noted, ‘Structures’ was chosen as a topic area and was new to all participants. It was appropriate for setting well-defined problems and allowed pupils to draw upon a sufficiently wide range of processes and knowledge types in developing a tangible, endurant solution. In discussion with the researcher, teachers did not alter their teaching style and adopted pedagogy and formative interactions that would support independent thinking, reasoning and problem solving. This study involved a learning phase, followed by a problem-solving phase.

In the learning phase, groups engaged over three fifty-minute periods with a unit of work on structures which was developed collaboratively with, and delivered by, the three classroom teachers. This allowed pupils to interact with materials and develop a qualitative understanding of key structural concepts including strength, tension and compression, triangulation, and turning moments. During this time, pupils also acclimatised to the presence of the researcher and recording equipment which helped to reduce any potential Hawthorne effect (Gall et al., 2003 ). Structured observations, teacher de-briefs and questionnaires were used in this phase to capture study effects, unit content coverage and environmental consistency between the three classrooms. Content coverage and environmental consistency were shown to be extremely high. Scores from the unit activity sheets that pupils completed were used to gauge group understanding of key concepts.

The problem-solving phase took place over two circa 50-minute periods (range: 40–52 m) in which pupils responded to a well-defined problem brief. This required them to develop a cantilever bridge enabling travel across a body of water. This bridge would enhance people’s ability to traverse terrain (conditions for being ‘technological’) with maximal span rigidity and minimal deflection (conditions for an ontologically ‘endurant’ solution). All groups had access to the same range and number of materials and resources and were issued with a base board showing water and land on which to develop their solutions.

While video capture was explored in depth (Lomax & Casey, 1998 ), challenges in reliably capturing solution detail resulted in group verbalisation being recorded as audio. This was synchronised with time interval photography and supplemented with structured observer-participant observation that focused on a sub-set of observable processes from the conceptual framework (Halfin, 1973 ). The developing technical solutions were viewed as manifestations of the knowledge and intellectual processes used by pupils at different points in time through their cognitive and material interactions. Photographs captured the results of these interactions in group solutions every 3–4 min but did not capture interactions between pupils. The structured observational approach adopted continuous coding similar to that found in the Flanders System of Interaction analysis (Amatari, 2015 ) and was refined through two pilot studies. During each problem-solving session, groups were observed at least twice between photographs and, following each session, pupil questionnaires, teacher de-briefs and solution videos (360° panoramic pivot about the solution) were completed to support future analysis. Reflexive accounts by the researcher also captured critical events, observer and study effects.

Analytical approach

All data were prepared, time-synchronised and analysed in three stages. Core verbal data (apx. 12h) and photographic data (n = 206) were triangulated with observational and other data against time. The problem-solving phase for each class was broken into a series of 3–4 min samples labelled S = 1, S = 2, S = 3…with durations in each recorded in minutes and seconds. Verbal data were analysed using NVivo software using digital waveforms rather than transcribed files to preserve immediacy, accuracy and minimise levels of interpretation (Wainwright & Russell, 2010 ; Zamawe, 2015 ). Photographic data were coded for the physical developments of the solutions (e.g. adding/removing materials in particular places) allowing solution development to be mapped for different groups over time. Triangulation of data also allowed coding to capture whether individual developments enhanced or detracted from the overall function efficacy of the solution.

The first stage of analysis was immersive, beginning with an initial codebook derived from the conceptual framework. In response to the data this iteratively shifted to a more inductive mode. To sensitise the analysis to differences, the most successful and the least successful groups were compared first as is discussed by Strauss 1987 (Miles & Huberman, 1994 , p.58). Three frameworks of differences emerged from this: (1) epistemic differences, (2) process differences, and (3) social and extrinsic differences. These were then applied to dyads of decreasing contrast and successively  refined in response to how these differences were reflected in the wider data set. Seven complete passes allowed non-profitable codes to be omitted and frameworks to be finalised. A final stage summarised differences across all groups.

Analysis and findings

The analysis and findings are presented in two main parts: (1) findings from the learning phase, and (2) findings from the problem-solving phase. Verbal data forms a core data source throughout and coding includes both counts and durations (in minutes and seconds). Direct quotations are used from verbal data, although the pupils involved in the study were from regions of Scotland with differing and often very strong local dialects. Quotations are therefore presented with dialect effects removed:

Example data excerpt reflecting dialect: “See instead-e all-e-us watchin’, we could all be doin’ su-hum instead-o watchin’ Leanne..” Example data excerpt with dialect removed: “See instead of all of us watching, we could all be doing something instead of watching Leanne..”

Part 1: Findings from the Learning Phase

Both teacher and researcher observation confirmed that pupils in all three classes engaged well with the unit of work (50 pupils across 13 groups) with all 40 content indicators covered by each class. Teachers of classes 1 and 3 commented that the lesson pace was slightly faster than pupils were used to. As expected, different teaching styles and examples were between classes, but all pupils completed the same unit activity sheets. The teacher of class 2, for example, used man-made structures and insect wings to explore triangulation; and the teacher in class 3 talked about the improved stability pupils get by standing with their feet apart. The understanding reflected in activity sheets was very good overall and Table 2 illustrates the percentage of correct responses for each class in relation to each of the three core concept areas.

