Writing about Goals Enhances Academic Performance and Aids Personal Development
Bibliographic record
Abstract
A renewed interest in goal-setting raises questions on how goal-setting contributes to performance, how goal-setting can be successfully induced, if goals can be effective if they are set only in our minds (versus written out), and how goal-setting relates to academic performance and personal development. However, despite considerable progress in our understanding, we know less about the underlying mechanisms of goal-setting, how goals are turned into effective behaviors, how goal-setting relates to performance, and the best ways to monitor goal- progress. Also, we do not know much about the development of a “goal-setting skill” – if and how people can be taught to set goals and track goal progress. Recent research suggests that writing about goals can enhance study success and that the “power of the pen” can be considerable. The current symposium examines interventions aimed at guided goal reflection via a staged process of 1) reflecting on goal choice and desires; 2) goal formulation; 3) articulation of implementation intentions; and 4) goal monitoring. The symposium examines the effects of these goal-setting interventions with a combination of qualitative and quantitative research, and longitudinal field studies, including over 1,500 students from two countries. These studies consider the roles of the extent of intervention participation, personality, and effort regulation factors, and the longitudinal effects of the interventions on academic performance and personal development. The studies also show that goal setting can be enhanced by an intervention and that, once learned, goal-setting is a key transferable skill, which can enhance employability and performance prospects. Reflective Goal Setting and its Impact on Personal Development Presenter: Cheryl Travers; Loughborough U. A brief goal-setting intervention closes both the gender and minority achievement gap Presenter: Michaéla C. Schippers; Erasmus U. Rotterdam Presenter: Ad Scheepers; Erasmus U. Rotterdam Enhancing Student Retention and Academic Performance: The Effects of Guided Reflection on Goals Presenter: Michaéla C. Schippers; Erasmus U. Rotterdam Presenter: Ad Scheepers; Erasmus U. Rotterdam Presenter: Dominique Morisano; Centre for Addiction and Mental Health / U. of Toronto Presenter: Edwin A. Locke; U. of Maryland Presenter: Jordan Peterson; U. of Toronto Investigating the On-going Impact of a Goal-Setting Intervention Presenter: Cheryl Travers; Loughborough U. Presenter: Raymond Randall; Loughborough U. Presenter: Alistair Cheyne; Loughborough U.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".