Mindsets: Case Studies on Incremental and Entity Theories in Undergraduate Music Students
Bibliographic record
Abstract
American psychologist Carol Dweck coined terms for two dichotomous theories of intelligence––entity and incremental theories––which depict the general trends of learners. Fixed mindset learners internalize entity theories and view their intelligence or ability as a fixed, stable unit that is incapable of growth past the individual’s own genetic limitations. Growth mindset learners believe in incremental theories and see their intelligence as dynamic, wherein their abilities are a direct product of their efforts. Dweck argues that adopting an incremental theory of learning (a growth mindset) can be beneficial to the learning process. Combining this body of psychological literature with auxiliary areas of psychology (e.g. self-determination theory), I conducted two case studies to analyze how these beliefs manifested in second-year instrumental music majors at Canadian post-secondary institutions. Through a six-week research period of one-on-one lessons, independent practice sessions, and interviews, I investigated the participants’ respective upbringings, their methods of framing goals, and their responses to challenges and criticisms with the aim of understanding how music students’ beliefs about mindsets affect their learning, as well as how music teachers may be able to maximize growth-oriented thinking in their interactions with students. After an analysis of the data, I concluded that the nurturing of growth mindsets had a positive effect on the students’ respective levels of patience, self-compassion, and resilience in dealing with the learning challenges inherent in music performance. The data highlighted how the participants’ implicit theories of intelligence were instilled in them by their parents and educators throughout their childhoods, and how growth-minded beliefs may be further nurtured through empathetic and process-oriented guidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".