Conceptualising and Measuring Student Disengagement in Higher Education: A Synthesis of the Literature
Bibliographic record
Abstract
Much has been written about why students engage in academic studies at university, with less attention given to the concept of disengagement. Understanding the risks and factors associated with student disengagement from learning provides opportunities for targeted remediation. The aims of this review were to 1) explore how student disengagement has been conceptualised, 2) identify factors associated with disengagement and 3) identify measureable indicators of disengagement in previous literature. A systematic search was conducted across relevant databases and key websites. Reference lists of included papers were screened for additional publications. Studies and national published survey data were included if they addressed issues pertaining to student disengagement with learning or the academic environment, were in full text and in English. In the 32 papers that met the inclusion criteria, student disengagement was conceptualised as a multi-faceted, complex yet fluid state that has a combination of behavioural, emotional and cognitive domains influenced by intrinsic (psychological factors, low motivation, inadequate preparation for higher education and unmet or unrealistic expectations) or extrinsic (competing demands, institutional structure and processes, teaching quality and online teaching and learning). A number of measurable indicators of disengagement were synthesised from the literature including those that were self-reported by students and those collected by an institution. An examination of the conceptualisation, influences and indicators of disengagement could inform intervention programs to ameliorate the consequences of disengagement for students and academic institutions.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".