The Impediments to Student Engagement: A hybrid Method Based on Fuzzy Delphi and Fuzzy DEMATEL
Bibliographic record
Abstract
Student engagement is one of the most important determinants of learning outcomes in higher education institutions. This paper focuses on impediments to student engagement. The Fuzzy Delphi technique was used to screen and elicit important impediments. Four main criteria (individual, family, institution, and environment) and Twenty three sub-criteria were selected by experts through the fuzzy Delphi technique. The fuzzy DEMATEL technique was used to determine the causal relationships among the criteria (impediments). Findings showed that institutional, environmental, and family factors were in the cause group, individual, and family factors were in the effect group. Among the 23 sub-factors, eleven factors were in the cause group and twelve factors were in the effect group. The first three influencing factors were: teachers' poor quality of teaching, inadequate facilities of classroom and institution, and non-applicable materials and curriculum. The first three influenced factors were: poor quality of the relationship between teachers and students, financial problems and high tuition fees, and decreasing the value and status of education in the society.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".