An Integrative Perspective for Studying Motivation in Relation to Engagement and Learning
Bibliographic record
Abstract
Several decades of research highlight the benefits of various motivational beliefs (e.g., perceived competence, achievement goals, task value) in supporting students’ learning and engagement. Much of this research utilizes a variable-focused approach, examining how different forms of motivation uniquely and independently predict educational outcomes. In contrast, a person-oriented approach allows one to examine how motivational processes combine to shape academic engagement and achievement. Person-oriented approaches are especially promising in that they allow one to simultaneously consider variations in several motivational indicators to better understand the multiple ways that students utilize motivational resources to support engagement and achievement. This chapter presents an integrative, person-oriented approach to studying student motivation. Specifically, the approach (1) draws from multiple theoretical perspectives to operationalize motivation, and (2) utilizes person-oriented analyses to model how motivational components combine to shape learning and engagement. Based on prior research and our own work, preliminary conclusions regarding what motivates students and which combinations of motivation are most and least adaptive are discussed. Implications for translating integrative research into effective classroom practices to support student motivation are considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".