The Impact of Emotion-Based Teaching Strategies on Motivation in Collaborative Learning
Bibliographic record
Abstract
This paper explores the role of EBTS in enhancing motivation within collaborative learning environments in Canada’s multicultural and inclusive educational landscape. Drawing from psychological frameworks such as Deci and Ryan’s SDT and Pekrun’s Control-Value Theory, the study delves into how emotions shape motivation, engagement, and academic success. The paper emphasizes the interplay between positive emotions, such as curiosity and pride, and essential psychological needs, including autonomy, competence, and relatedness, which are critical to sustaining intrinsic motivation. It examines the impact of emotions on group dynamics, highlighting the benefits of fostering trust, empathy, and respect in collaborative settings. Empirical evidence from Canadian classrooms demonstrates the transformative potential of EBTS in promoting student participation, resilience, and inclusivity. The findings underscore the need for integrating emotional engagement into pedagogical practices to enhance both individual and collective learning outcomes. By addressing emotional dimensions, educators can create enriched, culturally responsive, and motivating learning environments that prepare students for academic and interpersonal success.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".