Examining the Role of Emotions in Learning with Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many theorists suggest that emotions and learning are highly interconnected, however, research on the impact of emotions is limited. This study explored the emotions of 220 pre-service teachers while they learned new technology tools and the relationship of these emotions with technology experience and preferred learning strategies. Happiness was most often expressed while learning with technology, followed by anxiety, anger and sadness. Technology experience was positively correlated with happiness and negatively correlated with anxiety, sadness and anger. Experimental and authentic learning strategies were positively correlated with happiness and negatively correlated with anger, anxiety and sadness. Direct instruction was positively correlated with happiness, negatively correlated with anger and unrelated to anxiety and sadness. Finally, a social learning strategy was positively correlated with anxiety and unrelated to happiness, anger and sadness. Implications for and for practice and suggestions for future research are discussed.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it