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Employers login, modelling career in india : how to become a model: a complete guide for 2024.

About Modelling Career

If starting your modelling career in India is your goal, then be prepared & get ready to explore India’s vibrant entertainment industry, diverse fashion scene, and rich cultural legacy. You must carefully weigh your options and commit to rigorous training to get admission into the charming and glamorous world of modelling.

In addition, social networking and ongoing training are essential for a successful career in this field. More significantly, prospective models need to pay close attention to their bodies. We’ll provide you all the advice and insights you need to start your modelling career in this blog. 

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What Does A Model Do?

Depending on what kind of modelling job one selects, a model must work in a variety of industries and for a number of clients. One cannot put their job description in a box. In order to determine the precise description of the kind of job a model performs , one must investigate a variety of options. 

For the benefit of those who aspire to be models, we have included a list of what needs to be done:

• Following the instructions of the photographers or cinematographers when taking photos or striking poses
• Participating in advertising campaigns and ads that highlight clients’ products
• Working together with designers and stylists to create the necessary looks
• Participating in trade exhibitions and promotional events to advertise and promote goods and services

Read More: Creative Career Options

How To Become a Model?

Anybody who wants to be a model must work really hard and go through several phases. To succeed in the modelling industry, a person needs to work on their physique consistently. 

To assist those who are just starting out, we have included a detailed step-by-step guide on how to become an Indian model:

Tips to build career in modelling

Anybody who wants to be a model must work really hard and go through several phases. To succeed in the modelling industry, a person needs to work on their physique consistently. To survive in this cutthroat industry, candidates must make sure they are always improving themselves and their skill sets . 

Select the modelling style that you wish to use 

The first step in beginning a modelling career is determining what kind of modelling best fits the individual. An applicant must use videos and photographs to specialise in the chosen form after choosing the modelling stream. 

Start practising at home 

Thereafter, start practising at home with the help of videos of professional models. Learn from their poses and moves. Observe their walk and perfect your poses and runway walks.

Train and exercise 

Maintain your health and build the necessary body to secure a long-lasting profession. To create a timetable, sign up for yoga sessions, aerobics courses, dancing studios, or local gyms. Additionally, because this job is very demanding, monitor your eating habits and eat healthily. It is possible to sustain both physical and mental fitness with a balanced diet. 

Build your portfolio

Start with taking your pictures and shooting yourself at home and in aesthetically surrounding locations to create a portfolio . Choose and work on modelling assignments to build a strong portfolio. 

Look for an agent

After learning basic skills, search for an agent that suits your necessities. Always carry your portfolio while visiting a modelling agency. Provide relevant information about your body to the agency. They will help you find suitable opportunities. 

Look for opportunities to be noticed

Never stop promoting yourself, even after joining an agency. To be successful as a model, you must constantly search for suitable modelling opportunities and establish connections with possible customers through social events and social media.

Use social media

Make use of social media channels to showcase your skills. Use social media to connect with a larger audience and seize modelling chances.

Essential Skills And Qualities For Modelling Career

Just like any other professional, a model needs a certain set of abilities. In addition, there are some attributes that one needs to possess to become a model. These abilities and characteristics consist of:

• Technical proficiency with a camera, photography, lighting, makeup, costumes, and set design. 
• Dependable in their work, practising and exercising frequently.
• Developing an attractive and appealing body
• Be confident and tasteful.
• Tenacity and ardour 
• Ability to endure extended shooting sessions and fatigue 

Read More: Cinematic Photography: Meaning, Ideas and Tips

How To Start A Modelling Career With No Experience

Here are some pointers for someone with zero experience looking to get a modelling job in any of the aforementioned sectors.

Build a Killer Modelling Portfolio

The most important step to becoming a top model in India is to build a portfolio. It will help you in approaching casting agents and inform them about what you can bring to the table.This portfolio will also showcase your strengths through high-quality and professionally taken photos. This could refer to pictures you have taken professionally by a photographer, rather than necessarily pictures from modelling gigs.

For in-person casting calls, you will need to assemble a typical hardcopy portfolio consisting of 8–12 photographs.

It also helps to have an online portfolio. You can take advantage of it to showcase the breadth of your versatility as a would-be model and showcase why brands need you as their new face.

Read More: Independent Filmmakers in India

Find the Right Modelling Agency

Get yourself signed to a modelling agency; all models require them. A good agency will support you, guide your modelling career, make necessary improvements to your portfolio, arrange go-sees, and much more.

Social networking is being used by agencies to find new talent without ever having to leave the office. Making use of the appropriate Instagram hashtags may even help you schedule a meeting with a talent representative.

Read More: How Networking Can Build Your Creative Career

Just remember that  you do your research and work only with an agency that aligns with your goals, supports your passion and is positive about helping you achieve them. Diffr is a platform that will help you find top modelling gigs across India which are authentic and 100& verified. 

Learn to Work the Look

You should always dress in clothing that emphasises your body structure. A model should be proficient in makeup application and be able to quickly transform into several appearances. In modelling, your skin and body are your greatest assets, therefore look after them.

But it’s not just about that. You should also have a few poses nailed down that you drop into at the drop of a hat. To be more precise, you should be able to perform them automatically. If you are accepted to a model casting and have to make an impression right away, this is a huge help. Thus, practice such positions in front of the mirror and do a little homework!

Use Your Social Media Platform

Doesn’t matter how many followers you have, social media offers the perfect opportunity to get your images out there and let the world know that you are an upcoming model. 

However, if you are using social media to attract talent agencies, be smart and thoughtful. Choose your best photos and tag agencies wisely. Don’t use a scattergun approach or overdo it as you may just end up doing more harm than good.

Be Professional

It’s difficult to begin a modeling profession, particularly if you lack expertise. To succeed in this field, you must conduct yourself professionally. You must answer phones, arrive on time for meetings, shoots, and casting sessions, and you must quickly respond to voicemails and emails.

Many models miss out on possibilities because they believe their beautiful looks would get them by with little to no effort. Be professional because this is a unique position and you’ll be working with people that appreciate professionalism .

Learn to Embrace Rejection

Accept that not everyone you approach or who views your headshots will be a good fit for your company. Throughout their careers, even the most attractive supermodels were told no multiple times. In the modelling industry, criticism is commonplace, therefore it’s best to learn to accept it as soon as possible.

Challenge yourself to shrug off rejection each week. To learn how to accept rejection, a conscious mantra like “Every NO is a step closer to a YES” can be exactly what you need.

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FAQ About Modelling Career

Start by learning basic modelling techniques, practising regularly, and building a portfolio to showcase your work.

There are no strict basic qualifications, but having a good look, confidence, and professional photos can help in starting a modelling career.

Although there are different height requirements, it is generally desirable for ladies to be between 5’9″ and 6’0″ and for men to be between 6’0″ and 6′ 2″.

Established models can make anything from a few hundred to several thousand dollars per hour or per assignment, while actual model compensation varies greatly.

Although the duration of modelling careers varies, many models go on to other positions or sectors of the economy following their prime years in the fashion industry.

The salary range for models in India varies; novices might expect to make between ₹5,000 and ₹10,000 every assignment, while seasoned models may make much more, contingent on the job and their notoriety.

To network within the industry, attend casting calls, hone your professional portfolio, sign with a respectable modelling agency, and get work as a model. You can also register on Diffr.co, build your unique portfolio and explore top creative jobs.

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How to prepare for a modelling assignment

• Posted by admin
• March 16, 2022

One of our booking agents are calling to tell you all about an exciting assignment our client wants to cast you in….Lucky you and a big congrats from us!

Your mind is ticking fast and you’re now thinking about how to prepare for it like a pro! But first, you have to accept! We schedule another call to give you time to think it over and once the wait is over where we fulfil our promised communication, you accept our invitation to work with us – yay! 

We’re thrilled as much as you are. You’re in safe hands because our modelling agency walks you through preparing for your assignment every time an opportunity crops up without fail.

Now, if you’re contemplating on choosing this career path – you’re unsure for whatever reason – you still might be wondering how our models prepare for assignments. Well, guess what? We’d thought we’d help share some of the important info we exchange with our very own models when they hit up an assignment (because we’re nice like that!). More importantly, we know that the unknown can be a bit nerve-racking so we’d like to remove any anxieties or pre-modelling jitters you might be experiencing. Let’s get cracking!

Understanding the brief

When we call to inform you that you’ve been selected, we talk about the brief in detail. We cover who the client is, what their marketing entails and what they expect from you. Sounds simple but there’s a lot of detailed information within these parameters. But not to fret, we include all of this info in an email so that you can revisit it whenever need be.

Reviewing the schedule

You’ll be provided with the schedule of the day (or days, depending on the length of the project). What time you need to be where the client has requested has to be the most important part you need to make provisions for. It’s no use turning up late, or on another day, unfortunately, unless there’s a valid reason of course. So, to prepare, models need to know their journey plan to a tee and ensure they are on time!

Clothing, hair and makeup

We’ll go over what the client wishes for you to wear – colour schemes in particular. When it comes to hair and makeup, it usually tends to be natural and neutral but again, depending on the client’s request, we will fill you in ahead of time.

Assignment preps

Here’s a handy checklist to work through:

· Sleep well

· Eat clean and healthy foods

· Exercise to feel energised, strong and confident

Closer to the time you can:

· Organise your bag (include a compact mirror, makeup, brush, moisturiser, facial wipes, money and snacks).

· Know your route and travel timings

· Have your outfit ready

· Charge your phone

· Pack charger or power bank

Make yourself memorable

You want to wow the client you’re modelling for so that they remember you, your baby, your child, your teen, your family, your body part (if you’re a hand, ear, foot, leg model) or your pet!!! (Models Direct cover a plethora of modelling divisions so there are more categories to unlock!) So be well prepared when it comes to timing, travel, transportation mode, dress code, hair and makeup. And there you have it! But remember, if there’s ever anything ambiguous or oozing uncertainty, our agents are always a call or email away!

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• Modeling Portfolio

Modeling Portfolio Guide for Beginners

Elevate Your Career: Book Your Modeling Portfolio Shoot Today!

Modeling Portfolio Examples for Aspiring Models

Whether you’re a complete beginner, have some experience, or are a seasoned professional, we cater to anyone interested in creating their first modeling portfolio or updating their existing one.

Photoshoot Services for a Modeling portfolio

Explore our diverse range of photo packages designed specifically for your Modeling Portfolio. Seeking something one-of-a-kind? Ask about our custom packages crafted just for you.

Express Package

• outdoor session
• 1 retouched photo
• posing guidance

Essentials Package

• 2 retouched photos
• wardrobe guide

Deluxe Package

• 3 retouched photos
• image selection assistance

Ultimate Package

• professional photography studio
• 12 retouched photos
• celebrity hair stylist included
• celebrity make up artist included
• celebrity wardrobe stylist included

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Modeling portfolio essentials: capturing the right 3 looks.

Your modeling portfolio should include three essential “looks,” which are outfit changes.

• Lifestyle Look (includes model headshots)
• Editorial Fashion Look
• Body (physique) Look

In modeling portfolios, “looks” can also be referred to as “outfits,” “outfit changes,” or “wardrobe options.” These terms are interchangeable and refer to the different clothing options that you showcase in your portfolio.

Lifestyle Photos in a Modeling Portfolio

Including lifestyle photos in your modeling portfolio is crucial to your success as a model. These photos are not meant to showcase high-fashion or avant-garde looks but rather represent a more natural and approachable version of yourself.

The reason why lifestyle photos are so important is that they attract commercial and lifestyle clients who need models for their photoshoots. These clients shoot frequently due to the volume of clothing they produce each season. As a result, they need to regularly book models for their photoshoots, providing consistent income for models who work with them.

For instance, when shopping for clothes online, the models seen in the images are doing commercial modeling, which is also known as catalog or e-commerce modeling. Notable commercial clients can range from large retailers like Target , J.Crew , or Nordstrom to brands that specialize in a particular niche market like scrub brands, cheerleading uniform brands, or military exchange clients.

In summary, having lifestyle photos in your modeling portfolio is essential to showcasing your range as a model and attracting commercial clients. By presenting a more approachable and natural version of yourself, you can position yourself for more work opportunities in the modeling industry.

