Investigating the effect of emotional tone on learners’ reading engagement and peer acknowledgement in social annotation
Bibliographic record
Abstract
Social annotation fosters collaborative learning by encouraging knowledge sharing and a community of inquiry. However, research has primarily focused on the cognitive aspect of social annotation. This study aims to contribute an emotional perspective to the existing literature on social annotation. Specifically, we used the valence-aware dictionary for sentiment reasoning algorithm to measure students’ emotional tones in 1,954 comments posted during social annotation. We then utilised linear mixed-effect models to examine the effect of emotional tone on students’ reading engagement and peer acknowledgement, respectively. Our findings indicate that students who posted more positive sentiment comments were more likely to spend more time engaging in social annotation and receive peer acknowledgement. These findings offer insights into the significance of emotional tone in social annotation and the design of scaffolding strategies to foster positive emotional tone. Implications for practice or policy: Undergraduates’ peer acknowledgement can be enhanced by positive emotional tone in social annotation. Undergraduates engage more in active reading when their written comment expressed more positive sentiment in social annotation. Instructional designers and researchers can use sentiment analysis as an analytic approach to evaluate learners’ written texts for promoting peer interaction and reading engagement. Instructors and educators should consider understanding and monitoring the emotional tones students convey in their social annotations.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".