Machine Learning Approaches for Student Performance Prediction
13 Oct 2022-
TL;DR: In this article , a focus is given on additional external factors such as geographical location, parent education, health status etc. that can affect a students' performances apart from the grades in any course.
Abstract: India's Education system is very old and due to a large population of students in India, there are some serious issues in analyzing and predicting students' performance. In the Indian Context, every institution has its own set of standards for evaluating student success, there is no proper procedure for monitoring and analyzing a student's performance and progress. One of the major factors is lack of research in existing prediction approaches, making it difficult to determine the optimal prediction methodology for visualizing student academic growth and performance. Another reason could be the lack of research into the areas that can affect students' academic performance and achievement. In this paper, focus is given on additional external factors like geographical location, parent education, health status etc. that can affect a students' performances apart from the grades in any course. That will be more effective in visualizing and analyzing student's performance. For experimental work, data has been collected from UCI repository and results are obtained from two different machine learning algorithms (KNN and Logistic Regression). Performance analysis is also done for these two algorithms based on accuracy level of results as well as with some existing work.
TL;DR: An overview on the data mining techniques that have been used to predict students performance and how the prediction algorithm can be used to identify the most important attributes in a students data is provided.
02 Jul 2018
TL;DR: In this paper, the authors present a systematic literature review of work in the area of predicting student performance, which shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used.
Abstract: The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.
TL;DR: A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes as discussed by the authors.
Abstract: The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.
TL;DR: In this article, a computerized content analysis was conducted to examine how AI and deep learning research themes have evolved in major educational journals and uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade.
TL;DR: The main aim of this study is to identify the most commonly studied factors that affect the students’ performance, as well as, the most common data mining techniques applied to identify these factors.
Abstract: Predicting the students’ performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students’ performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually identified as educational data mining. Hence, the main aim of this study is to identify the most commonly studied factors that affect the students’ performance, as well as, the most common data mining techniques applied to identify these factors. In this study, 36 research articles out of a total of 420 from 2009 to 2018 were critically reviewed and analyzed by applying a systematic literature review approach. The results showed that the most common factors are grouped under four main categories, namely students’ previous grades and class performance, students’ e-Learning activity, students’ demographics, and students’ social information. Additionally, the results also indicated that the most common data mining techniques used to predict and classify students’ factors are decision trees, Naive Bayes classifiers, and artificial neural networks.
Related Papers (5)
13 Oct 2022