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Title: a review of data mining in personalized education: current trends and future prospects.

Abstract: Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.

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Information Discovery and Delivery

ISSN : 2398-6247

Article publication date: 19 May 2020

Issue publication date: 10 October 2020

Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education.

Design/methodology/approach

This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research.

Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward.

Research limitations/implications

Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals.

Originality/value

This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

• Educational data mining
• Learning analytics
• Systematic review
• Prediction of performance
• Decision support
• Artificial intelligence

Acknowledgements

Conflict of interest: The authors have declared no conflicts of interest for this article.

This study was supported by National Natural Science Foundation of China Under Grant No. 61877027.

Du, X. , Yang, J. , Hung, J.-L. and Shelton, B. (2020), "Educational data mining: a systematic review of research and emerging trends", Information Discovery and Delivery , Vol. 48 No. 4, pp. 225-236. https://doi.org/10.1108/IDD-09-2019-0070

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Journal of Educational Data Mining

About the journal.

The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100; see indexing ) is published by the International Educational Data Mining Society (IEDMS) . It is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is completely and permanently free and open-access to both authors and readers . 

• ►Processes or methodologies followed to analyse educational data
• ►Integrating data mining with pedagogical theories
• ►Describing the way findings are used for improving educational software or teacher support
• ►Improving understanding of learners' domain representations
• ►improving assessment of learners' engagement in the learning tasks

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Published: 2023-12-26

Editorial Acknowledgment

Examining algorithmic fairness for first- term college grade prediction models relying on pre-matriculation data, modelling argument quality in technology-mediated peer instruction, extended articles from the edm 2023 conference, automated search improves logistic knowledge tracing, surpassing deep learning in accuracy and explainability.

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A bibliometric analysis of Educational Data Mining studies in global perspective

• Published: 02 September 2023
• Volume 29 , pages 8961–8985, ( 2024 )

Cite this article

• Gizem Dilan Boztaş   ORCID: orcid.org/0000-0002-4593-032X 1 ,
• Muhammet Berigel   ORCID: orcid.org/0000-0001-5682-8956 2 &
• Fahriye Altınay   ORCID: orcid.org/0000-0002-3861-6447 3  

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Educational Data Mining (EDM) is an interdisciplinary field that encapsulates different fields such as computer science, education, and statistics. It is crucial to make data mining in education to shape future trends in education for policymakers, researchers, and educators in terms of developments. To have an all-inclusive understanding of EDM studies, a comprehensive examination of both the intellectual and social structure of the field with a global perspective and its evolution over time is required to provide adequate comprehension of the past, present, and as well future research direction for this research field. In this respect, this research study aims to explore the performance analysis of the EDM, the intellectual structure of the EDM, the social structure of the area, and the temporal modeling of the EDM through bibliometric analysis. In this bibliometric analysis, the existing body of knowledge that covered 2010–2021 was presented on educational data mining to provide future directions in the field of education. The results of the study showed the number of publications increased by 1325% (8 to 114) over the years. It has been determined that the most influential journals in the field are “Computers & Education”, “International Journal of Advanced Computer Science and Applications” and “Educational Technology & Society” and the most influential authors are “C. Romero”, “S. Ventura” and “R.S. Barker”, and the USA, Spain, and China seem to be the most influential countries in the field of EDM. The themes of “CLASSIFICATION” and “SYSTEMS” in the first sub-period (2010–2013) of EDM, and “LEARNING ANALYTICS”, “PREDICTION OF ACADEMIC PERFORMANCE” and “SOCIAL MEDIA” in the second sub-term (2014–2017) and the third and last sub-period (2018–2021), “PREDICTION OF ACADEMIC PERFORMANCE”, “MACHINE LEARNING”, “PROCESS MINING”, “PARTICIPATION”, “KNN”, “J48” and “BAYESIAN NETWORK” themes were identified as engine themes. In addition, as a result of the thematic evolution map, it was discovered that the themes “DEEP-LEARNING (DL)” and “EMOTION” are emerging themes for future studies for shaping the future of education based on sustainable goals.

