Supporting “time awareness” in self-regulated learning: How do students allocate time during exam preparation?

Abstract

The development of technology enables diverse learning experiences nowadays, which shows the importance of learners’ self-regulated skills at the same time. Particularly, the ability to allocate time properly becomes an issue for learners since time is a resource owned by all of them. However, they tend to struggle to manage their time well due to the lack of awareness of its existence. This study, hence, aims to reveal how learners allocate their time and evaluate the effectiveness of the time allocation by examining its effects on learners’ performance. We collect the learning logs of 116 seventh-graders from the online learning system implemented in a Japanese public junior high school. We look at the data in the time window of 34 days before the regular exam. Even though clustering techniques as a Learning Analytics method help identify different groups of learners, it is seldom applied to group students’ learning patterns with different levels of indicators extracted from their learning process data. In this study, we adopt the method to cluster students’ patterns of time allocation and find that better performance can result from the consistency of study time throughout the exam preparation period. Practical suggestions are then proposed for different roles involved in digital learning environments to facilitate students’ time management. Collectively, this study is expected to make contributions to smart learning environments supporting self-regulated learning in the digital era.

Differences in Biology Students’ Self-Regulation During Online and Offline Learning and Its Relationship to Learning Outcomes

“…Additionally, offline learners demonstrate the ability to allocate specific study periods for exam preparation, a practice less common among online learners who may rely on the accessibility of course materials during examinations. However, dedicating dedicated study time prior to exams yields superior performance outcomes (Hsu et al, 2023), highlighting the importance of structured study habits even in online learning contexts. Thus, instilling effective time management practices, including accurate workload estimation and dedicated study time allocation, is essential for optimizing learning outcomes across both online and offline modalities.…”

Data-Driven Support Infrastructure for Iterative Team-Based Learning

“…Additionally, this data can serve as a foundation for creating more advanced evaluation metrics tailored for processbased competency assessments [74]. Reflecting on these performance indicators can motivate students to develop strategies for addressing their weaknesses, actively contributing to their self-directed learning [75]. In TBL, this implementation of formative learning encourages students to apply their acquired knowledge and enhance areas where they may have identified deficiencies during the current round of group learning.…”

Designing data-informed support for building learning habits in the Japanese K12 context

“…For example, they calculated the entropy of the histogram of the learners' activities over time to identify whether their activities were concentrated around a particular hour of the day (P1) or a particular day of the week (P2). In contrast to calculating a value, our previous studies (Hsu et al, 2023a;Hsu et al, 2023b) used clustering analysis and presented the patterns by the clusters such as the learners who studied on weekday mornings (P1 and P2) or the learners who studied consistently throughout exam preparation (P6).…”

“…In our previous studies (Hsu et al, 2023a;Hsu et al, 2024), we explored the methods to extract types and stages of learning habits from log data, which imply learners' temporal affinity and phases of behavior change respectively. This paper further investigates the main research question (RQ): How can data-informed support for building learning habits be designed?…”