Ethical use of learning analytics for student support, not surveillance
Bibliographic record
Abstract
The move to online education necessitated by the COVID-19 pandemic has greatly increased institutional use of learning management systems, contributing to vast amounts of educational data, ranging from information on admissions and retention, to the minutiae of course activities. These vast amounts of learner data are collected, measured, analyzed, and reported on to understand learning, learners and the learning environment and can be defined as learning analytics (LA). LA are intended to support students and assist with their success; however, most instructors and students are unaware of how learning analytics can be used in their courses and are consequently unfamiliar with the ethical implications arising from that use. Contributing to this gap is the lack of literature examining the use of LA at the instructor and course level, rather than at the level of the institution. This lack of familiarity with the use of, and ethical principles related to, LA has created, for many faculty, a default to using analytics for performance management, surveillance, and evidence of academic misconduct rather than to support learning. This presentation will address this gap by examining the ethical issues associated with the use of learning analytics specifically for instructors, and provide recommended best practices, resources, and tips to better support students, particularly in online or blended learning contexts. The intent of this research is to provide a guiding framework for the ethical use of LA to promote robust pedagogical practices, transparency between instructors and students so the focus is on academic integrity rather than misconduct.
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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 | 0.104 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.093 |
| Scholarly communication | 0.050 | 0.019 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".