Though unit activity sheets are not standardised tests, the conditions of administration, scoring, standards for interpretation, fairness and concept validity discussed by Gall et al. ( 2003 , p.xx) were maintained as far as possible. Evidence did not show that representational/stylistic variations by teachers had any discernible effect on pupil understanding and was seen to maintain normality from the pupils’ perspective. Class 3 scored consistently highly across all conceptual areas, although the qualitative understanding of turning moments was least secure for all three classes. Non-completion of selected questions in the task sheets partially explains lower numerical attainment for this concept in class 1 and 2, however, it is unknown if omissions resulted from a lack of understanding. The figures in Table 2 are absence corrected to account for fluctuating pupil attendance at sessions: (17.6% pupil absence across sessions for class 1, compared with 8.8% and 2.5% for classes 3 and 3 respectively). Table 3 illustrates the percentage scores for activity sheets completed by groups in the more and less successful cohorts.

Observational and reflexive data highlighted evidence of some researcher and recorder effects. These were typically associated with pupils’ interest in understanding the roles of the researcher and class teacher, and discussion around what they could say while being recorded. These subsided over time for all but two groups in Class 1, but with no substantive effect on pupils’ technological thinking.

In summary, findings from the learning phase show that: (1) Pupils engagement was high, and all classes covered the core structural concepts in the unit; (2) pupil knowledge and understanding, as measured by activity sheet responses, was very good overall but scores for turning moments were comparatively lower, and (3) study effect subsided quite quickly for all but two groups and there was no evidence showing these to be detrimental to technological thinking. These differences are considered epistemic and are captured in the framework of difference in Fig. 5 .

Part 2: findings from the problem-solving phase

Part 2 begins by describing the differences from comparing the material solutions produced by the most and least successful groups (dyad 1). Subsequent sections report upon the three areas in which difference were found: epistemic differences, process differences and social and extrinsic differences. Each of these sections lead with the analysis from the most contrasting groups (dyad 1) before presenting the resultant framework of difference. They conclude by reporting on how the differences in these frameworks are reflected across the wider data set. As with findings across all sections, findings only account for areas of the conceptual framework in which differences were identified. For processes such as measuring and testing, no difference was found and other processes, such as computing, did not feature for any of the groups.

Differences in the solutions produced by the most & least successful groups (dyad 1)

Group 5′s solution was identified as the most successful and Group7′s solution was identified as the least successful. Overall, both of these groups engaged well with the task and produced cantilevers that are shown in Figs. 2 and 3 . The order in which different parts of the solutions were developed is indicated by colour with the lighter parts appearing earlier in problem solving than the darker parts. Figure 4 shows this cumulative physical development of each solution over time. Both groups shared a similar conceptual basis and employed triangulation above and below the road surface. Figure 4 shows that Group 5′s solution evolved through 36 developments, while Group 7 undertook 23 developments and chose to strip down and restart their solution at the beginning of the second problem solving session. Similarly, groups 6, 11 and 13 removed or rebuilt significant sections of their solution. Neither group 5 or 7 undertook any developments under the rear of the road surface, and the greatest percentage of developments applied to the road surface itself (Group 7: 30.6%; Group 5: 47.8%). For Group 5, it was only developments 5 and 6 (Fig. 2 ) which offered little to no functional structural advantage. All other developments contributed to either triangulation, rigidity or strength through configuration and material choice with no evidence of misconception, which was also noted by the Delphi panel. The orientation, configuration and choice of materials by Group 7 share similarities with Group 5 insofar as each reflected knowledge of a cognate concept or principle (e.g. triangulation). Delphi Panel Member 8 described Group 7′s solution as having a good conceptual basis. Key differences, however, lay in the overall structural integrity of the solution and the underdevelopment of the road surface (Fig. 3 , Dev.1 and Dev.5) which mean that Group 5 achieved a more ontologically endurant solution than Group 7 did. Evidence from Group 7′s discussion (S = 3, 3.34–3.37; S = 3, 3.38–3.39; S = 16, 3.26–3.30) suggests this is partly because of misconception and deficits in knowledge about materials and the task/cantilever requirements. This was also reflected in the group’s activity responses during structures unit in the learning phase. Alongside the photographic evidence and reflexive notes of the researcher, this suggest that there was  some difficultly in translating concepts and ideas into a practical form. This constitutes a difference in tacit-procedural knowledge between Group 5 and Group 7.

‘Group 5 solution schematic’

‘Group 7 solution schematic’

‘Cumulative development of tangible solutions’

Epistemic differences during problem solving

As well as the knowledge differences in the learning phase and the physical solutions, analysis of the most and least successful groups revealed epistemic differences in problem solving activity related to ‘task knowledge’ and ‘knowledge of concepts and principles’. The extent to which ‘knowledge’ can be reliably coded for in this context is limited because it rapidly becomes inseparable from process. Skills are processes which, in turn, are forms of enacted knowledge. Consequently, although Halfin ( 1973 ) defines idea generation as a knowledge generating process using all the senses, attempts to code for this were unsuccessful because it was not possible to ascertain with any confidence where one idea ended, and another began. Coding was possible, however, for ‘prior personal knowledge’, ‘task knowledge’ and ‘prior technological knowledge’. The analysis of these is present along with the resulting framework of epistemic difference with prior personal knowledge omitted on the basis that no differences between groups was found. The final section looks at how epistemic differences are reflected in the activity of the remaining groups.

