Understanding Student Absenteeism in Undergraduate Engineering Programmes
Bibliographic record
Abstract
CONTEXT Correlation between student attendance and eventual performance is well documented in the literature. Even with increasing access to compatible web-based resources or lecture recordings, traditional face-to-face classroom lectures are considered better at engaging the students with the content. Successful students are well aware of the importance of attendance. Yet absences are common in engineering lectures. Often a quarter of the students do not attend lectures. Frequent absences often result in subsequent academic hardship. \nPURPOSE While the relationship between attendance and performance is known, the drivers for the individual absences are either unknown or varied, and thus challenging to address. This study aims to understand the causes for absences in the hope to develop an evidence-based model for strengthening student attendance. \nAPPROACH A survey was developed to highlight common reasons for lecture absences and to capture ways students make up for their absences. The survey was administered on students taking core engineering courses at Auckland University of Technology, University of Waikato and University of Queensland. The overall themes from the survey are summarized and discussed. \nRESULTS The survey results agree with anecdotal attendance rates observed by faculty. Many factors influence student absenteeism. The responses reflect the challenge students face in balancing study, family life, and financial commitments. An additional layer of complexity was noted by the availability of recorded lectures. \nCONCLUSIONS Recognizing the various attitudes towards lectures and the varied reasons for lecture absences can yield a powerful mitigation tool. While the results highlight some drivers for absences that are difficult to easily address by a course instructor, the survey does provide insights on areas where instructors may be able to make a notable impact towards student engagement.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".