Self‐reflection, growth goals, and academic outcomes: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Goal-setting theory continues to be among the most popular and influential theories of motivation and performance, although there have been limited academic applications relative to applications in other domains, such as organizational psychology. AIMS: This paper summarizes existing quantitative research and then employs a qualitative approach to exploring academic growth via an in-depth reflective growth goal-setting methodology. SAMPLE: The study focuses on 92 UK final-year students enrolled in an elective advanced interpersonal skills and personal development module, with self-reflection and growth goal setting at its core. METHOD: Qualitative data in the form of regular reflective written diary entries and qualitative questionnaires were collected from students during, on completion of, and 6 months following the personal growth goal-setting programme. RESULTS: About 20% of students' self-set growth goals directly related to academic growth and performance; students reported that these had a strong impact on their achievement both during and following the reflective programme. Growth goals that were indirectly related to achievement (e.g., stress management) appeared to positively impact academic growth and other outcomes (e.g., well-being). A follow-up survey revealed that growth goal setting continued to impact academic growth factors (e.g., self-efficacy, academic performance) beyond the reflective programme itself. CONCLUSIONS: Academic growth can result from both academically direct and indirect growth goals, and growth goal setting appears to be aided by the process of simultaneous growth reflection. The implications for promoting academic growth via this unique learning and development approach are discussed.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".