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Educational Data Mining: A Systematic Review of the Published Literature 2006-2013

Uncovering trend-based research insights on teaching and learning in big data

Education big data and learning analytics: a bibliometric analysis

Data availability.

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

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Boztaş, G.D., Berigel, M. & Altınay, F. A bibliometric analysis of Educational Data Mining studies in global perspective. Educ Inf Technol 29 , 8961–8985 (2024). https://doi.org/10.1007/s10639-023-12170-0

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Educational data mining: prediction of students' academic performance using machine learning algorithms

• Mustafa Yağcı   ORCID: orcid.org/0000-0003-2911-3909 1  

Smart Learning Environments volume  9 , Article number:  11 ( 2022 ) Cite this article

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Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The performances of the random forests, nearest neighbour, support vector machines, logistic regression, Naïve Bayes, and k-nearest neighbour algorithms, which are among the machine learning algorithms, were calculated and compared to predict the final exam grades of the students. The dataset consisted of the academic achievement grades of 1854 students who took the Turkish Language-I course in a state University in Turkey during the fall semester of 2019–2020. The results show that the proposed model achieved a classification accuracy of 70–75%. The predictions were made using only three types of parameters; midterm exam grades, Department data and Faculty data. Such data-driven studies are very important in terms of establishing a learning analysis framework in higher education and contributing to the decision-making processes. Finally, this study presents a contribution to the early prediction of students at high risk of failure and determines the most effective machine learning methods.

Introduction

The application of data mining methods in the field of education has attracted great attention in recent years. Data Mining (DM) is the discovery of data. It is the field of discovering new and potentially useful information or meaningful results from big data (Witten et al., 2011 ). It also aims to obtain new trends and new patterns from large datasets by using different classification algorithms (Baker & Inventado, 2014 ).

Educational data mining (EDM) is the use of traditional DM methods to solve problems related to education (Baker & Yacef, 2009 ; cited in Fernandes et al., 2019 ). EDM is the use of DM methods on educational data such as student information, educational records, exam results, student participation in class, and the frequency of students' asking questions. In recent years, EDM has become an effective tool used to identify hidden patterns in educational data, predict academic achievement, and improve the learning/teaching environment.

Learning analytics has gained a new dimension through the use of EDM (Waheed et al., 2020 ). Learning analytics covers the various aspects of collecting student information together, better understanding the learning environment by examining and analysing it, and revealing the best student/teacher performance (Long & Siemens, 2011 ). Learning analytics is the compilation, measurement and reporting of data about students and their contexts in order to understand and optimize learning and the environments in which it takes place. It also deals with the institutions developing new strategies.

Another dimension of learning analytics is predicting student academic performance, uncovering patterns of system access and navigational actions, and determining students who are potentially at risk of failing (Waheed et al., 2020 ). Learning management systems (LMS), student information systems (SIS), intelligent teaching systems (ITS), MOOCs, and other web-based education systems leave digital data that can be examined to evaluate students' possible behavior. Using EDM method, these data can be employed to analyse the activities of successful students and those who are at risk of failure, to develop corrective strategies based on student academic performance, and therefore to assist educators in the development of pedagogical methods (Casquero et al., 2016 ; Fidalgo-Blanco et al., 2015 ).

The data collected on educational processes offer new opportunities to improve the learning experience and to optimize users' interaction with technological platforms (Shorfuzzaman et al., 2019 ). The processing of educational data yields improvements in many areas such as predicting student behaviour, analytical learning, and new approaches to education policies (Capuano & Toti, 2019 ; Viberg et al., 2018 ). This comprehensive collection of data will not only allow education authorities to make data-based policies, but also form the basis of software to be developed with artificial intelligence on the learning process.

EDM enables educators to predict situations such as dropping out of school or less interest in the course, analyse internal factors affecting their performance, and make statistical techniques to predict students' academic performance. A variety of DM methods are employed to predict student performance, identify slow learners, and dropouts (Hardman et al., 2013 ; Kaur et al., 2015 ). Early prediction is a new phenomenon that includes assessment methods to support students by proposing appropriate corrective strategies and policies in this field (Waheed et al., 2020 ).

