Digital Dashboards for Summative Assessment and Indicators Misinterpretation: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the last decade, teachers in France have been increasingly pressured to use digital learning environments, and to shift from grade-based to skill-based assessment. Educational dashboards, which measure student input electronically, could foster such a transition by providing insights into learners’ performances. However, such dashboards could also foster data misinterpretation during the summative assessment process, should the indicators that they display be used without a proper understanding of what they reflect. This article presents a methodology to detect potential mistakes in the interpretation of the indicators in the context of inquiry-based learning. During the design of a learning environment, we analyzed, through analytics and classroom observations in primary and middle schools, the issues that could arise from the use of a dashboard. Our data suggest that the amount of information practitioners needed to collect to make indicators relevant was burdensome, making the dashboard unfit for assessment purposes at the scale of a classroom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it