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MOOC Learners' Time-Investment Patterns and Temporal-Learning Characteristics

Authors :
Li, Shuang
Wang, Shuang
Du, Junlei
Pei, Yu
Shen, Xinyi
Source :
Journal of Computer Assisted Learning. Feb 2022 38(1):152-166.
Publication Year :
2022

Abstract

Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course design and teaching. However, understanding of how learners invest and manage their time during online learning remains limited. Objectives: This study aims to discover the typical time-investment patterns of MOOC learners and their temporal-learning characteristics based on a systematic time-investment analysis framework and their relationship with learning performance. Methods: Based on a proposed time-investment-analysis framework, this study applied statistical, cluster and lag sequential analyses to investigate learners' time-investment patterns and their relationships with learning performance, session time allocation, and learning sequences by analysing the learning data from 12,463 participants of a Massive Open Online Course (MOOC) in China. Results and Conclusions: Seven time-investment patterns of MOOC learners were defined, and learning performance was found to differ among them. Further analysis shows that high performers invested time throughout the whole course and allocated time to multiple activities, exam-takers performed better in time management and produced more behavioural sequences related to cognitive strategy and recourse use, and learners' motivation and prior knowledge affected the management and effectiveness of their time investment. Implications: The results support the recognition and evaluation of online learning time-investment patterns and suggest relevant cues for improving MOOC design and teaching.

Details

Language :
English
ISSN :
0266-4909
Volume :
38
Issue :
1
Database :
ERIC
Journal :
Journal of Computer Assisted Learning
Publication Type :
Academic Journal
Accession number :
EJ1322825
Document Type :
Journal Articles<br />Reports - Research
Full Text :
https://doi.org/10.1111/jcal.12597