Especially during the pandemic period, learning management systems, quickly put into practice, have become an indispensable part of higher education. While students use these systems, the log records produced have become ever more accessible. (Macfadyen & Dawson, 2010 ; Kotsiantis et al., 2013 ; Saqr et al., 2017 ). Universities now should improve the capacity of using these data to predict academic success and ensure student progress (Bernacki et al., 2020 ).

As a result, EDM provides the educators with new information by discovering hidden patterns in educational data. Using this model, some aspects of the education system can be evaluated and improved to ensure the quality of education.

In various studies on EDM, e-learning systems have been successfully analysed (Lara et al., 2014 ). Some studies have also classified educational data (Chakraborty et al., 2016 ), while some have tried to predict student performance (Fernandes et al., 2019 ).

Asif et al. ( 2017 ) focused on two aspects of the performance of undergraduate students using DM methods. The first aspect is to predict the academic achievements of students at the end of a four-year study program. The second one is to examine the development of students and combine them with predictive results. He divided the students into two parts as low achievement and high achievement groups. He have found that it is important for the educators to focus on a small number of courses indicating particularly good or poor performance in order to offer timely warnings, support underperforming students and offer advice and opportunities to high-performing students. Cruz-Jesus et al. ( 2020 ) predicted student academic performance with 16 demographics such as age, gender, class attendance, internet access, computer possession, and the number of courses taken. Random forest, logistic regression, k-nearest neighbours and support vector machines, which are among the machine learning methods, were able to predict students’ performance with accuracy ranging from 50 to 81%.

Fernandes et al. ( 2019 ) developed a model with the demographic characteristics of the students and the achievement grades obtained from the in-term activities. In that study, students' academic achievement was predicted with classification models based on Gradient Boosting Machine (GBM). The results showed that the best qualities for estimating achievement scores were the previous year's achievement scores and unattendance. The authors found that demographic characteristics such as neighbourhood, school and age information were also potential indicators of success or failure. In addition, he argued that this model could guide the development of new policies to prevent failure. Similarly, by using the student data requested during registration and environmental factors, Hoffait and Schyns ( 2017 ) determined the students with the potential to fail. He found that students with potential difficulties could be classified more precisely by using DM methods. Moreover, their approach makes it possible to rank the students by levels of risk. Rebai et al. ( 2020 ) proposed a machine learning-based model to identify the key factors affecting academic performance of schools and to determine the relationship between these factors. He concluded that the regression trees showed that the most important factors associated with higher performance were school size, competition, class size, parental pressure, and gender proportions. In addition, according to the random forest algorithm results, the school size and the percentage of girls had a powerful impact on the predictive accuracy of the model.

Ahmad and Shahzadi, ( 2018 ) proposed a machine learning-based model to find an answer to the question whether students were at risk regarding their academic performance. Using the students' learning skills, study habits, and academic interaction features, they made a prediction with a classification accuracy of 85%. The researchers concluded that the model they proposed could be used to determine academically unsuccessful student. Musso et al., ( 2020 ) proposed a machine learning model based on learning strategies, perception of social support, motivation, socio-demographics, health condition, and academic performance characteristics. With this model, he predicted the academic performance and dropouts. He concluded that the predictive variable with the highest effect on predicting GPA was learning strategies while the variable with the greatest effect on determining dropouts was background information.

