Associations between Students’ Standing Seen in Learning Analytics Dashboards and Their Following Learning Behaviours:
Bibliographic record
Abstract
An essential part of making dashboards more effective in motivating students and leading to desirable behavioural change is knowing what information to communicate to the student and how to frame and present it. Most of the research studying dashboards' impact on learning analyzes learning indicators of students as a group. Understanding how a student's learning unfolds after viewing the dashboard is necessary for personalized dashboard selection and its content. In the context of the discussion activity, we analyzed 28,290 actions of 896 students after they saw their learning status on the dashboards, which were integrated into 21 discussions in 11 courses. We provide a comparative perspective on three dashboard types: the class average, the leaderboard, and message-quality dashboards. Our results indicate that students' behaviours after viewing three dashboards were associated with their displayed standing in the discussion: views showing the student's status below the frame of reference were associated with a higher likelihood of posting, and views of the student outperforming the norm with diminished further posting, although demonstrating higher discussion engagement. We reiterate a need to understand the impact of dashboard states on students' behaviour, creating a foundation for a personalized selection of dashboard views based on individual students' standing.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".