Bibliographic record
Abstract
Learning science is enthusiastically adopting new instruments to gather physiological and other forms of event data to represent mental states and series of them that reflect processes. In an attempt to provoke more thought about this kind of research, I suggest paradigmatic issues relating to data, analyses of them and interpretations of results. I advocate we not label these data as “objective.” Instead, we share a subjective interpretation of them. I argue propositions about validity need more nuance. Bounds on generalization related to so-called ecological validity are rarely empirically justified. When researchers transform raw data before analysis and when analytic methods partition variance, interpretations of results omit key qualifications. I posit emotion and motivation be positioned in theory as moderators rather than mediators because agentic, self-regulating learners make and revise knowledge by choosing forms of cognitive engagement in a context where they interpret arousal. I note that researchers’ anchor interpretations of process data in learners’ accounts. This creates a tautology that troubles usual notions of reliability. Finally, I recommend research involving process data turn more toward helping learners identify conditions of learning that spark arousal so learners can regulate motivation and emotion. This leads to a surprise: Treating learners as individuals and helping them identify triggers of arousal may recommend learning science cast emotions and motivation as epiphenomena.
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.
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 | 0.613 | 0.619 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.008 | 0.092 |
| Scholarly communication | 0.028 | 0.040 |
| Open science | 0.015 | 0.013 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".