Academic Engagement Trajectories and Performance: Learning to Love or Honeymoon Hangover?
Bibliographic record
Abstract
How does students’ academic engagement develop, and how does this development influence academic performance? Process research on students’ early academic experiences has been scarce due to a lack of appropriate high- density-high-frequency research designs. We thus have very limited knowledge on how students become academically engaged and how its development influences their academic success. Drawing on the analogue of workplace commitment, we extracted three process-theoretical accounts regarding how students’ academic engagement might evolve over time: (1) Learning to Love; (2) Honeymoon Hangover; and (3) High, Moderate, or Low Match. We measured 180 students’ weekly levels of engagement across 18 weeks (2778 observations) of post-secondary education. At the end of this first term, we received indicators of objective performance (passed courses and average GPA). Our results confirmed our theoretical trajectories of academic engagement. Moreover, and in line with expectations based on Goal Setting Theory, we found that Learning to Love and High Match students outperformed all other students, whereas Low Match students performed worse than all other students. Our findings illustrate the utility of a more nuanced, temporal approach to the study of engagement, and carry both theoretical and practical implications to understand student development and learning potential.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".