Fashion Forward Photos in Your Modeling Portfolio

Fashion photography is the process of capturing models in the latest fashion trends or products through editorial images that showcase a certain mood. These images are often used in billboards, magazine spreads, and other high-profile media.

In a modeling portfolio, fashion photos are essential as they help models showcase their range and versatility when posing and wearing fashionable clothes. Including strong fashion photos in a modeling portfolio can increase the chances of getting noticed by clients for high-profile fashion campaigns or magazine fashion editorials.

Fashion campaigns are high-profile jobs that models dream of. They are designed to sell a brand or product and are presented in a way that appeals to the specific target audience. Fashion campaigns are usually shot by well-known photographers in exotic locations and published in major media outlets.

These campaigns are difficult to get and models need to have a strong modeling portfolio and the right look to fit the campaign. Smaller brands also need models for their campaigns, which can provide good images and experience for models to add to their modeling portfolio.

Body Photos Modeling Portfolio

When it comes to modeling, showcasing your body type is crucial to landing the right gigs. That’s why having model body photos in your portfolio is a must-have.

Body photos allow potential clients to get an idea of your body type and determine if it fits their needs. It’s especially important for clients who are considering models for swimwear, brief, or lingerie photoshoots, as well as for runway clients showcasing swimwear apparel.

Female Wardrobe & Make-Up Tips for lifestyle Photos

When it comes to casual outfits for women, it’s important to keep it simple and comfortable. Whether you’re headed to the beach, park for a picnic, or hanging out with friends, you want to look fresh and relaxed.

For instance, you can try pairing denim shorts with a vintage t-shirt and converse shoes. Jumpsuits, sweater dresses, or denim pants with wedges and a tank top are other great options that work well for lifestyle photos. When selecting colors, choose ones that complement your skin and eye color and avoid patterns or logos that can be distracting.

Similarly, when it comes to casual hair and make-up, the same relaxed guidelines apply. Avoid heavy make-up and bold lipstick. Instead, opt for a bit of concealer under the eyes and a lip color that matches your natural lip shade. You want to keep your make-up minimal, allowing your natural skin and freckles to show through. For your hair, loose beachy waves work great.

Avoid stiff and formal hairstyles that may not fit the casual look. Remember, overdoing it with hair and make-up may not be useful, as agents may not be able to use those images. They may require you to re-shoot the look altogether.

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Men’s wardrobe & grooming tips for lifestyle photos.

When it comes to modeling, casual outfits are often required for shoots. For men’s modeling, choosing the right outfit can make a significant difference in how you appear in pictures.

Slim-fitted chinos paired with a simple striped polo and clean white converse make a great outfit. Alternatively, you can choose medium-length linen shorts that are rolled up, paired with a simple t-shirt and sneakers for a casual and comfortable look.

Keeping it cool is important, and you should stay away from clothes that feature any type of characters, recognizable faces, or figures. Instead, opt for solid colors that enhance your eyes and skin, and keep the focus on you.

In addition to outfits, grooming is essential for men’s modeling. Always moisturize your face to prevent dry skin and carry chapstick to take care of any cracked lips.

Dark circles can be covered using concealer, so it’s important to work with a makeup artist to ensure that your face looks perfect. Untamed nose, ear, and eyebrow hairs can detract from your appearance, so make sure they’re trimmed and well-groomed.

Facial hair should be tidy and trimmed, or you can go for a clean-shaven look. These grooming tips, combined with the right casual outfits, are a must for professional modeling pictures.

Make-Up & Wardrobe Tips for Female Fashion Look

To make your fashion photos stand out, try to find clothing with unique cuts, shapes, patterns, or prints. However, be careful not to go overboard with the prints as they may come off too strong.

One way to balance the prints is by layering a solid-colored jacket or coat. Experiment with different pieces and try mixing and matching outfits that you wouldn’t normally put together. This can lead to unexpected and impressive results.

To complement your outfits, you should also consider your hair and make-up. When styling your hair for a fashion photo, you can go for a distressed or stylized look depending on the desired effect. Sometimes, a simple tousled hairstyle can work wonders.

For make-up, you can go for a subtle smokey eye or a bold lip, but try not to combine all three elements together. It is best to choose one or two and avoid cliché looks such as adding bird feathers or a nest in your hair.

Tips on Grooming & Wardrobe on Mens Fashion Look

Looking for inspiration to elevate your fashion game? Check out these fashion outfit ideas for men that will keep you looking stylish and confident.

A cool leather jacket paired with dark jeans and military boots is a classic and edgy combination that never goes out of style. Embrace your inner “bad boy” with this look.

If you prefer a more minimalistic look, try a simple black t-shirt with rolled-up sleeves. This instantly adds a cool factor to your outfit. For added drama, throw on a trench coat over the t-shirt.

A turtle neck looks great when paired with a fitted blazer, jeans and lace-up boots. Consider going for a monochromatic look and breaking it up with one contrasting color.

Grooming is a crucial part of any fashionable look. If you have facial hair, make sure to keep it neat and tidy. A well-groomed beard can add an extra element of style to your look, but patchy facial hair can detract from it. If you don’t have a full beard, consider going clean shaven.

When working with a hair and makeup artist, they may add texturing product to your hair to create a more disheveled look. This can add some edge to your overall appearance.

In some cases, makeup artists may even apply a bit of eye shadow to male models, giving them a punk-rock feel that can help complete a fashionable look.

Modeling Photo Tips: Female Body Images

When preparing a modeling portfolio, it is essential to select the right wardrobe that flatters your body and showcases your features. If you’re under 18, wearing appropriate swimwear or a “sporty” look can be ideal for body photos. For those above 18, lingerie or swimwear works best.

When choosing swimwear, opt for a solid-colored two-piece or one-piece swimsuit that complements your skin color. Avoid distracting patterns or logos that might shift attention from your body. The client or agent is interested in your body and not the swimwear brand.

Men’s Body Wardrobe and Grooming Tips for a Modeling Portfolio

For men over the age of 18, wearing nicely fitted briefs, hip briefs trunks or boxer briefs is recommended for body images in a modeling portfolio. It’s important to avoid loose boxers, as they don’t photograph well. White or black briefs work best, and it’s crucial to ensure they are not stained. To make a good impression, it’s best practice to buy a fresh, new pair of briefs.

Grooming for men is equally important for a modeling portfolio. Moisturizing the face, applying chapstick to lips, and clean shaving the face is recommended for body photos. Lotions should be applied on the neck, chest, arms, thighs, and legs to give skin a healthy shine. Trimming body hair, such as chest hair and hair between thighs, is advisable. It’s essential to tame any wild hairs on ears, nose, and eyebrows, and to take care of finger and toe nails.

Body shots are essential in men’s comp cards, and for young adults under 18, fitted jeans with no shirt on or a sport-inspired outfit can be worn. The jeans should be fitted with no rips or tears, and the sport-inspired outfit should consist of a fitted pair of running shorts, running shoes, and no shirt on.

As a premier male model photoshoot studio, we have the expertise to capture the perfect body photos for a male model portfolio. If you’re an aspiring male model and have questions about how to make a male model portfolio or portfolio requirements, please contact us below.

What Are Headshots for Modeling?

Modeling headshots are pivotal professional photographs that focus on showcasing a model’s facial features, typically from the chest or shoulders up. Serving as the face of your portfolio, they’re often the first thing agents, casting directors, and clients see.

Their format is generally standard: your face is centered, set against a neutral background, with an emphasis on your eyes, hair, and bone structure. The aim is to present a clean, simple, and uncluttered image where minimal makeup and straightforward hairstyles allow your natural beauty to shine.

In addition to the conventional headshots, you may also include environmental and lifestyle variations in your portfolio. Environmental headshots place you in specific settings like parks or beaches, while lifestyle headshots show you engaging in activities or hobbies. These offer a more rounded portrayal of your personality and potential.

To capture compelling headshots, it’s crucial to collaborate with a professional photographer experienced in the modeling industry. They can guide you on optimal poses, lighting, and angles. Your wardrobe, hair, and makeup should be carefully chosen to highlight your features without overshadowing them. If you’re targeting beauty or skincare campaigns, include a specialized beauty shot focusing on your skin, eyes, and lips.

By integrating high-quality, diverse headshots into your modeling portfolio, you enhance your chances of making a powerful first impression and succeeding in a highly competitive field.

Consider Hiring a Fashion Stylist for Your Modeling Portfolio

When creating a modeling portfolio, working with a professional fashion stylist can significantly enhance your images. A talented stylist possesses an eye for fashion that can turn any outfit into a trendsetting piece.

If you are unable to find a stylist in your area, there are several alternatives to consider. You can browse through the pages of the latest fashion magazines to gain a better understanding of what’s currently in vogue. Additionally, try exploring local thrift shops to put together an ensemble that mimics the latest fashion trends.

It’s worth noting that fashion branding does not necessarily make an entire outfit stylish. Rather, the entire ensemble’s composition is what contributes to its fashionable appearance. Thus, partnering with a fashion stylist can provide an advantage in composing an attractive and professional modeling portfolio.

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Top modeling agencies in the united states by city.

If you are interested in becoming a male or female model, it is essential to research and find the best modeling agencies in your area. Below we have listed the top modeling agencies in various cities in the United States.

Modeling Agencies in New York City

• New York Model Management
• Wilhelmina Models
• FORD Models
• Elite Model Management
• Red Model Management
• NEXT Management
• MUSE Model Management
• DNA Model Management
• Heroes Model Management
• Major Model Management
• Marilyn Models

Modeling Agencies in Chicago

Working with a modeling photographer in Chicago as well as local lifestyle and catalog clients can provide you with excellent experience as a model in Chicago. Consider the following modeling agencies in the Windy City:

• Select Model Management

Modeling Agencies in Miami

Miami is a hub for the fashion and modeling industry in the United States. Check out the following top modeling agencies in Miami:

• Front Model Management
• Next Management

Modeling Agencies in Dallas

Dallas provides an excellent opportunity to work with local commercial and e-commerce clients. Here are some modeling agencies in Dallas:

• The Campbell Agency
• Wallflower Management
• Grogan Management
• The Callidus Agency

Modeling Agencies in Seattle

Consider the following modeling agencies in Seattle for gaining modeling experience:

• Heffner Management
• TCM Models & Talent

Modeling Agencies in Oklahoma

For those living in Oklahoma, the following modeling agencies can help you get started in your modeling career:

• Brink Model Management
• PRIM Management

Modeling Agencies in Atlanta

Atlanta has a growing fashion and modeling industry, and these modeling agencies can provide you with opportunities:

• The Salt Agency
• Ei Model Management
• Ursula Wiedmann Models

Modeling Agencies in San Francisco

Check out the following modeling agencies in San Francisco to work with local clients:

• Look Model Agency
• Scout Model & Talent Agency
• RAE Model & Talent Agency

Modeling Agencies in Portland

Portland has a unique fashion and modeling industry, and these modeling agencies can provide you with opportunities:

• Muse Model Management
• Option Model & Media
• Reaction Models & Talent
• Ryan Artists

Modeling Agencies in Phoenix

Phoenix offers modeling opportunities through the following modeling agencies:

• Signature Models & Talent
• Southwest Model & Talent
• The Young Agency
• Deborah Maddox Agency
• FORD/Robert Black Agency

Mastering Movement: The Key to a Successful Modeling Portfolio

For new models, learning how to pose can be a daunting task. Unlike senior graduation photos, modeling images require more than just striking a static pose. To succeed in the industry, models must also master the art of movement to exude confidence and captivate audiences on set.

With as many as 15 crew members or more present during a shoot, it’s easy to feel overwhelmed in front of the camera. But freezing up is not an option, as it could lead to losing out on future modeling jobs. Therefore, it is crucial for models to feel comfortable moving in front of the camera.

Working with a professional model portfolio photographer is one of the best ways to master movement. They can provide guidance and direction, helping models to identify their best angles and movements that highlight their unique qualities. Additionally, they can also offer valuable feedback on what works and what doesn’t.

