Constructing Intelligent Learning Dashboard for Online Learners
Bibliographic record
Abstract
This research is motivated by the growing demand for online learning and the potential of using student-facing intelligent learning dashboards (SF-iLDs) to support online learners. SF-iLDs are designed to increase students' self-regulation, engagement, and course performance by creating visibility into their progress in the online courses. Data visualization and predictive modeling techniques are investigated and integrated into the SF-iLD designed in this study. A predictive model based on the learning management system (LMS) data (generated by both instructors and students) is used to extract and analyze valuable insights about learners' progress in the online courses. The data measures students' learning activities, such as grades on quizzes, assignments, exams, the number of logins, access to the course materials, and the overall course grade. These features are used to classify the learners into three groups: Persistent, Regular, and Irregular. Using this model, the course outcome and the learning gain can be predicted for the students based on their time management and performance in the course activities and assessments. Furthermore, data visualization in the SF-iLD enables students to track their performance in the course, which helps students to better understand their self-regulation ability in the online courses, which potentially influences their self-efficacy and performance in their courses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".