Waheed et al., ( 2020 ) designed a model with artificial neural networks on students' records related to their navigation through the LMS. The results showed that demographics and student clickstream activities had a significant impact on student performance. Students who navigated through courses performed higher. Students' participation in the learning environment had nothing to do with their performance. However, he concluded that the deep learning model could be an important tool in the early prediction of student performance. Xu et al. ( 2019 ) determined the relationship between the internet usage behaviors of university students and their academic performance and he predicted students’ performance with machine learning methods. The model he proposed predicted students' academic performance at a high level of accuracy. The results suggested that Internet connection frequency features were positively correlated with academic performance, whereas Internet traffic volume features were negatively correlated with academic performance. In addition, he concluded that internet usage features had an important role on students' academic performance. Bernacki et al. ( 2020 ) tried to find out whether the log records in the learning management system alone would be sufficient to predict achievement. He concluded that the behaviour-based prediction model successfully predicted 75% of those who would need to repeat a course. He also stated that, with this model, students who might be unsuccessful in the subsequent semesters could be identified and supported. Burgos et al. ( 2018 ) predicted the achievement grades that the students might get in the subsequent semesters and designed a tool for students who were likely to fail. He found that the number of unsuccessful students decreased by 14% compared to previous years. A comparative analysis of studies predicting the academic achievement grades using machine learning methods is given in Table 1 .

A review of previous research that aimed to predict academic achievement indicates that researchers have applied a range of machine learning algorithms, including multiple, probit and logistic regression, neural networks, and C4.5 and J48 decision trees. However, random forests (Zabriskie et al., 2019 ), genetic programming (Xing et al., 2015 ), and Naive Bayes algorithms (Ornelas & Ordonez, 2017 ) were used in recent studies. The prediction accuracy of these models reaches very high levels.

Prediction accuracy of student academic performance requires an deep understanding of the factors and features that impact student results and the achievement of student (Alshanqiti & Namoun, 2020 ). For this purpose, Hellas et al. ( 2018 ) reviewed 357 articles on student performance detailing the impact of 29 features. These features were mainly related to psychomotor skills such as course and pre-course performance, student participation, student demographics such as gender, high school performance, and self-regulation. However, the dropout rates were mainly influenced by student motivation, habits, social and financial issues, lack of progress, and career transitions.

The literature review suggests that, it is a necessity to improve the quality of education by predicting the academic performance of the students and supporting those who are in the risk group. In the literature, the prediction of academic performance was made with many and various variables, various digital traces left by students on the internet (browsing, lesson time, percentage of participation) (Fernandes et al., 2019 ; Rubin et al., 2010 ; Waheed et al., 2020 ; Xu et al., 2019 ) and students demographic characteristics (gender, age, economic status, number of courses attended, internet access, etc.) (Bernacki et al., 2020 ; Rizvi et al., 2019 ; García-González & Skrita, 2019 ; Rebai et al., 2020 ; Cruz-Jesus et al., 2020 ; Aydemir, 2017 ), learning skills, study approaches, study habits (Ahmad & Shahzadi, 2018 ), learning strategies, social support perception, motivation, socio-demography, health form, academic performance characteristics (Costa-Mendes et al., 2020 ; Gök, 2017 ; Kılınç, 2015 ; Musso et al., 2020 ), homework, projects, quizzes (Kardaş & Güvenir, 2020 ), etc. In almost all models developed in such studies, prediction accuracy is ranging from 70 to 95%. Hovewer, collecting and processing such a variety of data both takes a lot of time and requires expert knowledge. Similarly, Hoffait and Schyns ( 2017 ) suggested that collecting so many data is difficult and socio-economic data are unnecessary. Moreover, these demographic or socio-economic data may not always give the right idea of preventing failure (Bernacki et al., 2020 ).

The study concerns predicting students’ academic achievement using grades only, no demographic characteristics and no socio-economic data. This study aimed to develop a new model based on machine learning algorithms to predict the final exam grades of undergraduate students taking their midterm exam grades, Faculty and Department of the students.

For this purpose, classification algorithms with the highest performance in predicting students’ academic achievement were determined by using machine learning classification algorithms. The reason for choosing the Turkish Language-I course was that it is a compulsory course that all students enrolled in the university must take. Using this model, students’ final exam grades were predicted. These models will enable the development of pedagogical interventions and new policies to improve students' academic performance. In this way, the number of potentially unsuccessful students can be reduced following the assessments made after each midterm.

This section describes the details of the dataset, pre-processing techniques, and machine learning algorithms employed in this study.

