INFORMATION VISUALISATION IN EDUCATION: A REVIEW OF CURRENT TOOLS AND PRACTICES
Bibliographic record
Abstract
Faculties at post-secondary institutions have employed systems to gather vast amounts of assessment data with the ultimate goal of using the data to improve the student learning experience. Unfortunately, the largeamount of data and complex relationships make it difficult for instructors and administrators to interpret and enact program and policy changes based on it. Worse yet, this is still a nascent problem and there are few supports upon which they may lean. Information Visualization (IV) isone technique that has shown promise in facilitating the extraction of meaningful information, and a tool that can support a faculty’s extraction and application processes would be highly beneficial.This paper looks at the current work of IV in education. We look at what applications these visualizations are being used in, what considerations went into designing them, and how they were evaluated. This information will hopefully provide insight into how to build visualizations for a variety of other educational contexts.
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How this classification was reachedexpand
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".