Understanding Emotional Adaptivity in Study Tools: A Mixed-Methods Evaluation of Eunoia
Bibliographic record
Abstract
Productively tools are increasingly common amongst students. However, these applications do not take into account the impact of emotion on the cognitive characteristics of attention and motivation. Eunoia was developed for this purpose - it is a study assistant that consists of an emotion-adaptive interface which changes the feedback accordingly. This study investigated 17 participants in a within-subjects design, using objective and subjective measures. The findings of this study demonstrated that emotion-adaptive features as a result of self-reported moods were more likely to improve attention and motivation compared to auto-detect emotion and standard study time conditions. Furthermore, Participants seemed to prefer the self-report mode as it provided them with autonomy and control, along with accuracy. Although the auto-detect method was considered helpful, it did not feel trustworthy to a majority of participants. These results indicate that user agency and transparency in affective systems are crucial along with demonstrating the potential of these interfaces in everyday studying contexts.
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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 | 0.050 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".