Exploring the Lived Experiences of Chronic Absenteeism Among Undergraduate Students at a Historically Black University: An Interpretative Phenomenological Analysis
Bibliographic record
Abstract
This research examines chronic absenteeism at an American public, historically Black university. Chronic absenteeism, which became a problem during and following the COVID-19 pandemic, has contributed to rising failure rates and a 34% graduation-to-retention rate among students. Using the interpretative phenomenological analysis (IPA) method, the authors, who are college instructors, interviewed 13 students with histories of absenteeism. Student cases were analyzed separately and together to identify relevant absenteeism themes. Significant themes include life conflicts (such as a flat tire or illness), lack of perceived relevance of courses (such as courses that fall outside of a student’s major), and lack of course structure (such as inconsistent policies within a course). These barriers impact all students to some degree, and these obstacles have become more frequent and substantial post-COVID-19. The barriers are described using examples and recommendations, to mitigate the problems that include, among others, clear course policies of student support in syllabi. During course introductions, course time devoted to making clear, the items that are relevant to the students’ academic majors, and care taken to ensure correspondence between what is in the course schedule and what happens in class.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".