AdVizor: Using Visual Explanations to Guide Data-Driven Student Advising
Bibliographic record
Abstract
Academic advising can positively impact struggling students’ success. We developed AdVizor, a data-driven learning analytics tool for academic risk prediction for advisors. Our system is equipped with a random forest model for grade prediction probabilities uses a visualization dashboard to allows advisors to interpret model predictions. We evaluated our system in mock advising sessions with academic advisors and undergraduate students at our university. Results show that the system can easily integrate into the existing advising workflow, and visualizations of model outputs can be learned through short training sessions. AdVizor supports and complements the existing expertise of the advisor while helping to facilitate advisor-student discussion and analysis. Advisors found the system assisted them in guiding student course selection for the upcoming semester. It allowed them to guide students to prioritize the most critical and impactful courses. Both advisors and students perceived the system positively and were interested in using the system in the future. Our results encourage the development of intelligent advising systems in higher education, catered for advisors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".