Epistemic differences between the most & least successful groups (dyad 1)

Task knowledge is the knowledge pupils have of the problem statement and includes relatively objective parameters, conditions, and constraints. One key difference was the extent to which groups explicitly used this to support decision making. Group 5 spent considerably more time than Group 7 discussing what they knew and understood of the task prior to construction (1m10s vs. 8 s) but during construction, had more instances where their knowledge of the task appeared uncertain or was questioned (n = 6 for Group 5 vs. n = 2 for Group 7). Differences were also found in the prior technological knowledge used by groups. This knowledge includes core structural concepts and principles explored in the learning phase. As with task knowledge, Group 5 verbalised this category of knowledge to a far greater extent than Group 7, both apart from, and as part of, formative discussions with the class teacher (18:59 s vs. 14:43 s). In only one instance was the prior technological knowledge of Group 5 incorrect or uncertain compared with four instances for Group 7. These included misconceptions about triangulation and strength despite performing well with these in the learning phase. Furthermore, some instances of erroneous knowledge impacted directly upon solution development. In response to a discussion about rigidity and the physical performance of the road surface, one pupil stated: “Yes, but it is supposed to be able to bend in the middle..” (Group 7, S = 3, 3.34–3.37) meaning that the group did not sufficient attend to this point of structural weakness which resulted in a less endurant solution. No such occurrences took place with Group 5. More prominent and accurate use of this type of knowledge supports stronger application of learning into the problem-solving context and appeared to accompany greater solution integrity.

From these findings, and those from the learning phase, the framework of difference shown in Fig. 5 was developed:

‘Framework of epistemic differences from comparative analysis of Group 5 and 7’

Epistemic differences across all groups (dyads 1–4)

As with dyad 1, the more successful groups in dyads 3 and 4 scored higher (+ 14% and + 20.7%, respectively) in the learning phase compared with their less successful partner groups. This, however, was not seen with dyad 2. The less successful group achieved a higher average score of 86.3% compared with 71% and, despite greater fluctuations in pupil attendance, scored 100% for turning moments compared with 58% for the more successful group. Although comparatively minimal across all groups, more successful groups in each dyad tended to explicitly verbalise technological and task knowledge more than less successful groups. Furthermore, it was more often correct or certain for more successful groups. This was particularly true for dyad 2, although there was some uncertainty about the strongest shapes for given materials in, for example, Group 12 which was the more successful group of dyad 3. The greatest similarity in verbalised task knowledge was observed with the least contrasting dyad, although evidence from concept sketching (Figs. 6 , 7 ) illustrated a shared misunderstanding between both groups of the cantilever and task requirements.

‘Group 2 concept sketch’

‘Group 8 concept sketch’

The differences in tacit-procedural knowledge between Group 5 and 7 were reflected quite consistently across other dyads, with more successful groups showing greater accuracy, skill and judgement in solution construction. The more successful groups in dyads 2 and 3 undertook three material developments that offered little to no functional advantage, and each of the developments these groups undertook correctly embodied knowledge of cognate structural concepts and principles. Notably, Group 8 of dyad 4 was able to achieved this with no structural redundancy at all. Less successful groups, however, were not as secure in their grasp of the functional dependencies and interrelationships between different parts of their structural systems. The starkest example of this was with Group 4 of dyad 3, who explicitly used triangulation but their failure to physically connect it with other parts of the structure rendered the triangulation redundant. Group 2 of dyad 4 were the only group not to triangulate the underside of the road surface. Less successful groups tended to focus slightly more of their material developments in areas of the bridge other than the road surface, whereas the opposite tended to be true for the other groups. Significantly, while all groups in the study included developments that offered little to no functional advantage, it was only in the case of less successful groups that these impaired the overall functional performance of solutions in some way. Table 4 summarises the sustained epistemic difference across all four dyads.

Process differences

Analysis of the most contrasting dyad yielded process differences in: (1) managing (Halfin, 1973 ), (2) planning, and (3) reflection. Groups managed role and task allocation differently, as well as engaging in different approaches to planning aspects of solution development. Reflection, as a process of drawing meaning or conclusions from past events, is not explicitly identified by Halfin or the conceptual framework. Two new forms of reflection for well-defined technological problem solving (declarative reflection and analytical reflection) were therefor developed to account for the differences found. The analysis of the process differences is presented with the resulting framework for this dyad. The final section presents sustained process differences across all groups.

