To what extent emotion, cognition and behaviour enhance a student’s engagement: A case study on the MAJA teaching approach
Bibliographic record
Abstract
This empirical article problematises student engagement in today’s higher education system. The objective of this research is to stimulate a student’s behavioural, emotional and cognitive engagement. I employed an inclusive, inductive and reflexive approach and used mixed methods for collecting data from 948 volunteer participants. The preliminary findings illustrate that playing soft or lively music for a few minutes before a class as well as contextualising and delivering course content combined with enrolled students’ background, hobbies and preferences can go a long way in stimulating emotional and cognitive engagements. The findings also reveal that offering chair yoga during mid-term and/or final exam periods as well as encouraging students to hydrate can lead to increasing behavioural adjustments and then in attention and engagement. The results are encapsulated in a novel teaching framework, MAJA (meaning fun in Sanskrit) that stands for: (a) music, (b) anonymous class survey, (c) jest, and (d) aliment. The framework illuminates that when students tangibly sense a connection between a safe and comfortable class environment and course content, their participation increases and absenteeism decreases. They also promote student aspirations and accountability that facilitate critical thinking, an imperative learning outcome in higher education.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".