Learning Anytime, Anywhere: A Spatio-Temporal Analysis for Online Learning
Document Type
Article
Publication Date
2-2022
Abstract
The study proposes two new measures, time and location entropy, to depict students’ physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been touted as one of the most attractive features of online learning, the question remains as to the success of students when engaging in online courses through disparate locations and points-in-time. The procedures describe an analysis of 5293 students’ spatio-temporal patterns using metadata relating to place and time of access. Grouping into segments that describe their patterns of engagement, results indicate that the high location-high time entropy (i.e. multiple times, multiple locations) was the segment with lowest success when compared with other students. Statistical and modeling results also found that female students tended to learn at fixed or few locations resulting in the highest performance scores on the final exam. The primary implication is that female students tend to be successful because they study in fewer locations, and all students who study at consistent times outperform those with more varied time patterns. Existing brain research supports the findings on gender differences in learning performance and spatio-temporal characteristics.
Publication Information
Du, Xu; Zhang, Mingyan; Shelton, Brett E.; and Hung, Jui-Long. (2022). "Learning Anytime, Anywhere: A Spatio-Temporal Analysis for Online Learning". Interactive Learning Environments, 30(1), 34-48. https://doi.org/10.1080/10494820.2019.1633546