Beyond Grade Point Average of University Students: Theoretical Foundations for the Achievement as a Process Approach with an Empirical Illustration
Bibliographic record
Abstract
Psychological research in tertiary education typically follows the achievement as an outcome approach in which the focus is placed on inter-individual differences in the achievement level of students (e.g., semester GPA, cumulative GPA). In this article, the achievement as a process approach is proposed to reconceptualise academic achievement as a developmental story with far reaching consequences. Three principles are articulated to posit that the trajectory of academic achievement differ across students (principle #1) and that such inter-individual differences are consequential (principle #2) and far reaching (principle #3) predictors of long-term success of students (e.g., retention, timely graduation). An empirical illustration is presented and results of growth curve analyses indicate that (a) an achievement shock during the first year, (b) a bounce back effect during the second year, and (c) continuous improvement during the junior and senior years improves our capacity to predict long-term success of university students and outperforms the typical predictors used by universities. This new approach has far reaching consequences for the management, services, policies, and research agenda of people working to promote the success of students. Six broad implications are delineated to steer research and practices in novel, needed, and promising directions across tertiary education and beyond.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".