Abstract
AbstractA major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students’ performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Education
Reference49 articles.
1. Alabri, A., Al-Khanjari, Z., Jamoussi, Y., & Kraiem, N. (2019). Mining the students’ chat conversations in a personalized e-learning environment. International Journal of Emerging Technologies in Learning (iJET), 14(23), 98–124.
2. Alfere, S. S., & Maghari, A. Y. (2018). Prediction of student's performance using modified KNN classifiers. Prediction of Student's Performance Using Modified KNN Classifiers.
3. Asogwa, O., & Oladugba, A. (2015). Of students academic performance rates using artificial neural networks (ANNs). American Journal of Applied Mathematics and Statistics, 3(4), 151–155.
4. Chen, H. (2018). Predicting student performance using data from an Auto-grading system. University of Waterloo.
5. Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256.
Cited by
29 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献