Mastering Model Posing Techniques: Tips from a Supermodel

Coco Rocha, a renowned supermodel, has been featured in numerous fashion campaigns for well-known brands like Longchamp, Banana Republic, L’Oréal, and Diesel. In this insightful behind-the-scenes video, Coco demonstrates the art of model posing, showcasing how her poses and expressions vary based on the clothing she’s wearing.

Aspiring models can take inspiration from Coco and develop their own repertoire of posing techniques. This will help them to appear confident, alluring, and poised in front of the camera. Depending on the type of photoshoot, one can experiment with different poses, expressions, and body language to create a striking visual impact.

Leveraging Digital Platforms for Modeling Success

In the digital era, having a robust online presence is essential for models. Both a digital modeling portfolio and an active social media presence are crucial tools to widen your reach and engage with potential clients, agents, and industry professionals.

Creating a Digital Modeling Portfolio

A digital modeling portfolio serves as a professional, accessible platform to showcase your range and skills. It can exist in various formats—be it a dedicated website, an online portfolio, or a digital comp card. The core goal is to present a platform that mirrors your unique style and strengths in modeling. Populate it with high-quality images that display your versatility, incorporating fashion, lifestyle, and body shots. Ensure the layout is visually appealing and easy to navigate for the best user experience.

Mastering Social Media for Modeling

In addition to a digital portfolio, social media platforms like Instagram, Twitter, and Facebook offer an expansive stage to display your modeling prowess. To make the most out of social media, optimize your profiles with quality images and accurate descriptions of your experience and persona. Use targeted hashtags to boost your visibility and attract followers interested in your modeling niche.

Networking is another pivotal aspect of your social media strategy. Actively connect with fellow models and industry leaders, and make sure to engage with your audience by promptly responding to comments and messages.

In summary, a well-curated digital portfolio combined with strategic social media usage can significantly enhance your online presence. By focusing on these platforms and employing best practices, you can effectively showcase your modeling skills and create invaluable industry connections.

Finding the right photographer for your modeling portfolio

As you embark on your modeling career, securing a skilled local photographer is a crucial first step. Not only does it simplify logistics, but it also lays the foundation for a professional relationship within your local network. While it’s tempting to ask for free photos, approaching a photographer professionally by offering a mutually beneficial arrangement can create a lasting impression.

Options for Free Portfolio Work

If budget constraints are a concern, there are ways to build your portfolio without heavy investment. One approach is to look for emerging photographers keen to expand their own portfolios. Utilize search terms like “new photographer for modeling portfolio” or “aspiring photographer for model portfolio” to identify potential candidates. Reach out to express your interest and propose collaborative projects that can enhance both your and the photographer’s portfolio.

Broadening Your Search

While major cities like NYC or Los Angeles offer a plethora of experienced modeling photographers, those located in smaller regions may need to expand their search. Consider other photography niches, such as senior portrait or engagement photographers, who may possess the skills needed for modeling shots. However, vet these options carefully by reviewing their portfolio and social media presence to ensure their work aligns with your modeling aspirations.

Male Model Portfolio Examples

At Sergio Garcia Studios, we understand the importance of a compelling modeling portfolio. Led by esteemed photographer Sergio Garcia, our team specializes in male model headshots and a range of other styles including lifestyle and body shots. With a reputation for excellence, Sergio has collaborated with top agencies and delivers portfolios that help you get signed or secure jobs.

We offer our services across key U.S. cities like New York, Los Angeles, Chicago, and Miami. If you’re looking to elevate your modeling career, contact us for a tailored photoshoot designed to highlight your unique attributes.

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Faqs: modeling portfolio, how do i learn how to pose.

Starting a modeling portfolio can be challenging for beginners who have no idea what poses to strike or how to look confident and comfortable in front of the camera. However, the best way to learn how to pose like a model is to practice in front of a mirror. Tilt your head one way, put your hands in your pocket, and lean your body weight to one side. Try out different variations until you find what looks good for your male model headshots.

What is a beauty headshot for a modeling portfolio?

A “beauty shot” is a close-up photograph of the model’s face, typically taken during the lifestyle section of a modeling photoshoot when the hair and makeup are clean and simple. These photos are often used as the cover image for the model comp card. Men need these type of shots too, and they may reference them as a portrait shot instead of a beauty shot.

What photos should be on a model comp card?

A comp card is a printed card with two sides. The front side usually contains the beauty shot, and the back side has the model’s measurements, full name, agency name, and four photos. The four photos should consist of a mix of a lifestyle image, a fashion image, and a body image.

What size should a modeling portfolio be?

The standard size of a modeling portfolio is usually 8×10 or 8.5×11, but this can vary depending on the agency you are signed with.

What types of models are modeling agencies looking for?

Modeling agencies are looking for different types of models, including fashion/editorial models, runway models, swimsuit/lingerie models, parts models, commercial models, fitness models, and print models.

How to become a teenage model?

To become a teenage model, you can build your teenage modeling portfolio and submit it to local modeling agencies. The modeling agencies may also request quick snap shots of yourself, including a full-length shot, a close-up beauty shot, and a 3/4 shot. You can wear fitted jeans and a simple t-shirt or tank for these snap shots, and women should have their hair straight down for some and pulled back in a ponytail for others. Make sure you are evenly lit when taking these modeling “polaroids.”

Is It Necessary to Reside in a Major City to Pursue a Modeling Career?

Large cities are typically home to many top model portfolio photographers and modeling agencies in the US. However, aspiring models may wonder if they need to live in a big city to break into the modeling industry.

While big cities like New York, Los Angeles, and Chicago are widely recognized as hubs for modeling, it is not always necessary to live in a major city to pursue a modeling career. Many mid-size markets or smaller cities such as Atlanta, Seattle, and Las Vegas offer opportunities for aspiring models to get started and make a decent living.

In fact, some of these cities have their own local market, where numerous brands such as Neiman Marcus, BELK, and JC Penny are headquartered and need models. This makes mid-size cities a great place for models to develop their portfolios, regardless of their modeling niche, whether it be plus-size, commercial, or male modeling.

Furthermore, these mid-size cities can also provide models with an opportunity to learn the ins-and-outs of the modeling industry before potentially moving on to larger markets. By working with local agencies and photographers, models can build their experience and network in the industry.

Let’s Work on Your modeling portfolio together

a robust modeling portfolio is your ticket to success in the industry, serving as a crucial tool for both aspiring models and agencies. The goal is not just to have visually appealing images but to showcase your versatility in various settings, garments, and emotions. Working with a professional portfolio photographer is highly recommended, as they can guide you through poses and provide a team, including hair and makeup artists, to ensure top-quality images.

Male or female, your portfolio should demonstrate your range—from swimwear to formal wear—and your ability to convey different emotions that fit the campaign’s needs. Once your portfolio is ready, your next steps include connecting with modeling agencies to launch your career. By adhering to these guidelines, you’ll be well on your way to standing out in the competitive world of modeling.

Mastering the Art of the Modeling Portfolio

This guide serves as a comprehensive roadmap for aspiring models looking to create a standout modeling portfolio. It delves into the importance of showcasing not just physical beauty, but also versatility and emotional range in various settings and garments.

Recommendations for collaborating with professional photographers are offered, emphasizing their role in capturing diverse and high-quality images. From choosing the right photos to connecting with agencies, this guide equips you with the essential tools to launch and sustain a successful modeling career.

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7. 3D models

When you start creating 3D Models, you should be familiar with these terms:

• Part  – a single component or body that you are designing
• Dimension  – a constraint applied to edge length or surface size
• Assembly  – an arrangement of parts to form a construction
• CAD  – Computer Aided Design

Types of 3D models

There are two types of 3D models that you might like to design:

• Geometric models   — components made entirely from lines, shapes and extrusions
• Organic models  — involve using curves to sculpt a mesh to a desired form.

Geometric models are typically used for engineering and construction applications, while organic models are used in 3D animations and industrial design. A combination of both types is also possible.

Designing a 3D model for your assignment

Use this strategy to approach the design of a 3D model:

• Draw  a rough sketch of the part with pencil and paper
• Annotate  your sketch with dimensions, constraints or other key features
• Plan  steps to convert your drawing to a digital model (e.g. sketch, extrude, fillet, etc…)
• Apply  these steps in your 3D modelling software
• Refine  your model according to other details from your sketch
• Verify  that all dimensions and constraints were correctly applied.

Prepare your model for 3D printing

3D modelling software can export your model in a variety of formats. Depending on how or where you 3D print your model, these formats are typically used:

• A stereolithography file (.STL)
• A Wavefront 3D model file (.OBJ)

Steps for exporting your model in these formats are generally found in the help pages of the software you are using.

3D modelling software

Use of 3D modelling software largely depends on the model you are trying to create. If you are creating:

• a geometric engineering component, CAD software is usually the best option
• an organic model for 3D animation, then 3D modelling software is best.

Get more  information on 3D modelling tools .

3D Photogrammetry software

Photogrammetry software is a very useful tool for constructing 3D Models from photographs. This can be done with photos from a phone or digital camera, and then the software’s algorithms do all the work.  Visit 3D Photogrammetry tools for more information.

Ways to get 3D models

3D modelling can be used in a variety of ways. You can upload a 3D model for online interaction, 3D printing, animation or for use within VR/AR applications.

You can get a 3D model via:

• CAD or 3D Modelling software — 3D Models can be created from scratch using this software
• Photogrammetry — construct 3D models from photographs at the click of a button using specialised software
• MRI/CT Scan Conversion — extract a 3D model from any CT Scan or MRI data
• 3D Scanning — scanning an object with a 3D scanner
• Online Collections — download an online 3D model

Examples of 3D models you can create using photogrammetry. Press the play buttons to interact with each model:

Trilobite 3D model

Trilobite by Nick Wiggins on Sketchfab

Kangaroo Cranium 3D Model

Kangaroo Cranium by Nick Wiggins on Sketchfab

Model of sculpture ‘A student’s head’ 3D Model

Model of sculpture ‘A student’s head’ by The University of Queensland Library on Sketchfab

 Find existing 3D models

Find designs to download and use under a Creative Commons Licence from:

• Thingiverse  — a MakerBot website for sharing 3D models
• Yeggi  — 3D model search engine
• NIH 3D Print Exchange  — a collection biomedical 3D models that include, anatomy objects, proteins, cells and tissues

Museum collections

Some museums are now making parts of their collections available as scans for home 3D printing:

• British Museum Exhibits  on Sketchfab
• The New York Metropolitan Museum of Art  on Thingiverse
• Smithsonian

Types of Assignments Copyright © 2023 by The University of Queensland is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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K-12 Resources By Teachers, For Teachers Provided by the K-12 Teachers Alliance

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Modeling Teaching Strategy Examples for English Language Learners

Michael coleman.

• August 12, 2020

What is Modeling?

Modeling is an extremely useful teaching tool that should be used as often as possible. Modeling is a teaching strategy where a teacher explicitly shows the students how to complete an activity or assignment before the students begin. Modeling is also an excellent class management technique. Teachers who model what needs to be done will have much fewer questions or students who do not know how to do the assignment.

Modeling provides a clear picture in a student’s mind as to how to handle the task at hand. Creating a picture in a student’s mind will give the student confidence in how to complete the assignment. This type of guidance shows what the teacher expects and gets the students off on the right foot. There is nothing more frustrating for both a teacher and a student when directions have been given but students still do not know how or where to begin. Modeling will eliminate these frustrations and attribute to excellent classroom management.

Why is Modeling Beneficial for English Language Learners?

All students will benefit from modeling, but for English language learners modeling is even more advantageous. ELLs have a number of barriers to overcome in the classroom. Speaking, reading, writing, and listening require so many skills, and complete fluency in those areas takes many years. Therefore, finding success in class can be stressful for ELLs. Teachers must help ELLs find success just like their native English-speaking classmates. Every bit of success an ELL experiences will encourage them to continue to learn and grow in the class.

Academic language is often the last type of language that ELLs master which is why modeling can be so beneficial for them. Teachers who hand out an assignment and expect students to get to work and find success without much guidance are specifically failing ELLs. Spoken directions can be confusing and overwhelming for them. On the other hand, if a teacher models the directions and gives examples, ELLs will experience less anxiety and confusion when working on the assignment.

