Download pdf
Analyzing Student Online Learning Behaviors and Academic Performance in Science Education Using Machine Learning Techniques
- 1 Lingnan University, Hong Kong, China
* Author to whom correspondence should be addressed.
Abstract
This study investigates the factors influencing student engagement and performance in online science education through the application of machine learning models, specifically Random Forests, Decision Trees, and Support Vector Machines (SVM). With the rapid growth of online education, understanding students' adaptability and learning behaviors has become increasingly critical. A systematic analysis of features such as study duration, daily study habits, and demographic factors revealed significant insights into their impact on academic achievement in science subjects. The Random Forest model outperformed others in classification accuracy, achieving an accuracy of 81%. The findings emphasize the importance of tailored educational strategies that foster consistent study practices and address the unique needs of diverse learners, ultimately enhancing learning outcomes in online science education.
Keywords
Online Learning, Science Subjects, Machine Learning, Academic Performance
[1]. Wu Wentao, Liu Hehai, Bai Qian. Building a Learning Society: Practicing Chinese Modernization through Educational Digitization [J]. Chinese Journal of Educational Technology, 2023, (03): 17-24+45.
[2]. Zhou Hongwei. Research on the Adaptive Learning Model of Online Education Based on Educational Big Data and Its Application [J]. Research on Continuing Education, 2023, (03): 110-114.
[3]. Tian Lan. Review of Research on Learning Adaptability of Primary and Secondary School Students in China [J]. Psychological Science, 2004, (02): 502-504.
[4]. Zhai X, Yin Y, Pellegrino J W, et al. Applying machine learning in science assessment: a systematic review[J]. Studies in Science Education, 2020, 56(1): 111-151.
[5]. Zhai X, Shi L, Nehm R H. A meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements[J]. Journal of Science Education and Technology, 2021, 30: 361-379.
[6]. Almasri F. Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research[J]. Research in Science Education, 2024, 54(5): 977-997.
[7]. Maestrales S, Zhai X, Touitou I, et al. Using machine learning to score multi-dimensional assessments of chemistry and physics[J]. Journal of Science Education and Technology, 2021, 30: 239-254.
[8]. Zhai X, Shi L. Understanding how the perceived usefulness of mobile technology impacts physics learning achievement: A pedagogical perspective[J]. Journal of Science Education and Technology, 2020, 29(6): 743-757.
[9]. Breiman L. Random forests[J]. Machine learning, 2001, 45: 5-32.
[10]. Biau G, Scornet E. A random forest guided tour[J]. Test, 2016, 25: 197-227.
[11]. Quinlan J R. Learning decision tree classifiers[J]. ACM Computing Surveys (CSUR), 1996, 28(1): 71-72.
[12]. Song Y Y, Ying L U. Decision tree methods: applications for classification and prediction[J]. Shanghai archives of psychiatry, 2015, 27(2): 130.
[13]. Jakkula V. Tutorial on support vector machine (svm)[J]. School of EECS, Washington State University, 2006, 37(2.5): 3.
[14]. Schuldt C, Laptev I, Caputo B. Recognizing human actions: a local SVM approach[C]//Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004, 3: 32-36.
Cite this article
Feng,C. (2024). Analyzing Student Online Learning Behaviors and Academic Performance in Science Education Using Machine Learning Techniques. Applied and Computational Engineering,112,59-65.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).