Educational institutions regularly store all data that are available about students in electronic medium. Data are stored in databases for processing. These data can be of many types and volumes, from students’ demographics to their academic achievements. In this study, the data were taken from the Student Information System (SIS), where all student records are stored at a State University in Turkey. In these records, the midterm exam grades, final exam grades, Faculty, and Department of 1854 students who have taken the Turkish Language-I course in the 2019–2020 fall semester were selected as the dataset. Table 2 shows the distribution of students according to the academic unit. Moreover, as a additional file 1 the dataset are presented.

Midterm and final exam grades are ranging from 0 to 100. In this system, the end-of-semester achievement grade is calculated by taking 40% of the midterm exam and 60% of the final exam. Students with achievement grade below 60 are unsuccessful and those above 60 are successful. The midterm exam is usually held in the middle of the academic semester and the final exam is held at the end of the semester. There are approximately 9 weeks (2.5 months) from the midterm exam to the final exam. In other words, there is a two and a half month period for corrective actions for students who are at risk of failing thanks to the final exam predictions made. In other words, the answer to the question of how effective the student's performance in the middle of the semester is on his performance at the end of the semester was investigated.

Data identification and collection

At this phase, it is determined from which source the data will be stored, which features of the data will be used, and whether the collected data is suitable for the purpose. Feature selection involves decreasing the number of variables used to predict a particular outcome. The goal; to facilitate the interpretability of the model, reduce complexity, increase the computational efficiency of algorithms, and avoid overfitting.

Establishing DM model and implementation of algorithm

RF, NN, LR, SVM, NB and kNN were employed to predict students' academic performance. The prediction accuracy was evaluated using tenfold cross validation. The DM process serves two main purposes. The first purpose is to make predictions by analyzing the data in the database (predictive model). The second one is to describe behaviors (descriptive model). In predictive models, a model is created by using data with known results. Then, using this model, the result values are predicted for datasets whose results are unknown. In descriptive models, the patterns in the existing data are defined to make decisions.

When the focus is on analysing the causes of success or failure, statistical methods such as logistic regression and time series can be employed (Ortiz & Dehon, 2008 ; Arias Ortiz & Dehon, 2013 ). However, when the focus is on forecasting, neural networks (Delen, 2010 ; Vandamme et al., 2007 ), support vector machines (Huang & Fang, 2013 ), decision trees (Delen, 2011 ; Nandeshwar et al., 2011 ) and random forests (Delen, 2010 ; Vandamme et al., 2007 ) is more efficient and give more accurate results. Statistical techniques are to create a model that can successfully predict output values based on available input data. On the other hand, machine learning methods automatically create a model that matches the input data with the expected target values when a supervised optimization problem is given.

The performance of the model was measured by confusion matrix indicators. It is understood from the literature that there is no single classifier that works best for prediction results. Therefore, it is necessary to investigate which classifiers are more studied for the analysed data (Asif et al., 2017 ).

Experiments and results

The entire experimental phase was performed with Orange machine learning software. Orange is a powerful and easy-to-use component-based DM programming tool for expert data scientists as well as for data science beginners. In Orange, data analysis is done by stacking widgets into workflows. Each widget includes some data retrieval, data pre-processing, visualization, modelling, or evaluation task. A workflow is a series of actions or actions that will be performed on the platform to perform a specific task. Comprehensive data analysis charts can be created by combining different components in a workflow. Figure  1 shows the workflow diagram designed.

The workflow of the designed model

The dataset included midterm exam grades, final exam grades, Faculty, and Department of 1854 students taking the Turkish Language-I course in the 2019–2020 Fall Semester. The entire dataset is provided as Additional file 1 . Table 3 shows part of the dataset.

In the dataset, students' midterm exam grades, final exam grades, faculty, and department information were determined as features. Each measure contains data associated with a student. Midterm exam and final exam grade variables were explained under the heading "dataset". The faculty variable represents Faculties in Kırşehir Ahi Evran University and the department variable represents departments in faculties. In the development of the model, the midterm, the faculty, and the department information were determined as the independent variable and the final was determined as the dependent variable. Table 4 shows the variable model.

After the variable model was determined, the midterm exam grades and final exam grades were categorized according to the equal-width discretization model. Table 5 shows the criteria used in converting midterm exam grades and final exam grades into the categorical format.