Modeling Teaching Strategy Examples

One example of teachers using modeling to help ELLs in their classes is as easy as doing the first one together. The teacher can show step by step how to do it and then the students can begin working on the second by themselves. By doing the first problem together, a teacher can address what to do and what not to do. Immediately, students will know and can actually see the steps they should take.

Another way to model for ELLs is by using cloze activities to guide reading and writing. Cloze reading is an instructional strategy where students fill in the blanks within a reading passage. Depending on the language level of an ELL, cloze activities can reduce confusion and help build sentence and language structures without overwhelming the student. By providing this type of guidance, the assignment models grammar and content vocabulary, which the student can then use throughout the rest of the task.

Modeling should also be used to show ELLs the daily routines of the class. Explicitly showing where to hang your things, get your notebook, and begin the morning assignments will set an ELL up for a day of success. Routines are so important for ELLs, especially for those in the early stages of language learning. Even if they cannot comprehend much English, they will remember the routines and find pride by completing them.

Additionally, visuals are an excellent modeling tool. Having an example assignment already completed to show the finished product will create a picture in students’ minds so that they know what they are working toward. Teachers can use these visuals to show what they are expecting from their students.

Finally, with many schools choosing to be completely online this fall, modeling is even more important and must be used. Providing video explanations for assignments will be much easier for ELLs to understand. Reading directions and attempting an assignment on their own can be very stressful. ELLs often do not have parents or other siblings who speak English that can assist them with an assignment while at home. Therefore, when teachers provide video explanations modeling how to do the task at hand, ELLs will be able to do it and do it well.

ELLs have to find success in class. This will build their confidence and in turn improve their language skills. Modeling is often a forgotten teaching method, but when used, teachers and students will find enjoyment and great accomplishment in their classes.

• #EnglishLanguageLearners , #ModelingTeachingStrategy

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5 Effective Modeling Strategies for English Learners

Providing English learners—and all students—with examples of how to do learning tasks is particularly useful during distance learning.

Despite the huge shifts in pedagogical practice caused by the move to online learning, some tried-and-true strategies, such as modeling for English learners, remain crucial. Since teachers often can’t intervene in real time these days, effective modeling—in which the teacher’s expectations for student performance are made explicit through an example—is a lifeline for English learners because of the clarity they provide.

In our experience observing K–12 classrooms—including classrooms with only English learners as well as classrooms that contain a mix of ELs and fluent English speakers—modeling is consistently underutilized despite being an easy, high-leverage strategy. It’s helpful to remember that providing effective models saves time in the end, since it both provides clear examples of expectations for a given assignment and reduces the verbiage a teacher needs to explain a task.

5 Types of Effective Models

Effective modeling can take many different forms. In all cases, modeling should clarify the expectations of the task without giving away the answer, and should remain available for students to access throughout the task. The following are examples of effective models.

1. Completing the first one in a set as an example:  This is the simplest form of modeling, yet we’ve found that it’s not used enough. Click here  to see an example. In any kind of exercise in which students are working through multiple examples of the same type of question or problem, it’s helpful to model one or two examples so students see exactly what is expected of them.

2. Providing explicit guidance on the expectations of the assignment through visual models:  Click here for a humanities example and here for a math one. These embedded models clearly show the teacher’s expectations for performance with visuals instead of many words, without giving away the answers.

3. Using language frames as models for conversational moves:  Providing sentence frames models the kinds of conversations students should be having. ELs can engage in conversations more fluidly when they can focus on what they want to express instead of how to express it. See a See Think Wonder activity with language frames and contrast it with a standard version of this same activity.

4. Demonstrating how to complete the steps of the task through video:  This video example by Megan Berdugo at Brooklyn International High School models how to solve an equation by showing students each step with an analogous problem. Students can rewatch it as many times as they want and pause where necessary to catch words and ideas they missed.

5. Chunking the steps of a complex process and using a corresponding template for students to complete:  ELs can easily get overwhelmed by models of a paragraph, essay, or solution when there is a lot of language to wade through and it’s unclear which part of the model corresponds to which part of the assignment. Breaking the model into smaller chunks, and providing space next to each chunk, enables students to focus on one aspect at a time, reducing the cognitive and linguistic loads. Click here for a writing example and here for a math example.

We’ve heard concerns that providing a model reduces the rigor of an assignment. We would counter that while demystifying a teacher’s expectations does make the task less difficult for a student, it in no way makes it less complex as long as the model cannot be copied. In fact, great models enable students to jump to the heart of the work instead of spending precious mental energy and time on figuring out what a teacher is asking them to do.

Effective modeling is arguably the most straightforward of scaffolds and requires the least amount of customization for individual students. And like many scaffolds, effective modeling helps all students—not just ELs. For any students who struggle, it provides crucial access that can make the difference between frustration and success.

Browse Course Material

Course info, instructors.

• Prof. Markus Buehler
• Prof. Jeffrey Grossman

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As Taught In

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Introduction to modeling and simulation, assignments.

This page contains suggested readings for the assignments in Part I, and the problem sets for Part II of this course. The assignments for Part I are not available. Note that Part I materials are as taught the previous year.

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Essential Financial Modelling

Core financial modelling skills that every analyst should know.

This course will transform your financial modelling skill set and confidence. You will learn to build a complete 3 statement financial model - from start to finish.

We will teach you the core financial model design and construction techniques every professional should know.

If you work in the corporate, business modelling, banking, financial advisory, government or infrastructure sectors, this course is for you.

The course is the result of 20 years of experience in modelling and knowledge built up teaching thousands of analysts how to build better financial models, in less time, making fewer errors.

The course covers foundation level core modelling skills as well as advanced financial topics.

In a few weeks, this course will equip you to deliver effective and well constructed financial models.

Your Instructor

Kenny has spent the last 20 years in financial modelling both in delivery of financial modelling assignments in training of financial modelling professionals. He has trained thousands of modellers from the world’s leading commercial and investment banks, top tier accounting firms, infrastructure funds and developers.

Course Curriculum

• Preview 1.1 Part 1: Welcome (1:17)
• Start 1.1 Part 2: Introducing the Financial Modelling Handbook
• Start 1.2 Setting up your excel environment (Windows) (1:48)
• Start 1.3 The importance of keyboard shortcuts (1:52)
• Start 1.4 Introducing our Ninja modelling tools (3:17)
• Start 1.5 Ninja modelling tools: Formatting macros (2:53)
• Start 1.6 Ninja Modelling tools: Productivity macros (7:22)
• Start 1.7 Ninja Modelling Tools: Review macros (5:21)
• Start 1.8 Three ground rules for course participation (0:47)
• Start 1.9 FAQs
• Start 2.1 Introduction to core modelling skills
• Start 2.2 What makes models hard to read (2:24)
• Start 2.3 What makes models easy to read (1:37)
• Start 2.4 Understanding model structure (3:57)
• Start 2.5 Model structure part I: Workbook level (3:59)
• Start 2.6 Model structure part II: Worksheet level (5:45)
• Start 2.7 Model structure part III: Calculation block level (6:06)
• Start 2.8 Model structure part IV: Calculation block components (2:13)
• Start 2.9 Links Part I: When to create, when to copy (2:04)
• Start 2.10 Link Part II: How to navigate using links (2:16)
• Start 2.11 Links Part III: Why to avoid linking to a link (5:04)
• Start 2.12 How to use placeholders (5:17)
• Start 2.13 Why mark imports and exports (3:30)
• Start 2.14 The best keystroke in excel: F11 quick chart (1:12)
• Start 2.15 How to model balances (5:42)
• Start 3.1 Why we build models - the business analysis lifecycle (2:43)
• Start 3.2 Introduction to the Solar investment case study (1:04)
• Start 3.3 Being aware of our biases (2:05)
• Start 4.1 The 5 types of timing flag (2:14)
• Start 4.2 How to model each of the 5 types of timing flag (9:58)
• Start 5.1 Overview (3:16)
• Start 5.2 Modelling retained cash and retained earnings (5:44)
• Start 6.1 Understanding solar power production metrics (4:17)
• Start 6.2 Solar revenue (12:10)
• Start 6.3 O&M costs (6:32)
• Start 6.4 Sign convention (6:43)
• Start 7.1 Depreciation of non-current assets (10:26)
• Start 8.1 100% equity financing (2:50)
• Start 8.2 Profit restricted dividends (5:53)
• Start 8.2 Profit restricted dividends - student Q&A (6:25)
• Start 8.3 Equity IRR (1:16)
• Start 9.1 Input sheet structure (4:39)
• Start 9.2 Output track sheet (6:31)
• Start 9.3 How to relocate inputs (5:24)
• Start 9.4 Manual vs automatic calculation? (1:48)
• Start 10.1 Mid course check-in & discussion (0:17)
• Start 11.1 Understanding debt sizing
• Start 11.2 Debt repayment profiles (7:18)
• Start 11.3 Senior debt interest payment (10:23)
• Start 11.4 Debt service cover ratio (5:12)
• Start 12.1 IRR for co-investor - assignment
• Start 12.2 IRR for co-investor - solution (6:05)
• Start 12.3 IRR for co-investor - advanced solution
• Start 13.1 Understanding cash tax vs accounting tax (9:13)
• Start 13.2 Tax depreciation and thin capitalisation (13:18)
• Start 13.3 Tax loss carry forwards (6:40)
• Start 14.1 Introduction and assignment (6:53)
• Start 14.2 Revolving credit facility solution (22:32)
• Start 14.3 Revolving credit facility wrap up (2:58)
• Start 15.1 Debt sizing (21:46)
• Start 15.2 Breaking circularity using VBA (13:59)
• Start 15.3 Sculpting debt repayments to meet min DSCR (5:29)
• Start 15.4 Working capital assignment (5:40)
• Start 15.5 Working capital solution
• Start 15.6 Escalation assignment (4:19)
• Start 15.7 Escalation solution
• Start 15.8 Change control - introducing the change log (10:16)
• Start 15.9 Automating saving a new output set (3:22)
• Start 15.10 Detailed variance analysis (9:11)
• Start 15.11 Time based inputs - profile DSCR (11:23)
• Start 15.12 Time based inputs - senior debt forward curve (10:50)
• Start 15.13 Starting a model from scratch & customising the timeline. (7:27)
• Start 15.14 Amortising / level debt service debt repayment profile (9:03)
• Start 15.15 What to do when your balance sheet doesn't balance (8:00)
• Start 15.16 New macro tools
• Start Course completion questionnaire (0:39)
• Start Introducing Openbox (4:17)
• Start Modelling on Excel for Mac (9:40)
• Start Customising macro keystrokes, formats, and calculation mode (6:47)
• Start Modelling clinic recordings (56:59)

Get started now!

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Mathematical Modeling

The textbook for the course was Topics in Mathematical Modeling by K. K. Tung

Homework Assignments

Takehome exams, textbook information.

• JSTOR: Online Textbook
• JSTOR (Off-Campus Access): Online Textbook

If you want a paper copy, there are currently new copies selling on Amazon.com starting at $41, and used copies starting at $30.

Course Policies

Course description, prerequisites, final project, exams and grading.

Project Information

• • Though individual projects are allowed, I would prefer for you to work together in groups of two or three.
• • Individual Project: 4–5 minute presentation
• • Two-Person Project: 8–10 minute presentation
• • Three-Person Project: 12–15 minute presentation
• • All projects must include a short paper describing a specific mathematical model or class of models, followed by either a mathematical analysis or computer simulation of your model. The paper should be 4-5 pages per person, including a description of any results obtained from computer experiments.

Project Topics

• • The Consortium for Mathematics and its Applications (COMAP) runs an annual Mathematical Contest in Modeling (MCM) for undergraduate math majors. One option for your project would be to choose one of the MCM questions from previous years and develop a model for the situation described in the problem. The COMAP website keeps a list of all the old problems stretching back to 1999, so you can browse through the questions and find one that interests you.
• • You could learn about and describe a known mathematical model, such as one of the models in the textbook that we didn't cover, or any other mathematical model that you can find in another textbook or journal article. Possibilities include models of Zipf's law, network scaling and models of the internet and social networks, discrete-time logistic population models and the onset of chaos, further HIV models, differential equation models in epidemiology, models of traffic flow, vibration models, models of animal locomotion, neural models, models of oscillating chemical reactions, etc.
• • A discussion of some large class of models or aspect of modelling that we did not discuss in depth, such as Markov chain models, Monte Carlo simulations, network models, game theory models, decision tree models, optimization of models, polynomial fitting, Runge-Kutta methods for solving differential equations, and so forth. Any such topic must include discussion of at least one specific model.

