Analysing the evolution of student interaction patterns in a Massive Private Online Course

ABSTRACT

Recently, researchers have proposed to leverage technology-supported data (log files) to investigate temporal and sequential patterns of interaction behaviors in learning processes. There are two major challenges to be addressed: clarifying the positioning of interaction levels and identifying the evolution of the interaction action patterns in learning processes, particularly for students with differing achievements. This paper explores the use of sequential pattern mining to address the evolution of student action patterns in Massive Private Online Courses (MPOCs) and compare these patterns between different achievement groups. The study was conducted with first-year undergraduate computer science students enrolled in a computer application course at a traditional open university in one of the Chinese provinces (N = 1375). The results showed the development of various action patterns in each phase of the course and the distinct action patterns for high-achieving and low-achieving students. The findings of study provide a new perspective for instructors and students to understand interaction patterns at the fine-grained level, and can help instructional designers develop learner-cantered courses and platforms to improve online learning.

KEYWORDS:

• Sequential pattern mining
• pattern evolution
• interaction action
• academic achievement
• online learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Research Grant of the Open University of China, “Evaluation model and tool based on learning analytics for the effect of learning resources application in online education”, and the Higher Education Research Project of Hubei Province (Project No. 2021085).

Notes on contributors

Di Sun

Di Sun is an Associate Professor at Dalian University of Technology. Her research focus is on learning analytics and educational data mining.

Gang Cheng

Gang Cheng is an associate professor at the Open University of China. His research interests include resource and environment of digital learning, learner support, and learning analytics.

Heng Luo

Heng Luo is an Associate Professor at Central China Normal University. His research interests include instructional design, online instruction, and learning analytics.