In Table 6 , the values in the final column are the actual values. The values in the RF, SVM, LR, KNN, NB, and NN columns are the values predicted by the proposed model. For example, according to Table 5 , std1’s actual final grade was in the range 55 to 77. While the predicted value of the RF, SVM, LR, NB, and NN models were in the range of, the predicted value of the kNN model was greater than 77.

Evaluation of the model performance

The performance of model was evaluated with confusion matrix, classification accuracy (CA), precision, recall, f-score (F1), and area under roc curve (AUC) metrics.

Confusion matrix

The confusion matrix shows the current situation in the dataset and the number of correct/incorrect predictions of the model. Table 7 shows the confusion matrix. The performance of the model is calculated by the number of correctly classified instances and incorrectly classified instances. The rows show the real numbers of the samples in the test set, and the columns represent the estimation of the model.

In Table 6 , true positive (TP) and true negative (TN) show the number of correctly classified instances. False positive (FP) shows the number of instances predicted as 1 (positive) while it should be in the 0 (negative) class. False negative (FN) shows the number of instances predicted as 0 (negative) while it should be in class 1 (positive).

Table 8 shows the confusion matrix for the RF algorithm. In the confusion matrix of 4 × 4 dimensions, the main diagonal shows the percentage of correctly predicted instances, and the matrix elements other than the main diagonal shows the percentage of errors predicted.

Table 8 shows that 84.9% of those with the actual final grade greater than 77.5, 71.2% of those with range 55–77.5, 65.4% of those with range 32.5–55, and 60% of those with less than 32.5 were predicted correctly. Confusion matrixs of other algorithms are shown in Tables 9 , 10 , 11 , 12 , and 13 .

Classification accuracy:  CA is the ratio of the correct predictions (TP + TN) to the total number of instances (TP + TN + FP + FN).

Precision: Precision is the ratio of the number of positive instances that are correctly classified to the total number of instances that are predicted positive. Gets a value in the range [0.1].

Recall: Recall i s the ratio of the correctly classified number of positive instances to the number of all instances whose actual class is positive. The Recall is also called the true positive rate. Gets a value in the range [0.1].

F-Criterion (F1):  There is an opposite relationship between precision and recall. Therefore, the harmonic mean of both criteria is calculated for more accurate and sensitive results. This is called the F-criterion.

Receiver operating characteristics (ROC) curve

The AUC-ROC curve is used to evaluate the performance of a classification problem. AUC-ROC is a widely used metric to evaluate the performance of machine learning algorithms, especially in cases where there are unbalanced datasets, and explains how well the model is at predicting.

AUC: Area under the ROC curve

The larger the area covered, the better the machine learning algorithms at distinguishing given classes. AUC for the ideal value is 1. The AUC, Classification Accuracy (CA), F-Criterion (F1), precision, and recall values of the models are shown in Table 14 .

The AUC value of RF, NN, SVM, LR, NB, and kNN algorithms were 0.860, 0.863, 0.804, 0.826, 0.810, and 0.810 respectively. The classification accuracy of the RF, NN, SVM, LR, NB, and kNN algorithms were also 0.746, 0.746, 0.735, 0.717, 0.713, and 0,699 respectively. According to these findings, for example, the RF algorithm was able to achieve 74.6% accuracy. In other words, there was a very high-level correlation between the data predicted and the actual data. As a result, 74.6% of the samples were been classified correctly.

Discussion and conclusion

This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The performances of the Random Forests, nearest neighbour, support vector machines, Logistic Regression, Naïve Bayes, and k-nearest neighbour algorithms, which are among the machine learning algorithms, were calculated and compared to predict the final exam grades of the students. This study focused on two parameters. The first parameter was the prediction of academic performance based on previous achievement grades. The second one was the comparison of performance indicators of machine learning algorithms.

