Bibliographic record
Abstract
Mindset theory is an achievement motivation theory that centers on the concept of the malleability of abilities. According to mindset theory, students tend to have either a growth mindset or a fixed mindset about their intelligence; students with a growth mindset tend to believe that intelligence is malleable, whereas students with fixed mindsets tend to believe that intelligence is unchangeable. As described in many empirical and theoretical papers, the mindset a student holds can influence important psychological and behavioral factors, including reaction to failure, persistence and level of effort, and expectations of success, which ultimately impact academic achievement. Importantly, mindsets can be changed, and interventions have been developed to promote a more growth mindset. A growth mindset allows students to view challenges as an opportunity for improvement, is linked to enjoyment of learning, and increases motivation in school. School psychologists are often working with students with learning differences and/or mental health concerns who are particularly at-risk for poor academic achievement, and researchers have demonstrated the important impact a growth mindset can have for these vulnerable students. School psychologists are well-positioned to incorporate mindset theory into the school environment in order to best support the students they serve. In this paper we provide a theoretical overview of mindset theory and mindset interventions, and specifically review the literature on mindset theory for individuals with learning disabilities and mental health challenges. We discuss how school psychologists can incorporate mindset theory into their practice to support the shift from a fixed to a growth mindset for all students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".