Educational data mining: prediction of students' academic performance using machine learning algorithms
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In this article , 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, was proposed and the results showed that the proposed model achieved a classification accuracy of 70-75%.Abstract:
Abstract 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. read more
Citations
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References
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Proceedings ArticleDOI
The State of Educational Data Mining in 2009: A Review and Future Visions
Ryan S. Baker,Kalina Yacef +1 more
TL;DR: This paper reviewed the history and current trends in the field of EDM and discussed trends and shifts in the research conducted by this community, and discussed the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries, and the reduction in the frequency of relationship mining within the EDM community.
Journal ArticleDOI
Mining LMS data to develop an early warning system for educators: A proof of concept
Leah P. Macfadyen,Shane Dawson +1 more
TL;DR: This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboard-like reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success.
Book ChapterDOI
Educational Data Mining and Learning Analytics
TL;DR: How these methods emerged in the early days of research in this area is discussed, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.
Journal ArticleDOI
Analyzing undergraduate students\' performance using educational data mining
TL;DR: The results indicate that by focusing on a small number of courses that are indicators of particularly good or poor performance, it is possible to provide timely warning and support to low achieving students, and advice and opportunities to high performing students.
Journal ArticleDOI
The current landscape of learning analytics in higher education
TL;DR: This study is based on the analysis of 252 papers on learning analytics in higher education and finds that learning analytics can improve learning practice by transforming the ways the authors support learning processes.