The results show that the proposed model achieved a classification accuracy of 70–75%. According to this result, it can be said that students' midterm exam grades are an important predictor to be used in predicting their final exam grades. RF, NN, SVM, LR, NB, and kNN are algorithms with a very high accuracy rate that can be used to predict students' final exam grades. Furthermore, the predictions were made using only three types of parameters; midterm exam grades, Department data and Faculty data. The results of this study were compared with the studies that predicted the academic achievement grades of the students with various demographic and socio-economic variables. Hoffait and Schyns ( 2017 ) proposed a model that uses the academic achievement of students in previous years. With this model, they predicted students' performance to be successful in the courses they will take in the new semester. They found that 12.2% of the students had a very high risk of failure, with a 90% confidence rate. Waheed et al. ( 2020 ) predicted the achievement of the students with demographic and geographic characteristics. He found that it has a significant effect on students' academic performance. He predicted the failure or success of the students by 85% accuracy. Xu et al. ( 2019 ) found that internet usage data can distinguish and predict students' academic performance. Costa-Mendes et al. ( 2020 ), Cruz-Jesus et al. ( 2020 ), Costa-Mendes et al. ( 2020 ) predicted the academic achievement of students in the light of income, age, employment, cultural level indicators, place of residence, and socio-economic information. Similarly, Babić ( 2017 ) predicted students’ performance with an accuracy of 65% to 100% with artificial neural networks, classification tree, and support vector machines methods.

Another result of this study was RF, NN and SVM algorithms have the highest classification accuracy, while kNN has the lowest classification accuracy. According to this result, it can be said that RF, NN and SVM algorithms perform with more accurate results in predicting the academic achievement grades of students with machine learning algorithms. The results were compared with the results of the research in which machine learning algorithms were employed to predict academic performance according to various variables. For example, Hoffait and Schyns ( 2017 ) compared the performances of LR, ANN and RF algorithms to identify students at high risk of academic failure on their various demographic characteristics. They ranked the algorithms from those with the highest accuracy to the ones with the lowest accuracy as LR, ANN, and RF. On the other hand, Waheed et al. ( 2020 ) found that the SVM algorithm performed higher than the LR algorithm. According to Xu et al. ( 2019 ), the algorithm with the highest performance is SVM, followed by the NN algorithm, and the decision tree is the algorithm with the lowest performance.

The proposed model predicted the final exam grades of students with 73% accuracy. According to this result, it can be said that academic achievement can be predicted with this model in the future. By predicting students' achievement grades in future, students can be allowed to review their working methods and improve their performance. The importance of the proposed method can be better understood, considering that there is approximately 2.5 months between the midterm exams and the final exams in higher education. Similarly, Bernacki et al. ( 2020 ) work on the early warning model. He proposed a model to predict the academic achievements of students using their behavior data in the learning management system before the first exam. His algorithm correctly identified 75% of students who failed to earn the grade of B or better needed to advance to the next course. Ahmad and Shahzadi ( 2018 ) predicted students at risk for academic performance with 85% accuracy evaluating their study habits, learning skills, and academic interaction features. Cruz-Jesus et al. ( 2020 ) predicted students' end-of-semester grades with 16 independent variables. He concluded that students could be given the opportunity of early intervention.

As a result, students' academic performances were predicted using different predictors, different algorithms and different approaches. The results confirm that machine learning algorithms can be used to predict students’ academic performance. More importantly, the prediction was made only with the parameters of midterm grade, faculty and department. Teaching staff can benefit from the results of this research in the early recognition of students who have below or above average academic motivation. Later, for example, as Babić ( 2017 ) points out, they can match students with below-average academic motivation by students with above-average academic motivation and encourage them to work in groups or project work. In this way, the students' motivation can be improved, and their active participation in learning can be ensured. In addition, such data-driven studies should assist higher education in establishing a learning analytics framework and contribute to decision-making processes.

Future research can be conducted by including other parameters as input variables and adding other machine learning algorithms to the modelling process. In addition, it is necessary to harness the effectiveness of DM methods to investigate students' learning behaviors, address their problems, optimize the educational environment, and enable data-driven decision making.

