MOOC learners' time‐investment patterns and temporal‐learning characteristics.

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. Lay Description: What is currently known about the subject matter: Insufficient learning time affects learning persistence and effectiveness in open and online education programs.Learning time investment and management regarded as an element of self‐regulated learning is a predictor of learning performance.Some proxy behavioural variables or characteristics of online learning time management have been found in previous studies.Learning analysis can be used to dig the knowledge implicit in learning data for online teaching evaluation and improvement. What is paper adds to the subject: Based on a comprehensive analytic framework, seven time‐investment patterns of MOOC learners and their relationship with achievement are identified.Under different time frames, learners' time allocation and learning behaviour pattern are different.Learners performing better in time management across course learning allocate more time to multiple activities in session learning.Exam‐takers perform better in time management across MOOC learning, and produce more behaviour sequences related to cognitive strategy and course recourse use. The implications of study findings for practitioners: The recognition of time investment pattern can support online course design and teaching improvement.MOOC design and teaching need to consider the differences in learners' time resources and their ability of time management.MOOC design and teaching need to encourage learners lacking of performance motivation to engage in various learning activities instead of just watching videos.MOOC design need consider the differences in learners' prior knowledge, and offer personalized learning path based on assessment. [ABSTRACT FROM AUTHOR]