Investigating the Impact of an Incremental Mindset Intervention on Students’ Beliefs and Programming Performance
Bibliographic record
Abstract
Implicit theories of personal ability identify two kinds of mindsets, incremental and entity.Individuals with an incremental mindset believe in the value of effort, have masteryoriented goals, and have been associated with higher academic performance.In contrast, entity individuals see effort as evidence of a lack of ability, and have been associated with lower academic performance.Prior research in implicit theories found that incremental beliefs can be taught via mindset interventions, and improvements in academic achievement were associated with these interventions, especially for in face of challenging subjects such as math or physics.Programing has also been identified as a challenging subject for students, but to date little research has been conducted to study the impact that implicit theories might have among novice programmers.To address this gap, this research tests the effect of an incremental mindset intervention on students' beliefs and programming performance in a controlled experiment, as well as analyzes factors related to the mindset construct specifically for the programming domain.Our key results show that as in prior work on general mindset (outside of programming), students who believe programming is a fixed ability do not believe in the value of investing effort.Moreover, compared to the control group, the incremental mindset intervention made participants significantly less entity oriented in their beliefs towards programming, and in turn they exhibited marginally more persistence in their programming activities, especially among those with less prior programming experience.However, this did not translate to improvements in performance on the programming activity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".