Looking for something?

Six Characteristics of a Model Assignment

How many times have you had a student submit an assignment with few sources, poorly written and several days late? Probably happens more times than not. There are six characteristics of a model assignment which will not only alleviate instructor frustration, but also strengthen student writing and time management skills.

• Create assignments which directly relate to accomplishing the course objective. A model assignment maintains a clear goal toward accomplishing a course objective. For adult online learners, course goals relate less to theory or original research and more to practical approaches for day-to-day application or career advancement.
• More details equals higher quality of student final product. Since adult online learners come from diverse backgrounds, do not assume students will understand the purpose of the assignment. Be prepared to tell students what you expect (e.g. word count, citation format, number of sources, etc.) and how it should be done (e.g. upload to Moodle versus email attachment).
• Give incremental due dates. Large comprehensive assignments due at the course finality leads to unfocused, or even plagiarized, writing. Break down a large assignment into several smaller assignments due sporadically throughout the term. In turn, students receive valuable feedback incrementally as they progress throughout the course.
• Allow students to brainstorm for topics. Allow students to brainstorm topics or share with other students using the Moodle Discussion Board form. Or consider offering students a choice among 3-4 essay questions, case scenarios, or case studies. By allowing student choice, students will find a greater connection in their writing which in turn will lead to better final submissions.
• Give examples. In addition to clear directions, students also appreciate a visual piece of the final product. If you decide to use another student’s work, be sure to ask permission to use from the student. Post model assignments on your Moodle course shell.
• Share student evaluation tools. Share rubrics, or other evaluation tool, early in the assignment rather than at the end so students may clarify expectations firsthand. Post rubrics or evaluation tools on your Moodle course shell so students may refer to it when necessary.

• Mastering Mathematical Modeling: Essential Topics and Problem-Solving Strategies

Important Topics and Strategies in Mathematical Modeling Assignments

Mathematical modeling is a fascinating field that bridges the gap between theoretical mathematics and real-world applications. Embarking on a journey through the realm of mathematical modeling opens doors to a world where abstract mathematical concepts meet real-world complexities. Whether you're a student venturing into this domain for the first time or seeking to refresh your knowledge, grasping the foundational topics and cultivating effective problem-solving strategies is paramount. In this blog, we will delve into the pivotal topics that serve as the bedrock for tackling mathematical modeling assignments. Moreover, we'll unravel insightful strategies that empower you to conquer these assignments with confidence and finesse, potentially seeking assistance with your mathematical modeling assignment if needed. By the end, you'll be equipped not only with a toolkit of mathematical principles but also with the tactics to wield them effectively in the realm of practical problem-solving.

Understanding the Basics of Mathematical Modeling

Before diving into the specifics of an assignment, it's crucial to grasp the fundamental concepts of mathematical modeling. Mathematical modeling involves creating mathematical representations of real-world situations to analyze, predict, and understand their behavior. This often involves translating real-world problems into mathematical equations or systems, which can then be analyzed using various mathematical techniques.

Mathematical Tools and Techniques

To tackle mathematical modeling assignments effectively, you need a solid understanding of the mathematical tools and techniques commonly used in the field. Some essential tools include:

• Differential Equations Differential equations are a cornerstone of mathematical modeling. They describe how quantities change in relation to one another. Familiarity with ordinary differential equations (ODEs) and partial differential equations (PDEs) is vital for modeling dynamic processes and phenomena.
• Optimization Methods Optimization is about finding the best solution among a set of possible options. Linear programming, nonlinear programming, and integer programming are techniques used to optimize various aspects of a model, such as maximizing profits or minimizing costs.
• Probability and Statistics Many real-world situations involve uncertainty. Probability and statistics help you quantify and analyze uncertainty, making them essential tools for accurate modeling. Concepts like probability distributions, regression analysis, and hypothesis testing are invaluable for making informed decisions based on data.

Data Collection and Analysis

A significant part of mathematical modeling involves gathering relevant data from the real world. Without accurate data, your models may not reflect reality accurately. Therefore, understanding data collection methods and being proficient in data analysis techniques is crucial.

Assumptions and Limitations

Every mathematical model is built upon certain assumptions, and it's important to be aware of these assumptions and their potential impact on the model's accuracy. Additionally, understanding the limitations of a model helps you interpret its results more effectively and make informed decisions based on those results.

Model Validation and Verification

Ensuring that your mathematical model is valid and accurate is a critical step in the modeling process. Model validation involves comparing the model's predictions with real-world observations, while verification focuses on confirming that the model's equations and algorithms are implemented correctly.

Sensitivity Analysis

Sensitivity analysis involves examining how variations in the model's parameters impact its results. By performing sensitivity analysis, you can identify which parameters have the most significant influence on the model's outcomes and gain insights into the model's behavior under different conditions.

Effective Problem-Solving Strategies for Mathematical Modeling Assignments

Understanding the problem is the foundational step in any mathematical modeling endeavor. It involves thoroughly comprehending the real-world situation that needs to be modeled. This encompasses identifying the key variables, parameters, and relationships that play a role in the problem. It's crucial to dissect the problem statement, discern its objectives, and ascertain any constraints that need to be considered.

By gaining a deep understanding of the problem, you ensure that your model captures the essence of the real-world scenario accurately. This understanding guides your choices in selecting the appropriate mathematical techniques and tools to tackle the problem effectively. Without a clear grasp of the problem's nuances, there's a risk of creating a model that fails to address the core issues, leading to erroneous results and conclusions.

Conceptualize the Model

Conceptualizing the model involves transforming the real-world problem into a structured mathematical framework. At this stage, you visualize how the various components of the problem can be represented using mathematical equations, functions, and relationships. This step requires translating real-world phenomena into mathematical abstractions.

Choosing the type of model—whether deterministic or stochastic, continuous or discrete—is a critical decision during the conceptualization phase. You define the system's components, interactions, and the flow of information or quantities. This step also involves establishing the equations that govern the behavior of the variables in the model.

Conceptualization demands creativity and a keen eye for simplifying complex scenarios without sacrificing essential details. A well-conceptualized model serves as the blueprint for the subsequent mathematical manipulation and analysis, setting the stage for a successful resolution of the problem.

Choose Appropriate Techniques

Choosing the right techniques is a crucial step in the process of mathematical modeling. It involves selecting the mathematical tools and methods that align with the nature of the problem you're addressing. The goal is to find the most suitable approach that allows you to accurately capture the essence of the real-world scenario in mathematical terms. This requires a deep understanding of the problem's characteristics, such as whether it involves continuous or discrete variables, deterministic or stochastic processes, and linear or nonlinear relationships. By carefully evaluating these factors, you can decide whether to use differential equations, optimization algorithms, statistical methods, or a combination of these tools. The art lies in striking a balance between mathematical rigor and practical relevance, ensuring that your chosen techniques effectively capture the dynamics of the problem at hand.

Implement and Solve

Once you've chosen the appropriate techniques, the next step is to implement and solve the mathematical model. This involves translating the conceptual framework into actual equations, algorithms, or computational procedures. Depending on the complexity of the problem, you might use programming languages like Python or specialized software like MATLAB. During implementation, attention to detail is vital; even a small error in translating the model can lead to significant discrepancies in the results. After implementation, you embark on the process of solving the equations or running simulations to generate results. This step often requires iterative processes, fine-tuning parameters, and refining algorithms to converge towards accurate solutions. Implementing and solving a mathematical model is not merely a technical exercise—it's an intellectual journey where you blend theoretical understanding with practical execution, culminating in insights that bridge theoretical concepts with real-world applications.

Interpret Results

Interpreting the results of a mathematical modeling assignment is a crucial step that bridges the gap between abstract calculations and real-world insights. Once you've formulated and solved your model, you're presented with a plethora of numerical outcomes. However, these numbers hold little value until they're contextualized within the framework of the original problem. Interpretation involves deciphering what these results mean in practical terms.

Consider a scenario where you've developed a model to predict the spread of a contagious disease. The results might show a trajectory of infections over time. Interpreting these outcomes requires understanding the implications for public health policies, resource allocation, and potential intervention strategies. Are the infection rates in line with projections? Do they highlight the effectiveness of certain measures? This step is where your analytical prowess merges with your domain knowledge to extract actionable insights.

Validate and Verify

In the realm of mathematical modeling, the terms "validate" and "verify" hold distinct but interconnected meanings. They ensure that your model is both sound in theory and accurate in practice.

Validation involves assessing whether your model accurately represents the real-world system it aims to emulate. This is accomplished by comparing the model's predictions against empirical data or established theories. Returning to our disease spread example, you would compare your model's projected infection rates with actual recorded cases. If your model aligns closely with observed data, it suggests a higher degree of validation.

Verification, on the other hand, confirms that your model's equations and algorithms are correctly implemented. This verification is an internal process to ensure that mathematical operations are executed accurately. For instance, if you're using software to solve differential equations, verification would involve checking if your code faithfully represents the mathematical formulations.

In essence, validation ensures your model represents reality, while verification confirms that your model's computational aspects are error-free. Together, these steps create a robust modeling framework that enhances your ability to make informed decisions and predictions based on mathematical insights.

Communicate Clearly

Effective communication is the bridge that connects the intricate world of mathematical modeling with the broader audience. When working on assignments, clarity in conveying your methodology, findings, and conclusions is essential.

In 200 words, communication involves articulating your approach in a structured manner. Begin by introducing the problem and the modeling technique you've chosen. Clearly define your variables, parameters, and assumptions. Present your equations and algorithms concisely, avoiding jargon or overly complex language. Visual aids like graphs, charts, and diagrams can significantly enhance understanding. Use labels, legends, and annotations to guide the reader through the visuals.

Remember, your audience might not possess the same level of mathematical expertise, so strive for simplicity without sacrificing accuracy. State the significance of your findings in the context of the real-world problem and discuss any limitations or uncertainties. Conclude by summarizing your main results and insights.

Engage in meticulous proofreading to eliminate errors that might cloud your message. A well-structured, clear, and concise communication not only demonstrates your mastery of the subject but also ensures that your audience comprehends and appreciates the significance of your mathematical modeling endeavors.

In conclusion, mathematical modeling assignments require a combination of solid theoretical knowledge and practical problem-solving skills. By understanding the basics of mathematical modeling, mastering essential mathematical tools, collecting and analyzing data, and employing effective strategies, you can confidently tackle assignments in this exciting field. Remember, practice and persistence are key to becoming a proficient mathematical modeler which in turn would help you ace your mathematical modeling assignment.

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Mastering mathematical modeling: essential topics and problem-solving strategies submit your assignment, attached files.

Key Features

Model variants, performance, experiments and results, comparisons, usage examples, citations and acknowledgements.

• SAM (Segment Anything Model)
• MobileSAM (Mobile Segment Anything Model)
• FastSAM (Fast Segment Anything Model)
• YOLO-NAS (Neural Architecture Search)
• RT-DETR (Realtime Detection Transformer)
• YOLO-World (Real-Time Open-Vocabulary Object Detection)
• NEW 🚀 Solutions
• Integrations

YOLOv10: Real-Time End-to-End Object Detection

YOLOv10, built on the Ultralytics Python package by researchers at Tsinghua University , introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.

Real-time object detection aims to accurately predict object categories and positions in images with low latency. The YOLO series has been at the forefront of this research due to its balance between performance and efficiency. However, reliance on NMS and architectural inefficiencies have hindered optimal performance. YOLOv10 addresses these issues by introducing consistent dual assignments for NMS-free training and a holistic efficiency-accuracy driven model design strategy.