Difference in managing—role & task allocation & adoption (dyad 1)

The autonomous creation of roles and allocation of tasks featured heavily in the activity of Group 5. These typically clustered around agreed tasks such as sketching (S = 2, 1.46), and points where group members were not directly engaged in construction. In total, Group 5 allocated or adopted roles or task on 31 occasions during problem solving compared with only 7 for Group 7. Both groups did so to assist other members (Group 5, S = 16, 3.33–3.38; Group 7, S = 3, 0.37–0.41), to take advantage of certain skills that group members were perceived to possess (Group 5, S = 2, 1.47- 1.49; Group 7, S = 2, 2.03–2.06) and, for one instance in Group 7, to prevent one group member from executing something incorrectly (S = 16, 2.11–2.13). There was evidence, however, that Group 5 moved beyond these quite pragmatic drivers. Member often had more of a choice and, as shown in Excerpt 5, allocation and adoption is mediated by sense of ownership and fairness.

Excerpt 5: Idea Ownership (Sketching) Pupil ?: “You can’t draw on them..” Pupil 1: “You draw Chloe, I can’t draw..” Pupil 2: “I know I can’t draw on them, that’s why I doing them; no, because you, you had the ideas… because you had…” Pupil ?: “(unclear)” Pupil 3: “Just draw your own ideas, right, you can share with mine right…. Right, you draw the thread one, I’ll do the straw thing…” (Group 5, S  =  2, 1.46–1.59)

The effective use of role and task allocation appeared to play an important role in realising an effective technical solution, however, negative managerial traits were perhaps more significant.

Difference in managing—negative managerial traits (dyad 1)

Evidence of differences between Group 5 and 7 were found in relation to: (1) group involvement, and (2) fragmentation of group vision, which were found to be highly interrelated. Negative group involvement accounted for traits of dominance and dismissiveness. For Group 7, this was more prevalent earlier in the problem-solving activity where one group member tended to dominate the process. This pupil tabled 9 out of 11 proposals prior to working with physical materials and, at times, readily dismissed suggestions by other group members (See Excerpt 1). Moreover, ideas and proposals within the group were sometimes poorly communicated (Excerpt 2), which led to a growing level of disenfranchisement for some group members and a fragmented group vision for solution development.

Excerpt 1 Pupil 1:“We could do it that way…” (Pupils continue discussion without acknowledgement) Pupil 1:“You could do that..” Pupil 2:“Shut up, how are we going to do that?” Pupil 1:“Well you’re allowed glue, and you’re allowed scissors..” Several group members: “Shut-up!” (Group 7, S = 1, 2.07–2.28) Excerpt 2 “(Loud inhalation) Watch my brilliant idea… I need scissors.. Are you allowed scissors?” (Group 7, S = 1, 1.36–1.41)

The was some evidence of dismissiveness present with Group 5 also (e.g. S = 9, 1.32–1.46), however, group members were able to voice their ideas which appeared to support a better shared understanding among group members. Notably, Group 5 reached a degree of consensus about what they would do prior to constructing anything, whilst Group 7 did not. Even in these early stages, two of the four members of Group 7 made it very clear that they did not know what was happening (Excerpt 3).

Excerpt 3 Pupil 1: “What are you all up to?” Pupil 2: “Move you” Pupil 4: “No idea” Pupil 2: “You’re allowed to say hell are you not?” Pupil ?: “Helli-yeh” Pupil 2: “Hellilouya” (slight laughter) Pupil 3: “Right so were going to..(unclear) and do that..” Pupil 1: “What are you all up to?” Pupil 2: “Just… I know what he’s thinking of..” Pupil 4: “I don’t have a clue what you’re thinking of..” Pupil 3: “Neither do I..” (Group 7, S = 2, 0.15–0.33)

Occurrence like these contributed to a growing sense of fragmentation in the group. Verbal and observational data show this to have been picked up by the class teacher who tried to encourage and support the group to share and discussed ideas more fully. Despite this, the group lost their sense of shared vision about how to approach a solution and, part way through the first session, two group members attempted to begin developing a separate solution of their own (S-3, 2.52).

The final managerial difference between Group 5 and 7 was the way in which efforts were made to increase the efficiency of solution development. Seen as a positive managerial trait, both groups did this, but it was more frequent and more developed with Group 5. There were four examples of this with Group 7 in the form of simple prompts to speed the process up (E.g. S = 5:3.02–3.04; S = 6:2.22–2.23; S = 11: 1.34–1.35) and 25 examples with Group 5 involving prompts and orchestrating parallel rather than successive activity.

Differences in planning (dyad 1)

Differences emerged in how Group 5 and 7 thought about and prepared for future problem-solving activity. While the complexity of the pupils’ problem-solving prevented cause and effect from being attributed to planning decisions, four areas of difference were identified: (1) determining use of/amount of materials/resources, (2) sequencing, ordering or prioritising, (3) identification of global solution requirements, and (4) working through how an idea should be practically executed. Across both problem-solving sessions, Group 5 spent over three times as long as Group 7 did, engaging in these forms of planning (8m17s vs. 2.23 s), but Group 7 planned on almost twice as many occasions (n = 98 vs. n = 56). Both groups considered the availability of materials for, and matching of materials to, given ideas (e.g. Group 5, S = 5:3.38–3.48; Group 7, S = 4:2.20–2.34; S = 12:1.53–2.00) and both identified global solution requirements. At the start, Group 5 engaged in 12 min of planning in which they read task instructions (S = 1, 0.49–1.49), explored, tested, and compared the available materials (S = 1, 1.49–2.10), and agreed on a starting point. As shown in Excerpt 4, these discussions attempted to integrate thinking on materials, joining methods, placement. As the class teacher observed, Group 7 were eager to begin construction after 4m45s and did so without an agreed starting point. Pupils in this group explored materials in a more reactive way in response to construction.

