Motivation From a Self-Regulated Learning Perspective: Application to School Psychology
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
Self-regulation of learning involves developing metacognitive awareness (planning, monitoring, and evaluating) of (a) cognition—motivational beliefs, (b) behaviors—persistence, effort, engagement, and (c) affect—enjoyment, interest, and other emotions. Metacognitive awareness creates opportunities to exert metacognitive control as needed, which may involve sustaining or manipulating motivational cognition, behavior, and affect. By adopting a self-regulation perspective, this paper discusses the ways motivation develops within and across academic tasks and situations, as well as the ways learners can be supported to take control of their motivation in those contexts. Applying self-regulation principles in the practice of School Psychology means to consider the role of situation, context, and learners’ socio-historical experiences while empowering learners to focus attention on things they can control.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it