Modeling Emotional Response to Feedback Based on its Affective Focus in STEM E-learning
Bibliographic record
Abstract
Although the relevance of feedback in education has been widely recognized, its effective provision remains as an open challenge, especially with respect to student affect. This challenge is particularly concerning when considering the growing body of literature suggesting the key role emotions play in STEM education. While some studies consider feedback ineffective when affective information is included, others argue the benefits of its inclusion. With the objective of providing a practical basis to model the emotional response of students when receiving feedback in a STEM e-learning environment, a system was designed considering two types of feedback focus: self-personal (SP) and task-impersonal (TI), and five emotional responses (happy, surprised, neutral, embarrassed, and frustrated) for students to indicate how the feedback provided made them feel. The implementation of the system with 37 participants showed that, when receiving feedback after an incorrect answer was chosen, students are significantly more likely to select an embarrassed response in the SP case, and significantly more likely to select a happy and surprised response in the TI case. In addition, a method is proposed to model the subsequent change in emotional state in relation with the correctness of the answer selected and the focus of feedback received. Finally, implications for future automated feedback models are discussed, arguing a need for integrating affect in the design of feedback, especially when the primary objective of the feedback is to correct the student.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".