Monitoring Student Cues: Tracking Student Behaviour in Order to Improve Instruction in Higher Education
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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