Is Showing Up Half the Work? The Relationship among Student Attendance, Engagement and Test Scores
Bibliographic record
Abstract
Differentiating engagement from attendance is important for understanding predictors of academic achievement. In 613 students, engagement was psychometrically operationalized, whereas attendance was defined as physical presence in the classroom. Achievement was operationalized as exam scores. Significant correlations emerged between engagement and achievement (.26–.43), and attendance and achievement (.25–.40). Correlation coefficients increased in the tails of the distribution (.35–.62). Engagement explained an additional 6–10% of the variance in achievement (22–38%) compared to attendance (16–28%). Students who never attended class scored in the failing range on the final exam. In contrast, students who attended every class scored 20% higher on the same exam. Students with perfect attendance and perfect engagement scores outperformed students with perfect attendance but less than perfect engagement on exams. Perfect engagement provided a relative advantage of 0.33–0.44 Cohen’s d units above and beyond perfect attendance. Since attendance alone fails to capture essential aspects of student behavior that predict academic achievement, developing instruments that measure the quality of engagement has the potential to capture additional variance in student participation. Making the difference between attendance and engagement explicit to students may have pedagogical value.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".