Excerpt 4 “..a tiny bit of cardboard, right, this is the cardboard, right.. (picks up part) put glue on it so that’s on that, right.. (modelling part orientation) then put glue on it there so it sticks down.. something to stick it down, do you know what I mean?” (Group 5, S = 9, 2.10–2.20)

Despite similar types of planning processes, the planning discourse of Group 5 was more proactive, and this may have minimised inefficiencies and avoidable errors. For Group 7, two group members unintentionally drew the same idea (S = 2, 3.19–3.26), parts were taped in the wrong place (S = 17, 1.26–1.40) and others glued in the wrong order (S = 5, 1.28–1.30 and 1.48–1.56). Such occurrences, however, notably reduced after the group re-started their solution in the second session which also mirrored a 73% drop in poor group involvement. Communication played an important role in planning and there was no evidence of avoidable errors with Group 5.

Differences in reflection (dyad 1)

The most prevent differences in this study were found in how Group 5 and Group 7 reflected upon their developing solutions. Analysis revealed two main forms of reflection that were used differently by groups. ‘Declarative reflection’ lies close to observation and is defined by this study as reflection that does not explicitly reveal anything of a pupil’s knowledge of technical relationships within their solution, e.g.: “that’s not going to be strong…” (Group 7, S = 2, 0.49–0.51). This form of reflection was critical for both groups who used it heuristically to quality assure material developments, but it was used slightly more often by Group 7 (n = 164:4m30s vs. n = 145:4m07s). By contrast, ‘analytical reflection’ is defined as that which does reveal something of a pupil’s knowledge of technical relationships between two or more parts of a solution. Examples of this are shown in Excerpts 5 and 6 where pupils are reflecting upon an attempt made to support the underside of the road surface.

Excerpt 5: “It’s not going to work because it’s in compression and straws bend..” (Group 5, S = 9, 2.3–2.35) Excerpt 6: “no, that’ll be… oh, aye, because that would weight it down and it would go into the water.” (Group 5, S = 14, 3.35–3.38)

Looking across verbal and observational data, there was no consistent pattern to the use of declarative reflection but analytical reflection for both groups was almost exclusively anchored around, and promoted by, the practical enactment of an idea and could be associated with predictions about the future performance of their solution. Overall, both Group 5 and 7 reflected a similar number of times (n = 216 and n = 209, respectively) although the total amount of time spent reflecting was 17% longer for Group 5. This difference in time was accounted for by comparatively more analytical reflection in Group 5 (n = 75:3m47s vs. n = 45:2m10s for Group 7), particularly during the first half of problem solving. It was also interesting that Group 7 engaged with no analytical reflection at all prior to construction.

Findings from process management, planning and reflection led to the framework of difference in Fig. 8 . This also accounts for differences in the amount of time each group reflected upon the task detail, but this was extremely limited (Group 5: n = 7, 26 s; Group 7: n = 5, 10 s).

‘Framework of process differences from comparative analysis of Group 5 and 7’

Process differences across all groups (dyads 1–4)

Task reflection, attempts at increasing efficiency and differences of fragmented vision found with the most contrasting dyad were not sustained across remaining groups. The only sufficiently consistent difference in patterns of solution development was that more successful groups, on average, spent 18% longer in planning and discussion before beginning to construct anything.

Overall, the nature and patterns of good and poor group involvement from dyad 1 were reflected more widely, with some instances of deviation. The more successful group in dyad 4 had more significant and numerous examples of poor group involvement than did the less successful group (n = 16 vs. n = 10), although they made more effective use of roles and task allocation and spent longer engaged in planning processes. Dyad 2 deviated also insofar as the less successful group (13) actually had fewer avoidable errors than Group 6 who accidentally cut the incorrect parts (e.g. S = 15, 2.44–2.47), undertook developments that were not required (e.g. S = 6, 2.11–2.16) and integrated the wrong parts into their solution (e.g. S = 7, 1.10–1.13).

Differences in the nature and use of reflection was one of the most consistently sustained findings between the most and least successful cohorts. All four of the more successful groups engaged more heavily in reflective processes and more of this reflection was analytical in nature. This shows that reflection which explicitly integrates knowledge of technical relationships between different aspects of a solution plays an important role in more successful technical outcomes. Whilst declarative reflection remained important for all groups, it was also less prominent for groups in the less successful cohort. Table 5 summarises the sustained process difference across dyads 1, 2, 3 and 4.

