Monitoring Student Cues: Tracking Student Behaviour in Order to Improve Instruction in Higher Education
Bibliographic record
Abstract
In this paper, we focus on monitoring, a particular aspect of reflection related to teaching. We define monitoring as a feedback mechanism which entails attending to and evaluating a multitude of cues in the envi- ronment in order to evaluate progress towards a goal. We direct our attention to monitoring because it is a way in which a teacher is able to gain understanding of how effective his/her teaching actions are. Thus, knowing what cues to evaluate (and being able to do so) is a critical skill in reflection. Further, we focus exclusively in this paper on the concur- rent monitoring of cues related to students since we believe that attention to student cues while teaching provides teachers with a window into their students' learning experiences. We call this particular type of reflection, reflection-in-action. As well as depicting multiple examples of monitoring drawn from our research, we explore the contribution of this work to the literature in higher education and to faculty development activities, particularly, to the growing literature on teacher thinking.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".