Architecture

The architecture of YOLOv10 builds upon the strengths of previous YOLO models while introducing several key innovations. The model architecture consists of the following components:

• Backbone : Responsible for feature extraction, the backbone in YOLOv10 uses an enhanced version of CSPNet (Cross Stage Partial Network) to improve gradient flow and reduce computational redundancy.
• Neck : The neck is designed to aggregate features from different scales and passes them to the head. It includes PAN (Path Aggregation Network) layers for effective multiscale feature fusion.
• One-to-Many Head : Generates multiple predictions per object during training to provide rich supervisory signals and improve learning accuracy.
• One-to-One Head : Generates a single best prediction per object during inference to eliminate the need for NMS, thereby reducing latency and improving efficiency.
• NMS-Free Training : Utilizes consistent dual assignments to eliminate the need for NMS, reducing inference latency.
• Holistic Model Design : Comprehensive optimization of various components from both efficiency and accuracy perspectives, including lightweight classification heads, spatial-channel decoupled down sampling, and rank-guided block design.
• Enhanced Model Capabilities : Incorporates large-kernel convolutions and partial self-attention modules to improve performance without significant computational cost.

YOLOv10 comes in various model scales to cater to different application needs:

• YOLOv10-N : Nano version for extremely resource-constrained environments.
• YOLOv10-S : Small version balancing speed and accuracy.
• YOLOv10-M : Medium version for general-purpose use.
• YOLOv10-B : Balanced version with increased width for higher accuracy.
• YOLOv10-L : Large version for higher accuracy at the cost of increased computational resources.
• YOLOv10-X : Extra-large version for maximum accuracy and performance.

YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in terms of accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance.

Latency measured with TensorRT FP16 on T4 GPU.

Methodology

Consistent dual assignments for nms-free training.

YOLOv10 employs dual label assignments, combining one-to-many and one-to-one strategies during training to ensure rich supervision and efficient end-to-end deployment. The consistent matching metric aligns the supervision between both strategies, enhancing the quality of predictions during inference.

Holistic Efficiency-Accuracy Driven Model Design

Efficiency enhancements.

• Lightweight Classification Head : Reduces the computational overhead of the classification head by using depth-wise separable convolutions.
• Spatial-Channel Decoupled Down sampling : Decouples spatial reduction and channel modulation to minimize information loss and computational cost.
• Rank-Guided Block Design : Adapts block design based on intrinsic stage redundancy, ensuring optimal parameter utilization.

Accuracy Enhancements

• Large-Kernel Convolution : Enlarges the receptive field to enhance feature extraction capability.
• Partial Self-Attention (PSA) : Incorporates self-attention modules to improve global representation learning with minimal overhead.

YOLOv10 has been extensively tested on standard benchmarks like COCO, demonstrating superior performance and efficiency. The model achieves state-of-the-art results across different variants, showcasing significant improvements in latency and accuracy compared to previous versions and other contemporary detectors.

Compared to other state-of-the-art detectors:

• YOLOv10-S / X are 1.8× / 1.3× faster than RT-DETR-R18 / R101 with similar accuracy
• YOLOv10-B has 25% fewer parameters and 46% lower latency than YOLOv9-C at same accuracy
• YOLOv10-L / X outperform YOLOv8-L / X by 0.3 AP / 0.5 AP with 1.8× / 2.3× fewer parameters

Here is a detailed comparison of YOLOv10 variants with other state-of-the-art models:

Coming Soon

The Ultralytics team is actively working on officially integrating the YOLOv10 models into the ultralytics package. Once the integration is complete, the usage examples shown below will be fully functional. Please stay tuned by following our social media and GitHub repository for the latest updates on YOLOv10 integration. We appreciate your patience and excitement! 🚀

For predicting new images with YOLOv10:

For training YOLOv10 on a custom dataset:

YOLOv10 sets a new standard in real-time object detection by addressing the shortcomings of previous YOLO versions and incorporating innovative design strategies. Its ability to deliver high accuracy with low computational cost makes it an ideal choice for a wide range of real-world applications.

We would like to acknowledge the YOLOv10 authors from Tsinghua University for their extensive research and significant contributions to the Ultralytics framework:

For detailed implementation, architectural innovations, and experimental results, please refer to the YOLOv10 research paper and GitHub repository by the Tsinghua University team.

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• Published: 06 June 2024

Learning efficient backprojections across cortical hierarchies in real time

• Kevin Max   ORCID: orcid.org/0000-0002-9433-2190 1 ,
• Laura Kriener   ORCID: orcid.org/0000-0001-5275-9199 1 ,
• Garibaldi Pineda García   ORCID: orcid.org/0000-0002-6550-6016 2 ,
• Thomas Nowotny   ORCID: orcid.org/0000-0002-4451-915X 2 ,
• Ismael Jaras   ORCID: orcid.org/0000-0001-6856-2075 1 ,
• Walter Senn 1 &
• Mihai A. Petrovici   ORCID: orcid.org/0000-0003-2632-0427 1  

Nature Machine Intelligence ( 2024 ) Cite this article

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• Learning algorithms
• Machine learning
• Network models

A preprint version of the article is available at arXiv.

Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas. In deep learning, a known solution is error backpropagation, which requires biologically implausible weight transport from feed-forwards to feedback paths. We introduce phaseless alignment learning, a bio-plausible method to learn efficient feedback weights in layered cortical hierarchies. This is achieved by exploiting the noise naturally found in biophysical systems as an additional carrier of information. In our dynamical system, all weights are learned simultaneously with always-on plasticity and using only information locally available to the synapses. Our method is completely phase-free (no forwards and backwards passes or phased learning) and allows for efficient error propagation across multi-layer cortical hierarchies, while maintaining biologically plausible signal transport and learning. Our method is applicable to a wide class of models and improves on previously known biologically plausible ways of credit assignment: compared to random synaptic feedback, it can solve complex tasks with fewer neurons and learn more useful latent representations. We demonstrate this on various classification tasks using a cortical microcircuit model with prospective coding.

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Inferring neural activity before plasticity as a foundation for learning beyond backpropagation

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Introducing principles of synaptic integration in the optimization of deep neural networks

Data availability.

The datasets analysed during the current study are publicly available in the following repositories: https://github.com/lkriener/yin_yang_data_set (Yin-Yang dataset 40 ), http://yann.lecun.com/exdb/mnist/ (MNIST dataset 71 ) and https://www.cs.toronto.edu/~kriz/cifar.html (CIFAR-10, ref. 72 ).

Code availability

The simulations used custom code written in Python using numpy (v.1.26.2) and PyTorch (v.2.0.1+cu117). Some simulations were made using a custom module for the GeNN simulation suite. All code is made available under https://zenodo.org/records/10405083 (ref. 73 ).

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Acknowledgements

We thank J. Jordan, A. Meulemans and J. Sacramento for valuable discussions. We gratefully acknowledge funding from the European Union under grant agreement nos. 604102, 720270, 785907 and 945539 (Human Brain Project) and the Manfred Stärk Foundation. Additionally, our work has greatly benefited from access to the Fenix Infrastructure resources, which are partially funded from the European Union’s Horizon 2020 Research and Innovation programme through the ICEI project under the grant agreement no. 800858. This includes access to Piz Daint at the Swiss National Supercomputing Centre, Switzerland. Further calculations were performed on UBELIX, the High Performance Computing cluster at the University of Bern.

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K.M. derived, with contributions by L.K. and M.A.P., the PAL algorithm. K.M. and L.K. adapted the dendritic microcircuit model to include PAL for learning the feedback weights. G.P.G. and T.N. developed a dendritic microcircuit module for the GeNN simulator. L.K. added the latent equilibrium and PAL mechanisms to the module. K.M. and L.K. performed the simulation experiments. I.J. and K.M. worked on scaling the algorithm to a larger benchmark during the revision process. The paper was mainly written by K.M., aided by L.K. and M.A.P. M.A.P. and W.S. provided supervision and funding to this project.

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Effectiveness of digital and analog learning methods for learning anatomical structures in physiotherapy education

• Larissa Pagels   ORCID: orcid.org/0000-0003-3850-9454 1 ,
• Robert-Christopher Eschke   ORCID: orcid.org/0000-0002-1698-3334 1 &
• Kerstin Luedtke   ORCID: orcid.org/0000-0002-7308-5469 1  

BMC Medical Education volume  24 , Article number:  500 ( 2024 ) Cite this article

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According to the German Physiotherapy Education and Qualification Regulations, teaching of anatomical structures is one of the fundamental subjects of physiotherapy education. Besides exhibits and models, anatomy atlases are usually used as teaching and learning tools. These are available in both analog form such as printed books or in digital form as a mobile application. Furthermore, the use of digital teaching and learning tools is steadily increasing within the education of health professionals.

To assess the efficacy of a digital educational tool in contrast to an analog anatomical atlas in acquiring knowledge about anatomical structures.

Material and method

The data collection took place in the context of an anatomy tutorial for students of the bachelor’s degree program in physiotherapy. In a cross-over design, the students completed two learning assignments, each, with different learning materials provided, either with an anatomy app on a tablet or with an anatomy atlas as a book. The tests to assess the newly acquired knowledge immediately after the task, consisted of questions about the anatomical structures of the knee as well as the shoulder. In addition, the students’ satisfaction with the learning materials provided was surveyed using a questionnaire. The survey assessed their satisfaction, their assessment of learning success, and their affinity to digital learning materials. This was done using a 5-point Likert scale and a free-text field. The data was analyzed descriptively, and group differences were calculated using a t-tests.

Thirty students participated. The group comparison showed a significantly better outcome for the group that prepared with the analog anatomy atlas for the questions on the knee than the comparison group that used the anatomy app (t(28) = 2.6; p  = 0.007). For the questions concerning the shoulder, there was no significant difference between the digital and analog groups (t(28) = 1.14; p  = 0.26). The questionnaire revealed that satisfaction with the analog anatomy atlas was significantly higher than with the anatomy app. A total of 93.34% rated their experience with the analog learning tool at least “somewhat satisfied”. In contrast, 72.67% of students partially or fully agreed that they “enjoyed learning with digital learning tools”.

Learning anatomical structures with the Human Anatomy Atlas 2023 + app did not show a clear advantage when compared to an anatomy book in these two cohorts of physiotherapy students. The results of the questionnaire also showed greater satisfaction with the analog anatomy atlas than with the anatomy app, whereas most students stated that they frequently use digital learning tools, including some for anatomical structures. Satisfaction with the learning tool seems to play a central role in their effectiveness. In addition, sufficient time must be provided for users to familiarize themselves with the user interface of digital applications to use them effectively.

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Introduction

The desire for digital teaching is growing in all areas of university teaching. Due to the Sars-CoV-2 pandemic, teaching has experienced a major boost in digitalization since March 2020 [ 1 ]. A substantial number of digital teaching and learning tools are already available for this purpose. Especially in the of basic education in health sciences, the use of various teaching and learning tools can promote student motivation [ 2 ].

As part of the “HySkiLabs project - Teaching and learning health in hybrid skills labs”, education in the health sciences at the University of Luebeck is being enriched with digital teaching and learning tools. The project not only aims to transfer classroom teaching approaches to digital and hybrid teaching-learning environments, but also to systematically investigate their effectiveness. Previous surveys have shown a positive attitude of students towards digital teaching [ 3 ].

Over the last few years, the availability of digital learning tools has also increased considerably. There are various softwares and apps that students can use to organize their time in self-study or that are used as complementary teaching methods in anatomy lectures. An early study by Keedy et al. (2011) compared a 3D digital application to a 2D form for learning the anatomy of the liver and gall bladder by medical students [ 1 ]. No significant difference between the two visually different teaching methods was found for the knowledge of anatomical structures at the end of their study. Nevertheless, students’ satisfaction with the 3D digital application was very high. Two years later, Noguera et al. (2013) analyzed the effect of a digital 3D anatomy app in comparison to traditional teaching (lectures) in a physiotherapy degree program [ 2 ]. They found significantly better results in musculoskeletal anatomical knowledge among those students who used the digital anatomy app. More recently, Browne et al. (2019) analyzed the effect of online quizzes to learn anatomical structures complementing traditional learning in laboratory sessions (with wet and dry specimen, plastic models, histological slides etc.) and lectures [ 3 ]. Questions using images of anatomical structures and multiple-choice questions were provided in the online quizzes that were subsequently completed by students during self-study periods. The experiences of the students were evaluated and indicated a high level of engagement and satisfaction with the supplementary online material. Another study from 2014 used online discussion forums as an addition to their traditional learning (laboratory sessions and lectures) as an option for the students to interact and help each other in the learning process [ 4 ]. This digital learning method showed good effects on the students grades at the end of the module. In 2023, a study used Kahoot! quizzes to promote the learning of anatomical structures with a game-based learning method [ 5 ]. The quizzes contained questions about anatomical structures with four response options and were presented at the end of each lecture. An open-book technique was used, giving the students only 20s to answer the questions. A significant increase of short-term knowledge retention and an increase in the frequencies of correctly answered responses was found, compared to the traditional teaching method (lectures without Kahoot! quizzes). Additionally, all students perceived that the use of the interactive quiz improved their anatomy short-term knowledge retention.

