Does Class Attendance Predict Academic Performance in First Year Psychology Tutorials?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it