Social & extrinsic differences (dyad 1)

Differences reported in this section lie out with the formal conceptual framework of the study but, nonetheless, were shown to play a role in the technological problem-solving activity of dyad 1. Differences between Group 5 and 7 emerged in three areas: (1) group tension, (2) effects of the classroom competitive dynamic, and (3) study effects. Group tension, which relates to aspects of interaction such as argumentative discourse, raised voices and exasperation, were negligible for Group 5 (n = 4, 0m24s) when compared with Group 7 (n = 38, 2m38s) and related exclusively to pupils having their voiced heard. For group 7, tension was evident during both sessions, but was more significant in the first session before re-starting the solution in session 2 and purposeful attempts to work more collaboratively with the support of the teacher (Group 7, S = 10, 0.36–1.29). Observations revealed that tension was typically caused by pupils failing to carry out practical processes to the standard of other group members, or breaking parts such as the thread supporting the road surface in the 36 th minute of Session 2.

Despite collaborative efforts within groups, there was a sense of competitive dynamic which appeared either to positively bias, negatively bias, or to not affect group activity. This competitive dynamic was present in groups comparing themselves to other groups in the class. Group 7 had 3.7 times as many instances of this as Group 5 with 73% of these negatively affecting the group. These included interference from and with other groups (S = 7, 0.07–0.12), attempting to copy other groups (S = 7, 1.14–1.22) and comparing the solutions of other groups to their own (S = 8, 2.55–2.59). In contrast, Group 5 appeared to be far less affected by perceptions of competition. Around a third of instances were coded as neutral, however, Group 7 experienced more instances of positive competitive effects than Group 5 did (n = 5 vs. n = 1).

Study effects were present for both groups often triggered by the arrival of the researcher at their table to observe or take photographs. The biggest difference in study effects was associated with the audio recorder. Recorder effects for Group 7 were three and half times that of Group 5 involving discussion about how it worked (Group 7, S = 10, 3.04–3.17), or about what was caught or not caught on tape (Group 7, S = 14, 1.01–1.45). Although questionnaire data showed that pupils in Group 5 felt that they talked less in the presence of the recorder, this was not supported by observations, verbal data, or the class teacher. From these findings, the framework of social and extrinsic difference in Fig. 9 was developed.

‘Framework of social & extrinsic differences from comparative analysis of Group 5 and 7’

Social & extrinsic differences across all groups (dyads 1–4)

Most of the social and extrinsic differences identified with Groups 5 and 7 were reflected to greater or lesser extents in other dyads. In addition to less successful groups being more susceptible to researcher and recorder effects, two specific points of interest emerged. Firstly, group tension was considerably more prominent for less successful groups than it was for more successful groups. Although no evidence of a direct relationship was established, tension appeared to accompany poor managerial traits and the changing of group composition (e.g. Group 8, Group 13). The most significant differences in tension were found with dyad 3. No occurrences were found for the most successful group and 29 were seen with the least successful group including aggressive and abrupt communication between pupils involving blame for substandard construction (S = 10, 2.28–2.38), through to name calling (S = 12, 0.20–0.22), arguing (S = 6, 1.46–2.10) and threats of physical violence (S = 11, 3.25–3.29).

Secondly, the more successful groups were influenced by the competitive class dynamic more than the less successful groups were. This is the only sustained finding that directly opposes what was found with dyad 1. These took the form of neutral or negative inter-group effects involving comparing and judging other groups (e.g. Group 6), espionage, copying or suspicion thereof (e.g. Group 6, 8 and 12). Table 6 summarises the sustained social and extrinsic differences across the more and less successful cohorts.

Discussion and Conclusions

This study established and applied three frameworks to capture the epistemic, procedural, and social and extrinsic differences between groups of pupils as they developed solutions to a well-defined technological problem. Social & extrinsic findings revealed higher levels of group tension for the less successful cohort, but that more successful groups elicited more negative responses to the competitive class dynamic created by different groups solving the same problem. Major findings about differences in knowledge and process are discussed. Thereafter, a three-part characterisation of thinking for well-defined technological problem solving is presented in support of pedagogy for Design & Technology classrooms.

The most important of those knowledge differences uncovered were found in: (1) the material development of the solution itself, and (2) the reflective processes used by groups during problem solving. The conceptual framework characterises ‘tacit-procedural knowledge’ as the implicit procedural knowledge embodied in technical skill, accuracy and judgement, and this was further refined in the solutions of more successful groups. Linked to this was the fact that several of the material developments for triangulation and strength were improperly realised by less successful groups which negatively impacted on the functional performance of their solutions. Often, this was despite evidence of a good conceptual understanding of triangulation, tension, and compression in the learning phase. An ontologically endurant solution requires stability over time and lesser developed aspects of tacit-procedural knowledge and knowledge application meant that this was not realised as fully as possible for some groups.

This can be partly explained by the challenge of learning transfer, or more accurately, learning application. Several notable studies have explored these difficulties in technology education (Brown, 2001 ; Dixon & Brown, 2012 ; Kelly & Kellam, 2009 ; Wicklein & Schell, 1995 ), but typically at a subject or interdisciplinary level. The findings of this study suggest that, even when the concepts in a learning unit are tightly aligned with a well-defined problem brief, some pupils find difficulty in applying them within a tangible, material context. It could be argued that more successful groups were better at connecting learning between different contexts associated with the problem-solving task and could apply this with more developed skill and judgement.

