Student-Facing Educational Dashboard Design for Online Learners
Bibliographic record
Abstract
The current shift from traditional classrooms to online learning in higher education calls for more attention to self-regulated learning. This research is motivated by the growing interest in potential of using learning analytics dashboard (LAD) to increase individuals' self-regulation by creating visibility into their performance in various applications. This study explores how data visualization can be integrated with online learning to improve learners' performance through enhancing their skills in planning and organization. We are working on the design of a comprehensive LAD, focusing on micro-level of learning analytics to support learning activities of students. The LAD includes the following two features to enhance students' self-regulation in online learning: (1) a function to track students' progress compared to other students' over time; (2) reminders to help students with upcoming deadlines and auto-generating to do lists. The hypothesis is that the LAD will increase students' engagement, motivation, and self-regulation in an online learning environment. This study is significant because it contributes to the body of knowledge by exploring how student-generated data can be used to improve self-regulated learning. The practical contribution of this study is to create a personalized LAD for students based on the learner-generated data to benefit students' organization skill, planning skill, and motivation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".