Innovative computer-based learning tools can improve the learning of the complex spatial relationships of the musculoskeletal system and facilitate the transfer of anatomical knowledge to patients [ 5 ]. Inaccurate identification of anatomical structures is a common source of error in the assessment and treatment of musculoskeletal conditions, therefore, accurate learning of these, is essential for clinical practice [ 5 ]. From an educational perspective, interactive learning with 3D visualizations also offers several potential advantages over traditional methods of teaching anatomy: (1) a directly recognizable visualization of anatomical structures, (2) a reduction in cognitive load as students do not need to build their own mental visualization of the model, (3) many different anatomical perspectives and the ability to move the model interactively, and (4) the ability to incorporate 3D models obtained from live human imaging datasets − 2D drawings of anatomical structures are potentially inaccurate [ 5 , 6 ].

In this study, a digital 3D anatomy atlas was used to promote the short-term learning retention of physiotherapy students. To create a comparison between an analog and digital learning tool in this study, the app Human Anatomy Atlas 2023 + by Visible Body® (further referred to as “digital anatomy app”) was chosen.

There are currently no studies to indicate how effective this digital teaching and learning tool (digital anatomy app) is compared to traditional methods (analog anatomy atlas), hence, this study investigated the effect of using the Human Anatomy Atlas 2023 +  on physiotherapy students’ learning of anatomical structures compared to learning with the Prometheus Atlas of Anatomy (further referred to as “analog anatomy atlas”) .

The study was designed as an empirical cross-sectional study. The data collection took place in the context of a tutorial in which students were able to intensively study anatomical structures of the musculoskeletal system and peripheral nervous system. For this purpose, they were using work assignments, as well as teaching and learning tools provided by the supervisor.

The students were randomized into two groups (“digital/analog” or “analog/digital” depending on the order of learning tools that were provided) and allocated by one of the supervisors of the tutorial in two different rooms before the beginning of the study. The groups attended the tutorials in these two different rooms and were both given the same tasks but with different teaching and learning materials (Fig.  1 ).

Study design

Participants

All students of the bachelor’s degree program in physiotherapy at the University of Luebeck were invited to participate in the study. At the University of Luebeck the anatomy module is taught as face-to-face lectures, practice sessions in the dissection room and 50% self-study time. In the latter, the students deepen their knowledge independently - typically this is done with the help of anatomy atlases. These can be analog (2D) and digital (2D or 3D). As a rule, the collective work “Prometheus- Atlas of Anatomy” [ 4 ] serves as an analog anatomy atlas. Previous knowledge of anatomical structures was mandatory for the participation in this study, but it had to be assumed that the knowledge was rather heterogenous due to different levels of studying of the participants. The students were informed about the data collection at the beginning of the tutorial and written consent was obtained. Participation was voluntary and had no influence on the tutorial procedure, further study program or examination results.

Application used in this study

There are several applications to learn anatomical structures with different learning modes. Some show theoretical descriptions, as well as drawings (2D) of anatomical structures, and additional skill related content as placing of ultrasound probes or manual palpation techniques (Ecofisio app; [ 6 , 7 ]). Other applications use vision-based augmented reality to display anatomical structures on human models [ 8 ] or in the room, with the option to move around the augmented reality simulated anatomical structure [ 8 , 9 ]. In addition to augmented reality 3D visualizations of anatomical structures, there are also applications that use three-dimensional images to display their content interactively [ 2 , 9 ].

The digital anatomy app used for the purpose of this study (Human Anatomy Atlas 2023+), provides various options to learn anatomical structures, and physiological processes using 3D models (by option as augmented reality simulation). The learning content is presented as a 3D model, which is interactive and can be used individually by the students. Thereby, various information on the anatomical structures and common pathologies can be accessed and learned. Additionally, short videoclips of functional anatomy (e.g. showing the muscles that are required to bend the knee while an animated skeleton is bending the knee), or rather complex functions as swallowing food, are part of the content of the app. The app does not provide options for self-testing of knowledge. This app was chosen after screening different options as it is already known by some of the students and the teaching staff and it offers interactive 3D models that have been proven to facilitate knowledge gain and satisfaction of the students when learning anatomical structures [ 2 , 8 ]. Next to being the most practicable option (as it needs time to familiarize with the interface of new applications) this app provides all functions needed to operate with the work assignments in this study. The costs of the app were covered by the “Stiftung Innovation in der Hochschullehre” as part of the HySkiLabs project.

Material and procedure

Work assignments were prepared by the supervisor of the tutorial. These contained questions about structures of the knee (ligaments, bone and joint structures) including surrounding muscles and their innervation (assignment 1). Assignment 2 focused on the shoulder joint. The supervisor was a physiotherapist with experience in teaching and good knowledge of anatomical structures. Both work assignments were double-checked by faculty members of the physiotherapy degree program of the University of Luebeck for comprehensibility.

The knowledge of the students after each learning session, was assessed via written tests that contained open ended questions about the previously repeated learning content (e.g. “name all the ligaments of the knee joint and their special features.“). The number of points to be achieved were displayed next to each question, so that the students knew about the expected scope of the answers.

In the group “analog/digital”, each participant received an analog anatomy atlas (Prometheus), while in the group “digital/analog”, each participant received the digital anatomy app on a tablet device (Human Anatomy Atlas 2023+). All students were given an initial 45-minute work assignment, which was identical in both groups and related to structures of the knee joint. A supervisor was available in each room to answer questions.

to familiarize themselves with their learning tool. Merely a verbal suggestion was given to the users of the anatomy app to use the search function of the app. After the first assignment, the participants completed the first test (maximum score 41 points) on the teaching content. During the test, no books or apps were allowed. Afterwards, the teaching and learning materials were exchanged in the rooms and the participants thus received the respective teaching and learning tool. With the new teaching and learning tool, the participants worked on another 45-minute assignment (on structures of the shoulder joint) and completed the subsequent test (maximum score 47 points).

Subsequently, the students filled out a questionnaire in which their name, age, gender and satisfaction with the teaching/learning tool offered (0 = not at all satisfied − 5 = very satisfied) were asked. The teaching/learning tool used privately by the students (free response option) and the desire for similar teaching units as exam preparation (0 = not at all − 5 = absolutely) on a 5-point Likert scale were also part of the questionnaire.

In addition, the following sub-questions were formulated for secondary analyses and assessed as a survey by students after the completion of the tasks:

How satisfied are students with the analog or digital teaching and learning tools measured on a 5-point Likert scale?

How do students rate their learning success in relation to the teaching and learning tools available on a 5-point Likert scale?

Are the teaching and learning tools offered known and have they already been used by the students (open ended question)?

Data analysis

The collected data were tabulated and analyzed using Stata (Student Version BE 17, Mac).

The null hypothesis for the analysis was:

H0 = there is no difference between the group using an analog anatomy atlas and the group using a digital anatomy app.

H1 = the respective group that learns with the digital anatomy app shows better results in the tests.

Socio-demographic data, answers from the questionnaire and the evaluation of the work assignments were analyzed descriptively with regard to frequencies (mode, median, mean) as well as dispersion measures ((interquartile) range, standard deviation) and shape measures (kurtosis, skewness) for the groups “analog/digital” and “digital/analog”.

Normal distribution of the data was tested using Shapiro-Wilk tests and group differences were calculated using t-tests.

The assessment of the group differences took place on the basis of the calculated Cohen’s d. Thus, the effect size of the use of digital vs. analog teaching and learning aids (here: anatomy atlases) was determined.

Thirty students from the semesters 2–8 of the physiotherapy degree program of the University of Luebeck participated in the study. The demographic analysis revealed an asymmetric data set for the variable semester in the analog/digital group and the variable age in the digital/analog group. The detailed results can be found in Table  1 . The majority of participants identified as female ( n  = 25; 83.3%). The groups analog/digital and digital/analog differed significantly in the distribution of male and female participants (t(28)=-2.43; p  = 0.01).

The results of test A (knee) unveiled a significant group difference (t(28) = 2.6; p  = 0.01) and a Cohen’s d of 0.95 (Fig.  2 ). with a higher score for the group that completed the task using an analog anatomy atlas. No significant group difference was found for test B (shoulder) (t(28) = 1.14; p  = 0.26). In that analysis the effect size was a Cohen’s d of 0.42 (Fig.  3 ; Table  2 ).

The evaluation of the questionnaire (Table  3 ) showed that satisfaction with the analog anatomy atlas is significantly higher than with the anatomy app. In the question about the analog learning tool 93.34% selected “somewhat satisfied” to “very satisfied”. On the other hand, 43.33% of the participants were “somewhat dissatisfied” with the digital anatomy atlas offered. In contrast, 72.67% of the students partially or fully agreed that they generally “enjoy learning with digital learning tools”.

Twenty of the students stated that they learn privately with the analog anatomy atlas used in the study (Table  4 ). In addition, the students mainly use the notes from the anatomy lectures ( n  = 12), and the lecture material ( n  = 10). Apps and software for learning anatomical structures, on the other hand, were mentioned less frequently. Only 10 of the students stated that they used additional software (Visible Body, Anvil, etc.) for independent learning of anatomical structures. It was frequently mentioned that the Prometheus atlas was used digitally and recordings from the lectures and other teaching videos were used.

Boxplot of the results of test A

This study analyzed the effect of learning anatomical structures with a digital anatomy app in comparison to the use of an analog anatomy atlas in the context of a physiotherapy students’ tutorial. The test results showed that the group which prepared with the analog anatomy atlas for the first test (A; knee) performed significantly better than the digital group. This could not be confirmed with the second test (B; shoulder). Hence, the results of this study about the effect of the digital anatomy app on knowledge gain is ambivalent.

Two-thirds of the participants ( n  = 20) reported that they used the analog Prometheus Anatomy Atlas for studying at home and expressed satisfaction with it as a learning tool during the tutorial. Interestingly, the questionnaire also revealed that the students enjoy working with digital learning tools, but not with the one they used during the study. This might explain the difference in the first test results because it insinuates that the students had a better learning experience with the familiar learning tool. This can be supported with the results of a study from 2016, that was able to show that familiarities improve the acquisition of new knowledge. This can also be supported by the fact that the app can be used more effectively if the user interface is known beforehand and operation are clear because less working memory is devoted to understand the interface [ 10 , 11 ].

Research has shown that students’ dissatisfaction with a learning tool plays a role in its effectiveness and learning success [ 2 ]. As the participants in this study were not satisfied with the digital anatomy app provided (Table  2 ), this presents a valid explanation for the poor results of the digital learning outcomes. Moreover, many students of this sample still prefer to use analog learning materials (e.g. index cards, lecture notes, Prometheus atlas) or combine both by supplementing their analog learning materials with information from the internet (e.g. DocCheck, learning videos), further explaining the results.

The main functions of the digital anatomy app used in this study are of interactive nature and it is assumed, that active learning took place when students used the app. Interactive learning has been shown to lead to greater learning progress [ 12 ]. There is evidence for a significant better knowledge gain and student satisfaction when learning anatomical structures with mobile applications compared to traditional learning (2D images, textbook learning) [ 2 , 6 , 7 , 8 ]. The fact that the test results did not show a superiority of the digital learning tool compared to the analog anatomy atlas could be, amongst other things, that the students had too little time to learn the body regions tested digitally. But according to Noguera et al. (2013), it is necessary that students have enough time to internalize anatomical structures learned in 2 dimensions and to convert them into a 3D understanding as well [ 2 ].

