Measuring emotions with an agent-based learning environment
Bibliographic record
Abstract
Learning and emotions are inextricably connected, but our scientific understanding of their relationship is largely limited by the trait-like use of self-report methods that still dominate the measurement of emotions.This methodological tradition is incongruous with the nature of emotions which are dynamic, rapidly changing, multi-componential, goal-related psychological processes.Moreover, as advanced learning environments (e.g., computer-based learning environments) continue to evolve in complexity, richer data from learners' interactions with them is needed than global self-reports.One solution is a broad methodological approach to measuring emotions that captures how emotions change over the course of a learning session, what information is contributed by different emotional components (behavioral, experiential, physiological), how learners feel about important aspects of the environments they are interacting with, and how the emotions they experience while interacting with new technology compare to their typical academic achievement emotions.This dissertation addresses these research questions in the context of an advanced, agent-based learning environment, referred to as MetaTutor, and in doing so attempts to provide theoretical, conceptual, and methodological contributions to the fields of psychology, education, and computer science.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".