Does Class Attendance Predict Academic Performance in First Year Psychology Tutorials?
Bibliographic record
Abstract
Student absenteeism is common across universities. Learning through attending lectures and tutorials is still expected in our technological age, though there are major changes in how information in lectures and tutorials can be transmitted via the use of iLearn and related packages, by video streaming of classes and by online technology generally. Consequently, availability of these supplementary resources and, in general terms, the issue of physical absence from classes, raises the question of whether missing class impacts on student learning. Does it matter if students attend classes or not? The aim of the current study was to assess whether student attendance in tutorials in first year subjects in psychology was associated with academic performance, that is, was attendance linked with improved performance? We took data from tutor held records on attendance and on results for article review assignments and laboratory reports for a total of 383 students who completed introductory psychology courses in classes over the years 2012-2015. The hypothesis that class attendance and performance would be significantly related was supported in 13 of the 14 class relationships examined separately, and, in the class that was the exception the correlation was in the expected direction. These results suggest that attending class continues to have a positive impact on student learning in this technological age. The limitations of the current study are discussed as are implications regarding instructor resource applications and/or compulsory class attendance policies.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".