Massification in Higher Education: A Systematic Review of its Boundaries, Drivers, and the Role of Critical Pedagogy
Bibliographic record
Abstract
Massification has transformed higher education worldwide, yet the accumulated empirical research on this phenomenon has not been systematically reviewed. This study conducts a systematic review of 28 peer-reviewed empirical articles published between 2011 and 2024, following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines, and using the Mixed Methods Appraisal Tool (MMAT) 2018 framework. The synthesis reveals that: (1) traditional definitions based on Trow’s enrolment thresholds are increasingly inadequate for explaining contemporary dynamics, particularly in contexts of hypermassification; (2) economic, political, social, and cultural drivers interact to expand participation while simultaneously reinforcing inequalities and credential inflation; and (3) critical pedagogy—rooted in Freire and Giroux—offers a transformative framework that democratizes learning, fosters student agency, and promotes equity within massified systems. These findings contribute to a deeper understanding of the complex nature of higher education massification and offer practical implications for achieving equitable massification through critical pedagogy.
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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 | 0.040 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".