Previous studies have found divergent results when comparing digital vs. analog learning tools for learning anatomical structures. Keedy et al. already showed in 2011 that there was no significant difference in learning anatomical structures (liver and gall bladder) with a 3D digital application or a 2D application [ 1 ]. In contrast to the present study, the students’ satisfaction with the 3D digital application was very high [ 1 ]. One reason might be that in 2011 there were fewer alternative digital learning tools available and the comparison between several digital learning tools was therefore low.

Contradicting tothe present results Noguera et al. (2013) found a significantly better result in musculoskeletal anatomical knowledge among physiotherapy students who used a digital (3D) anatomy app than among students who received traditional teaching [ 2 ]. This difference may be attributed to their utilization of a different, more rudimentary application, characterized by a reduced set of functions compared to the alternative. Presumably, this helped students to familiarize themselves with the digital application more quickly leading to better learning effects. Furthermore, anatomical knowledge was tested witha multiple-choice questionnaire, which means that the mere probability of correct answers is higher than in this study.

Limitations

In this current study, only the students’ ability to acquire knowledge in a short time and to recall it immediately, is tested. No conclusion can be drawn about how well the students can recall the knowledge acquired after a longer period of time. Likewise, it is not possible to say how good the students’ knowledge was in advance of the tutorial, so that the learning gain through the work assignments cannot be precisely mapped. Since it was announced in advance of the tutorial that the test results would have no effect on the further course of studies, students might not have taken the test seriously. However, this effect would have been comparable in both groups.

The survey used in this study was only checked by faculty members for comprehensibility, relevance, expected acceptance of the students as well as feasibility. It has not been pilot-tested in the target population (physiotherapy students), therefore no conclusion can be drawn to its content validity.

Conclusions

This study highlights that the analog and familiar learning tools are superior if the user-friendliness and simplicity of the digital tool are not on a comparable level. Regarding the “HySkiLabs” framework project, it can be deduced from the results that the students enjoy working with digital learning tools, but a higher effectiveness of these tools could not be shown.

Further research should investigate, whether additional teaching and learning methods like discussion forums, or interactive quizzing situations might be more beneficial for knowledge retention of anatomical structures and enjoyment of learning than the mere tool itself [ 3 , 4 , 5 ].

Through digitalization, technical solutions are increasingly emerging with the potential to positively effect students’ motivation to learn and provide an effective learning environment [ 13 , 14 ].

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Pagels, L., Eschke, RC. & Luedtke, K. Effectiveness of digital and analog learning methods for learning anatomical structures in physiotherapy education. BMC Med Educ 24 , 500 (2024). https://doi.org/10.1186/s12909-024-05484-1

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导出模型有问题,local variable 'model' referenced before assignment #502

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OptLLM: Optimal Assignment of Queries to Large Language Models

• Liu, Yueyue
• Zhang, Hongyu
• Miao, Yuantian
• Le, Van-Hoang
• Li, Zhiqiang

Large Language Models (LLMs) have garnered considerable attention owing to their remarkable capabilities, leading to an increasing number of companies offering LLMs as services. Different LLMs achieve different performance at different costs. A challenge for users lies in choosing the LLMs that best fit their needs, balancing cost and performance. In this paper, we propose a framework for addressing the cost-effective query allocation problem for LLMs. Given a set of input queries and candidate LLMs, our framework, named OptLLM, provides users with a range of optimal solutions to choose from, aligning with their budget constraints and performance preferences, including options for maximizing accuracy and minimizing cost. OptLLM predicts the performance of candidate LLMs on each query using a multi-label classification model with uncertainty estimation and then iteratively generates a set of non-dominated solutions by destructing and reconstructing the current solution. To evaluate the effectiveness of OptLLM, we conduct extensive experiments on various types of tasks, including text classification, question answering, sentiment analysis, reasoning, and log parsing. Our experimental results demonstrate that OptLLM substantially reduces costs by 2.40% to 49.18% while achieving the same accuracy as the best LLM. Compared to other multi-objective optimization algorithms, OptLLM improves accuracy by 2.94% to 69.05% at the same cost or saves costs by 8.79% and 95.87% while maintaining the highest attainable accuracy.

• Computer Science - Software Engineering;
• Computer Science - Computation and Language;
• Computer Science - Machine Learning

The potential of AI technology has been percolating in the background for years. But when ChatGPT, the AI chatbot, began grabbing headlines in early 2023, it put generative AI in the spotlight. This guide is your go-to manual for generative AI, covering its benefits, limits, use cases, prospects and much more.

Ai hallucination.

• Ben Lutkevich, Site Editor

What are AI hallucinations?

An AI hallucination is when a large language model (LLM) generates false information.

LLMs are AI models that power chatbots, such as ChatGPT and Google Bard . Hallucinations can be deviations from external facts, contextual logic or both.

Hallucinations often appear plausible because LLMs are designed to produce fluent, coherent text . They occur because LLMs have no understanding of the underlying reality that language describes. LLMs use statistics to generate language that is grammatically and semantically correct within the context of the prompt.

However, hallucinations do not always appear plausible. Sometimes they can be clearly nonsensical. There is no clear way to determine the exact causes of hallucinations on a case-by-case basis.

Another term for an AI hallucination is a confabulation . Hallucinations are most associated with LLMs, but they can also appear in AI-generated video, images and audio .

This article is part of

What is generative AI? Everything you need to know

• Which also includes:
• 8 top generative AI tool categories for 2024
• Will AI replace jobs? 9 job types that might be affected
• 19 of the best large language models in 2024

Examples of AI hallucinations

One infamous example of an AI hallucination happened when Google's chatbot, Bard, made an untrue claim about the James Webb Space Telescope.

When prompted, "What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?" Bard responded with the claim that the James Webb Space Telescope took the very first pictures of an exoplanet outside this solar system. This information was false. The first images of an exoplanet were taken in 2004 according to NASA, and the James Webb Space Telescope was not launched until 2021.

Bard's answer sounded plausible, and was consistent with the prompt, but was proven false with some fact checking.

Another example was when Meta demoed Galactica, an LLM designed for science researchers and students. When asked to draft a paper about creating avatars, the model cited a fake paper about the topic from a real author working in a relevant area.

Types of AI hallucinations

Hallucinations can range from minor inconsistencies to completely fabricated or contradictory information.

There are several types of AI hallucinations, including the following:

• Prompt: "Write a description of a landscape in four-word sentences."
• Output: "The grass was green. The mountains were blue. The river was purple. The grass was brown."
• Prompt: "Write a birthday card for my niece."
• Output: "Happy anniversary, mom and dad!"
• Prompt: "Name three cities in the United States."
• Output: "New York, Los Angeles, Toronto."
• Prompt: "Describe London to me."
• Output: "London is a city in England. Cats need to be fed at least once a day."

Why do AI hallucinations happen?

There are many possible technical reasons for hallucinations in LLMs. While the inner workings of LLMs and the exact mechanisms that produce outputs are opaque, there are several general causes that researchers point to. Some of them include the following:

• Data quality. Hallucinations from data occur when there is bad information in the source content. LLMs rely on a large body of training data that data that can contain noise , errors, biases or inconsistencies. ChatGPT, for example, included Reddit in its training data .
• Generation method. Hallucinations can also occur from the training and generation methods -- even when the data set is consistent and reliable. For example, bias created by the model's previous generations could cause a hallucination. A false decoding from the transformer could also be the cause of hallucination. Models might also have a bias toward generic or specific words, which influences the information they generate and fabricate.
• Input context. If the input prompt is unclear, inconsistent or contradictory, hallucinations can arise. While data quality and training are out of the user's hands, input context is not. Users can hone their inputs to improve results.

Why are AI hallucinations a problem?

An immediate problem with AI hallucinations is that they significantly disturb user trust. As users begin to experience AI as more real, they might develop more inherent trust in them quickly and are more surprised when that trust is betrayed.

One challenge with framing these outputs as hallucinations is that it encourages anthropomorphism. Describing a false output from a language model as a hallucination anthropomorphizes the inanimate AI technology to some extent. AI systems, despite their function, are not conscious. They do not have their own perception of the world. Their output manipulates the users' perception and might be more aptly named a mirage -- something the user wants to believe isn't there, rather than a machine hallucination.

Another challenge of hallucinations is the newness of the phenomenon and large language models in general. Hallucinations and LLM outputs in general are designed to sound fluid and plausible. If someone is not prepared to read LLM outputs with a skeptical eye, they might believe the hallucination. Hallucinations can be dangerous due to their capacity to fool people. They could inadvertently spread misinformation , fabricate citations and references and even be weaponized in cyberattacks .

A third challenge of mitigating hallucination is that LLMs are often black box AI . It can be difficult or in many cases impossible to determine why the LLM generated the specific hallucination. There are limited ways to fix LLMs that produce these hallucinations because their training cuts off at a certain point. Going into the model to change the training data can use a lot of energy . AI infrastructure is expensive in general. It is often on the user -- not the proprietor of the LLM -- to watch for hallucinations.

Generative AI is just that -- generative . In some sense, generative AI makes everything up.

For more on generative AI, read the following articles:

Pros and cons of AI-generated content

How to prevent deepfakes in the era of generative AI

Generative AI challenges that businesses should consider

AI existential risk: Is AI a threat to humanity?

Generative AI landscape: Potential future trends

How to prevent AI hallucinations

There are several ways users can avoid or minimize the occurrence of hallucinations during LLM use , including the following:

• Limiting the possible outputs.
• Providing the model with relevant data sources.
• Giving the model a role to play. For example, "You are a writer for a technology website. Write an article about x, y and z."
• Filtering and ranking strategies. LLMs often have parameters that users can tune. One example is the temperature parameter, which controls output randomness. When the temperature is set higher, the outputs created by the language model are more random. TopK, which manages how the model deals with probabilities, is another example of a parameter that can be tuned.
• Multishot prompting. Provide several examples of the desired output format or context to help the model recognize patterns.

Hallucinations are considered an inherent feature of LLMs. There are ways that researchers and the people working on LLMs are trying to understand and mitigate hallucinations.

OpenAI proposed a strategy to reward AI models for each correct step in reasoning toward the correct answer instead of just rewarding the conclusion if correct. This approach is called process supervision and it aims to manipulate models into following a chain-of-thought approach that decomposes prompts into steps.

Other research proposed pointing two models at each other and instructing them to communicate until they arrive at an answer.

How can AI hallucinations be detected?

The most basic way to detect an AI hallucination is to carefully fact check the model's output. This can be difficult when dealing with unfamiliar, complex or dense material. Users can ask the model to self-evaluate and generate the probability that an answer is correct or highlight the parts of an answer that might be wrong, using that as a starting point for fact checking.

Users can also familiarize themselves with the model's sources of information to help them fact check. For example, ChatGPT's training data cuts off at 2021, so any answer generated that relies on detailed knowledge of the world past that point in time is worth double-checking.

History of hallucinations in AI

Google DeepMind researchers surfaced the term "IT hallucinations" in 2018 , which gained it some popularity. The term became more popular and tightly linked to AI with the rise of ChatGPT in late 2022, which made LLMs more accessible.

The term then appeared in 2000 in papers in Proceedings: Fourth IEEE International Conference on Automatic Face and Gesture Recognition. A 2022 report called "Survey of Hallucination in Natural Language Generation" describes the initial use of the term in computer vision, drawing from the original 2000 publication. Here is part of the description from that survey:

"…carried more positive meanings, such as superresolution, image inpainting and image synthesizing. Such hallucination is something we take advantage of rather than avoid in CV. Nevertheless, recent works have started to refer to a specific type of error as hallucination in image captioning and object detection, which denotes non-existing objects detected or localized at their expected position. The latter conception is similar to hallucination in NLG."

Editor's note: ChatGPT was many people's introduction to generative AI. Take a deep dive into the history of generative AI , which spans more than nine decades.

Continue Reading About AI hallucination

• Key benefits of AI for business
• AI risks businesses must confront and how to address them
• 4 main types of artificial intelligence: Explained
• AI transparency: What is it and why do we need it?
• Generative AI ethics: 8 biggest concerns

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