Practical Visualization of Learning and Teaching Behaviour Patterns Using Moodle Blocks
Bibliographic record
Abstract
Dashboards that illustrate the behaviour patterns of students and/or instructors can be enormously valuable for students and instructors. They can be used to promote self-reflection or support monitoring student activity. However, many dashboards simply display available data without considering whether this data is useful or supports learning success. In contrast, this research established useful visualizations based on underlying behavioural theories, developing three Moodle blocks as dashboards to explore the question: How to provide useful and understandable information about student and instructor behaviours in the form of instructor and student dashboards? While the focus of this paper is on the dashboard designs, this research is part of a larger project that uses artificial intelligence to (1) verify and depict the usefulness of the information presented with respect to its impact on student success, failure or dropout likelihood and (2) recommend alternative behaviour patterns for students and instructors to increase student success. The resulting dashboards are useful tools that allow students and instructors to self-monitor, encouraging behaviour changes that could lead to greater learning success.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".