Obstacles to Promoting Growth Mindset in a Streamed Mathematics Course: “It’s like Confirming They Can’t Make the Cut”
Bibliographic record
Abstract
In this study, we consider the experiences of a professional learning community (PLC) who focused on fostering growth mindset to improve learning in a lower-stream grade 9 mathematics course. Using a sociocultural theoretical lens and drawing on data from a two-year case study, we summarize the ways that PLC members fostered growth mindset and then more deeply explore the obstacles they encountered. Participants found that shifting from a fixed mindset to a growth mindset required changes to the ways they taught mathematics. Even with these changes, shifting mindsets about mathematics learning proved more challenging than they expected. PLC members shared the view that the process was impeded by: students’ ingrained fixed mindsets, views of mathematics as a right or wrong subject, assessment practices focused on grading, the need to overcome their own fixed mindsets, and the streamed nature of the course. Through sustained collaboration, they found ways to begin to address these obstacles, with the notable exception of streaming. This study provides evidence of the ways streaming can inhibit the efficacy of a growth mindset initiative and offers suggestions for teachers, schools, and divisions planning to implement a growth mindset initiative to improve mathematics learning in similar contexts.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".