The second important knowledge difference arose in the various forms of reflection that groups engaged with. Reflection in this study supports pupils in cycling through the re-application/transformation loop in a similar way to the perception/translation/evaluation blocks of the iterative models of problem solving (Argyle, 1972 ; Miodusre & Kipperman, 2002 ; Scrivener et al., 2002 ). Surprisingly few studies explore ‘reflection’ as a process in technological thinking (Kavousi et al., 2020 ; Luppicini, 2003 ; Lousberg et al., 2020 ), and fewer still in the context of school-level technological problem solving. This study found that more successful groups reflected more frequently, and that more of this reflection was analytical insofar as it explicitly revealed knowledge of technical relationships between different variables or parts of their solution. Such instances are likely to have been powerful in shaping the shared understanding of the group. This type of reflection is significant because it takes place at a deeper level than declarative reflection and is amalgamated with pupils’ subject knowledge and qualitative understanding of their technical solution. This allowed pupils to look back and to predict by explicitly making connections between technical aspects of their solution.

The final area in which important differences were found was management of the problem-solving process which is accounted for by Halfin ( 1973 ) in his mental process set. When analysed, the more successful cohort exploited more positive managerial strategies, and fewer negative traits. They made more extensive and effective use of role and task allocation, spent more time planning ahead and longer in the earlier conceptual phase prior to construction. Other studies have also captured aspects of these for technology education. Hennessy and Murphy ( 1999 ) discuss peer interaction, planning, co-operation and conflict, and changing roles and responsibilities as features of collaboration with significant potential for problem solving in technology. Rowell ( 2002 ), in a study of a single pair of technology pupils, demonstrated the significance of roles and participative decisions as enablers and inhibiters of what pupils take away from learning situations. What was interesting about the groups involved in this study, was that the managerial approaches were collectively more proactive in nature for more successful groups. Less successful groups were generally more reactive to emergent successes or problems during solution development.

The problem-solving activity of pupils in this study was exceptionally complex and a fuller understanding of how these complexities interacted would have to be further explored. Yet, key differences in knowledge and process collectively suggest that effectively solving well-defined technological problems involves a combination of proactive rather than reactive process management, and an ability to make two different types of technology-specific connections: contextual connections and technical connections. Proactively managing is generic and involves planning, sequencing, and resourcing developments beyond those that are immediately in play to minimise avoidable errors with reference to problem parameters. It involves group members through agreed roles and task allocation that, where possible, capitalise on their strengths. Contextual connections involve effectively linking and applying technological knowledge, concepts, and principles to the material context that have been learnt form other contexts out with solution development. This is supported by skill and judgement in the material developments that embody this knowledge. Finally, technical connections appear to be important for better functioning solutions. These are links in understanding that pupils make between different parts of the developing solution that reveal and build knowledge of interrelationships, dependencies and how their solution works. In addition to helping pupils developing effective managerial approaches in group work, this suggests that pedagogical approaches should not assume pupils are simply able to make contextual and technical connections during technological problem solving.  Rather, pedagogy should actively seek to help pupils make both forms of connection explicit in their thinking.

This study has determined that proactive management, contextual and technical connections are important characteristics of the modus operandi of pupils who successfully solve well-defined technological problems. This study does not make any claim about the learning that pupils might have taken from the problem-solving experience. It does, however, provide key findings that teachers can use to support questioning, formative assessment and pedagogies that help pupils in solving well-structured technological problems more effectively.

Ethical approval

Ethical approval for this study was granted by the School of Education Ethics Committee at the University of Glasgow and guided by the British Educational Research Association Ethical Code of Conduct. All necessary permissions and informed consents were gained, and participants knew they could withdraw at any time without giving a reason. The author declares no conflicts of interest in carrying out this study.

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Acknowledgements

I would like to thank Dr Jane V. Magill, Dr. Alastair D. McPhee and Professor Frank Banks for their support in this work as well as the participating teachers and pupils who made this possible.

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Morrison-Love, D. Technological problem solving: an investigation of differences associated with levels of task success. Int J Technol Des Educ 32 , 1725–1753 (2022). https://doi.org/10.1007/s10798-021-09675-5

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Accepted : 25 April 2021

Published : 02 June 2021

Issue Date : July 2022

DOI : https://doi.org/10.1007/s10798-021-09675-5

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14 Major Tech Issues — and the Innovations That Will Resolve Them

Members of the Young Entrepreneur Council discuss some of the past year’s most pressing technology concerns and how we should address them.

The past year has seen unprecedented challenges to public-health systems and the global economy. Many facets of daily life and work have moved into the digital realm, and the shift has highlighted some underlying business technology issues that are getting in the way of productivity, communication and security.

As successful business leaders, the members of the  Young Entrepreneur Council understand how important it is to have functional, up-to-date technology. That ’ s why we asked a panel of them to share what they view as the biggest business tech problem of the past year. Here are the issues they ’ re concerned about and the innovations they believe will help solve them.

