How Motivation Influences Student Engagement: A Qualitative Case Study
Bibliographic record
Abstract
The authors use Ryan and Deci’s (2000) Self-Determination Theory (SDT) to better understand how student motivation and engagement are linked combined with Schlechty’s Student Engagement Continuum to analyse the impact of intrinsic and extrinsic motivation on students’ different engagement types. The study seeks to understand which type of motivation – intrinsic or extrinsic – is more closely aligned to authentic student engagement as identified by Schlechty (2002, 2011). A qualitative research framework was adopted and data was collected from one elementary school class. According to Ryan and Deci’s SDT, the majority of students who indicated that their motivation type was either intrinsic or integrated regulated motivation also demonstrated that they were authentically engaged in their education (Schlechty, 2002, 2011). The students who preferred extrinsic motivation also showed ritual and retreatist forms of engagement and students demonstrating both intrinsic and extrinsic motivation showed authentic, ritual, retreatist and rebellious engagement. In line with findings by Zyngier (2008) in this particular study at least, when pedagogical reciprocity (Zyngier, 2011) was present, intrinsic motivation assisted authentic student engagement in learning, and that extrinsic motivation served to develop ritual engagement in students however, students who had both types of motivation showed different types of engagement in their learning.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".