Self-Directed Learning and Psychological Flow Regarding the Differences Among Athletes, Musicians, and Researchers
Bibliographic record
Abstract
Background: Self-directed learning (SDL) most appropriately is learning that is personally selected based on individual values. SDL potentially achieves psychological flow. Flow is an outcome identified and investigated by psychologist Mihaly Csikszentmihalyi. Among those whose flow he studied were individuals who engaged in self-directed careers—athletes, musicians, and researchers. Method: Based on their career self-direction, this investigation compares the reports of athletes, musicians, and researchers of Csikszentmihalyi through a qualitative narrative analysis of his relevant forty-seven-year publication record. The included reports have Csikszentmihalyi as an author, are an analysis of athletes, musicians, or researchers, and mention flow. The lack of an empirical study is the reason for exclusion. Results: The results reveal a significant difference between those who experience flow from a performance of their achieved skills and those who experience flow while learning. This examination of Csikszentmihalyi’s studies regarding athletes and musicians identifies that they are most likely to experience flow during performances of their mastered skills, unlike researchers, whose flow occurs during SDL—a distinction unmentioned by Csikszentmihalyi. Conclusions: Although athletes and musicians may self-direct their careers, only the flow of researchers corresponds with SDL. This result meaningfully extends the literature on SDL and flow, offering direction for future empirical studies and educational opportunities.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".