Availability of data and materials

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

Abbreviations

• Educational data mining

Random forests

Neural networks

Support vector machines

Logistic regression

Naïve Bayes

K-nearest neighbour

Decision trees

Artificial neural networks

Extremely randomized trees

Regression trees

Multilayer perceptron neural network

Feed-forward neural network

Adaptive resonance theory mapping

Learning management systems

Student information systems

Intelligent teaching systems

Classification accuracy

Area under roc curve

True positive

True negative

False positive

False negative

Receiver operating characteristics

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Yağcı, M. Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learn. Environ. 9 , 11 (2022). https://doi.org/10.1186/s40561-022-00192-z

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Received : 15 November 2021

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DOI : https://doi.org/10.1186/s40561-022-00192-z

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ORIGINAL RESEARCH article

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Education for the Future: Learning and Teaching for Sustainable Development in Education

Blending Pedagogy: Equipping Student Teachers to Foster Transversal Competencies in Future-oriented Education Provisionally Accepted

• 1 Department of Education, Faculty of Educational Sciences, University of Helsinki, Finland

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Blended teaching and learning, combining online and face-to-face instruction, and shared reflection are gaining in popularity worldwide and present evolving challenges in the field of teacher training and education. There is also a growing need to focus on transversal competencies such as critical thinking and collaboration. This study is positioned at the intersection of blended education and transversal competencies in the context of a blended ECEC teacher-training program (1000+) at the University of Helsinki. Blended education is a novel approach to training teachers, and there is a desire to explore how such an approach supports the acquisition of transversal competencies and whether the associated methods offer something essential for the development of teacher training. The aim is to explore what transversal competencies this teacher-training program supports for future teachers, and how students reflect on their learning experiences. The data consist of documents from teacher-education curricula and essays from the students on the 1000+ program. They were content-analyzed from a scoping perspective. Students' experiences of studying enhanced the achievement of generic goals in teacher education, such as to develop critical and reflective thinking, interaction competence, collaboration skills, and independent and collective expertise. We highlight the importance of teacher development in preparing for education in the future during the teacher training. Emphasizing professional development, we challenge the conventional teaching paradigm by introducing a holistic approach.

Keywords: blended teacher training, Transversal competencies, future of education, Teacher Education, early childhood education

Received: 19 Jan 2024; Accepted: 15 May 2024.

Copyright: © 2024 Niemi, Kangas and Köngäs. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Laura H. Niemi, Department of Education, Faculty of Educational Sciences, University of Helsinki, Helsinki, 00014, Uusimaa, Finland

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Coming out of the ashes we rise: Experiences of culturally and linguistically diverse international nursing students at two Australian universities during the Covid-19 pandemic

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Background and aim: Research on international students conducted during the COVID-19 pandemic has persistently highlighted the vulnerabilities and challenges that they experienced when staying in the host country to continue with their studies. The findings from such research can inevitably create a negative image of international students and their ability to respond to challenges during unprecedented times. Therefore, this paper took a different stance and reported on a qualitative study that explored culturally and linguistically diverse (CaLD) international nursing students who overcame the challenges brought about by the pandemic to continue with their studies in Australia. Method: A descriptive qualitative research design guided by the processes of constructivist grounded theory was selected to ascertain insights from participants' experiences of studying abroad in Australia during the COVID-19 pandemic. Results: Three themes emerged from the collected data that described the participants' lived experiences, and they were: 1) Viewing international education as the pursuit of a better life, 2) Focusing on personal growth, and 3) Coming out of the ashes we rise. Discussion: The findings highlight the importance of recognising the investments and sacrifices that CaLD international students and their families make in pursuit of international tertiary education. The findings also underscore the importance of acknowledging the qualities that CaLD international students have to achieve self-growth and ultimately self-efficacy as they stay in the host country during a pandemic. Conclusion: Future research should focus on identifying strategies that are useful for CaLD international nursing students to experience personal growth and ultimately self-efficacy and continue with their studies in the host country during times of uncertainty such as a pandemic.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was obtained from Curtin University Human Research Ethics Office (HRE2022-0238) and The University of Southern Queensland Ethical Review Committee (H22REA114).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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