Facial expressions when learning with a Queer History App: Application of the Control Value Theory of Achievement Emotions
Bibliographic record
Abstract
Abstract Learning analytics (LA) incorporates analyzing cognitive, social and emotional processes in learning scenarios to make informed decisions regarding instructional design and delivery. Research has highlighted important roles that emotions play in learning. We have extended this field of research by exploring the role of emotions in a relatively uncommon learning scenario: learning about queer history with a multimedia mobile app. Specifically, we used an automatic facial recognition software (FaceReader 7) to measure learners’ discrete emotions and a counter‐balanced multiple‐choice quiz to assess learning. We also used an eye tracker (EyeLink 1000) to identify the emotions learners experienced while they read specific content, as opposed to the emotions they experienced over the course of the entire learning session. A total of 33 out of 57 of the learners’ data were eligible to be analyzed. Results revealed that learners expressed more negative‐activating emotions (ie, anger, anxiety) and negative‐deactivating emotions (ie, sadness) than positive‐activating emotions (ie, happiness). Learners with an angry emotion profile had the highest learning gains. The importance of examining typically undesirable emotions in learning, such as anger, is discussed using the control‐value theory of achievement emotions. Further, this study describes a multimodal methodology to integrate behavioral trace data into learning analytics research.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".