When Do Learners Study? An Analysis of the Time-of-Day and Weekday-Weekend Usage Patterns of Learning Management Systems from Mobile and Computers in Blended Learning

Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index

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23

Time Factors (Learning); Use Studies; Learning Management Systems; Handheld Devices; Blended Learning; Tablet Computers; Computers; Attendance Patterns; Preferences; Electronic Learning; Learning Analytics; Undergraduate Students; Foreign Countries

1929-7750

Recent advances in smart devices and online technologies have facilitated the emergence of ubiquitous learning environments for participating in different learning activities. This poses an interesting question about modality access, i.e., what students are using each platform for and at what time of day. In this paper, we present a log-based exploratory study on learning management system (LMS) use comparing three different modalities--computer, mobile, and tablet--based on the aspect of time. Our objective is to better understand how and to what extent learning sessions via mobiles and tablets occur at different times throughout the day compared to computer sessions. The complexity of the question is further intensified because learners rarely use a single modality for their learning activities but rather prefer a combination of two or more. Thus, we check the associations between "patterns" of modality usage and time of day as opposed to the "counts" of modality usage and time of day. The results indicate that computer-dominant learners are similar to limited-computer learners in terms of their session-time distribution, while intensive learners show completely different patterns. For all students, sessions on mobile devices are more frequent in the afternoon, while the proportion of computer sessions was higher at night. On comparison of these time-of-day preferences with respect to modalities on weekdays and weekends, they were found consistent for computer-dominant and limited-computer learners only. We demonstrate the implication of this research for enhancing contextual profiling and subsequently improving the personalization of learning systems such that personalized notification systems can be integrated with LMSs to deliver notifications to students at appropriate times.

As Provided

2022

EJ1359202