Current Major Technology Issues

• Need For Strong Digital Conference Platforms
• Remote Internet Speed and Connections
• Phishing and Data Privacy Issues
• Deepfake Content
• Too Much Focus on Automation
• Data Mixups Due to AI Implementation
• Poor User Experience

1. Employee Productivity Measurement

As most companies switched to 100 percent remote almost overnight, many realized that they lacked an efficient way to measure employee productivity. Technology with “ user productivity reports ”  has become invaluable. Without being able to “ see ”  an employee in the workplace, companies must find technology that helps them to track and report how productive employees are at home. — Bill Mulholland , ARC Relocation

2. Digital Industry Conference Platforms

Nothing beats in-person communication when it comes to business development. In the past, industry conferences were king. Today, though, the move to remote conferences really leaves a lot to be desired and transforms the largely intangible value derived from attending into something that is purely informational. A new form or platform for industry conferences is sorely needed. — Nick Reese , Elder Guide

3. Remote Internet Speed and Equipment

With a sudden shift to most employees working remotely, corporations need to boost at-home internet speed and capacity for employees that didn ’ t previously have the requirements to produce work adequately. Companies need to invest in new technologies like 5G and ensure they are supported at home. — Matthew Podolsky , Florida Law Advisers, P.A.

4. Too Much Focus on Automation

Yes, automation and multi-platform management might be ideal for big-name brands and companies, but for small site owners and businesses, it ’ s just overkill. Way too many people are overcomplicating things. Stick to your business model and what works without trying to overload the process. — Zac Johnson , Blogger

5. Phishing Sites

There are many examples of phishing site victims. Last year, I realized the importance of good pop-up blockers for your laptop and mobile devices. It is so scary to be directed to a website that you don ’ t know or to even pay to get to sites that actually don ’t  exist. Come up with better pop-up blockers if possible. — Daisy Jing , Banish

6. Data Privacy

I think data privacy is still one of the biggest business tech issues around. Blockchain technology can solve this problem. We need more and more businesses to understand that blockchains don’t just serve digital currencies, they also protect people’s privacy. We also need Amazon, Facebook, Google, etc. to understand that personal data belongs in the hands of the individual. — Amine Rahal , IronMonk Solutions

7. Mobile Security

Mobile security is a big issue because we rely so much on mobile internet access today. We need to be more aware of how these networks can be compromised and how to protect them. Whether it ’ s the IoT devices helping deliver data wirelessly to companies or people using apps on their smartphones, we need to become more aware of our mobile cybersecurity and how to protect our data. — Josh Kohlbach , Wholesale Suite

8. Deepfake Content

More and more people are embracing deepfake content, which is content created to look real but isn ’ t. Using AI, people can edit videos to look like someone did something they didn ’ t do and vice versa, which hurts authenticity and makes people question what ’ s real. Lawmakers need to take this issue seriously and create ways to stop people from doing this. — Jared Atchison , WPForms

9. Poor User Experience

I ’ ve noticed some brands struggling with building a seamless user experience. There are so many themes, plugins and changes people can make to their site that it can be overwhelming. As a result, the business owner eventually builds something they like, but sacrifices UX in the process. I suspect that we will see more businesses using customer feedback to make design changes. — John Brackett , Smash Balloon LLC

10. Cybersecurity Threats

Cybersecurity threats are more prevalent than ever before with increased digital activities. This has drawn many hackers, who are becoming more sophisticated and are targeting many more businesses. Vital Information, such as trade secrets, price-sensitive information, HR records, and many others are more vulnerable. Strengthening cybersecurity laws can maintain equilibrium. — Vikas Agrawal , Infobrandz

11. Data Backup and Recovery

As a company, you ’ ll store and keep lots of data crucial to keeping business moving forward. A huge tech issue that businesses face is their backup recovery process when their system goes down. If anything happens, you need access to your information. Backing up your data is crucial to ensure your brand isn ’ t at a standstill. Your IT department should have a backup plan in case anything happens. — Stephanie Wells , Formidable Forms

12. Multiple Ad and Marketing Platforms

A major issue that marketers are dealing with is having to use multiple advertising and marketing platforms, with each one handling a different activity. It can overload a website and is quite expensive. We ’ re already seeing AdTech and MarTech coming together as MAdTech. Businesses need to keep an eye on this convergence of technologies and adopt new platforms that support it. — Syed Balkhi , WPBeginner

13. Location-Based Innovation

The concentration of tech companies in places like Seattle and San Francisco has led to a quick rise in living costs in these cities. Income isn ’ t catching up, and there ’ s stress on public infrastructure. Poor internet services in rural areas also exacerbate this issue. Innovation should be decentralized. — Samuel Thimothy , OneIMS

14. Artificial Intelligence Implementation

Businesses, especially those in the tech industry, are having trouble implementing AI. If you ’ ve used and improved upon your AI over the years, you ’ re likely having an easier time adjusting. But new online businesses test multiple AI programs at once and it ’ s causing communication and data mix-ups. As businesses settle with specific programs and learn what works for them, we will see improvements. — Chris Christoff , MonsterInsights

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