Conceptualising and Measuring Student Disengagement in Higher Education: A Synthesis of the Literature
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
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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