Writing about personal goals and plans regardless of goal type boosts academic performance
Bibliographic record
Abstract
Academic underachievement is a problem for both our education system and general society. Setting personal goals has the potential to impact academic performance, as many students realize through reflection that studying is a path towards realizing important life goals. Consequently, the potential impact of a brief (4–6 h), written, and staged personal goal-setting intervention on undergraduate academic performance (earned European Credit Transfer and Accumulation System credits) was investigated. Using a time-lagged quasi-experimental design, our model was tested with two first-year university goal-setting cohorts and two control cohorts (total n = 2928). The goal-setting cohorts (n = 698 and 711) showed a 22% increase in academic performance versus the control cohorts (n = 810 and 707). This increase depended on (1) the extent of participation in the 3-stage goal-setting intervention, (2) number of words written in the exercise, and (3) the specificity of students’ goal-achievement plans (GAP). Contrary to goal-setting theory, which necessitates goal-task specificity, the results revealed that it did not matter whether the students wrote about academic or non-academic goals, or a combination of both. Rather, it appeared to be the overall process of writing about their personal goals, the specificity of their strategies for goal attainment, and the extent of their participation in the intervention that led to an increase in their academic performance. This study suggests an important modification to goal-setting theory, namely a potential contagion effect of setting life goals, an academic goal primed in the subconscious, and subsequent academic performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".