Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".