Utilizing Students' Interests to Support Engagement and Intrinsic Motivation
Bibliographic record
Abstract
The following qualitative research study examines the question: How do a small sample of elementary school teachers draw on the interests of their students to increase intrinsic motivation and what outcomes have they observed from students as a result? To investigate this question, data was collected through semi-structured interviews with one Registered Early Childhood Educator and two Ontario Certified Teachers who utilize intrinsic motivation in their classrooms. All participants that were interviewed for this research study were contacted through convenient sampling within the Greater Toronto Area, where transcripts from these interviews were analyzed numerous times in order for central themes to emerge. The overall four themes that were most influential within the transcripts were: 1) How teachers learn about students’ interests, 2) Challenges with using students’ interests, 3) Indicators of student interest, and 4) Gender, interest, and intrinsic motivation. Implications for the education community and personal practice are then discussed in addition to some recommendations for educators to increase intrinsic motivation outcomes within the classroom.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".