E-Learning Behavior Categories and Influencing Factors of STEM Courses: A Case Study of the Open University Learning Analysis Dataset (OULAD)

Jingran Zhang, Feiyue Qiu (), Wei Wu, Jiayue Wang, Rongqiang Li, Mujie Guan and Jiang Huang
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Jingran Zhang: College of Education, Zhejiang University of Technology, Hangzhou 310023, China
Feiyue Qiu: College of Education, Zhejiang University of Technology, Hangzhou 310023, China
Wei Wu: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Jiayue Wang: College of Education, Zhejiang University of Technology, Hangzhou 310023, China
Rongqiang Li: College of Education, Zhejiang University of Technology, Hangzhou 310023, China
Mujie Guan: College of Education, Zhejiang University of Technology, Hangzhou 310023, China
Jiang Huang: College of Education, Zhejiang University of Technology, Hangzhou 310023, China

Sustainability, 2023, vol. 15, issue 10, 1-22

Abstract: With a focus on enhancing national scientific and technological competitiveness and cultivating innovative talents, STEM education has achieved remarkable results in developing students’ core quality and improving academic achievement. Online courses built for STEM education have attracted many learners. However, as the number of learners continues to grow, online STEM education faces problems such as difficulties in ensuring the quality of teaching and learning in STEM online courses and poor performance of students in online learning. An in-depth exploration of the correlations between learners’ E-learning behavior categories and learning outcomes in STEM education online courses will facilitate teachers’ precise interventions for students who are learning online. This study first predicts the E-learning performance of STEM course learners through machine learning and deep learning algorithms, then uses factor analysis methods to discover correlations between behavioral features, uses the random forest algorithm to explore the vital behavioral features that influence the E-learning performance of STEM courses, and finally performs a category classification of important characteristic behaviors based on the learning behavior category basis. The results show that the learning behavior classifications of learning preparation behavior, knowledge acquisition behavior, and learning consolidation behavior affect the E-learning performance of learners in STEM courses. Moreover, a series of characteristic behaviors strongly affect E-learning performance. In general, teachers can systematically intervene in time for at-risk students from the perspective of learning behavior categories and further improve the construction of STEM online courses.

Keywords: STEM course; learning behavior category; E-learning performance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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