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.time, effort, and expertise, which he generously contributed to many of the important stages of my doctoral degree.I am grateful for his many contributions to my program of research, which included access to cutting-edge research methods, human research expertise, and opportunities to meet and, in some cases, collaborate with members of his vast network of international research colleagues.I would also like to acknowledge my current doctoral supervisor,
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".