Supporting pre‐service teachers’ motivation beliefs and approaches to instruction through an online intervention
Bibliographic record
Abstract
BACKGROUND: Previous research has shown that pre-service teachers have low responsibility for student motivation and feel underprepared to deal with motivational issues. As an extension, researchers have designed interventions to shift teachers' beliefs about motivation or equip them with approaches to instruction, but never both. AIMS: Following best practices for motivation interventions, we created a one-session online intervention and tested its efficacy to shift pre-service teachers' self-reported beliefs and approaches to instruction to be more supportive of student motivation. The intervention included priming, materials designed for mindsets and/or for approaches to instruction, a consolidation activity, and take-home materials. SAMPLE: A convenience sample of 384 pre-service teachers from one Canadian university participated. METHODS: We embedded an experimental design into multiple sections of a required assessment course. After completing a pre-test, participants were randomly assigned to one of four conditions: beliefs-only, approaches-only, combined beliefs and approaches, or control. After completing the online module, students received a lecture on assessment practices and motivation. RESULTS: The results from our MANCOVA showed that pre-service teachers who participated in the beliefs-only condition reported increased levels of responsibility for student motivation, more growth mindset beliefs, and less fixed mindset beliefs than participants in the other conditions. The approaches-only condition did not influence self-reported mastery or performance approaches to instruction and the combined condition had no effect on beliefs or approaches. CONCLUSIONS: We discuss the implications for educating pre-service teachers about student motivation and suggest that beliefs and approaches to instruction need to be addressed separately.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".