Co-creating Value in Higher Education: The Role of Interactive Classroom Response Technologies
Bibliographic record
Abstract
As competition intensifies, it is essential that higher education providers endeavour to develop and offer high quality, satisfaction-creating service experiences. This requires a comprehensive understanding of the factors that lead to positive perceptions of the institutions services. Current perspectives suggest that the student should be engaged as an active co-producer of the university experience. Interactive classroom technologies may enhance the student experience by encouraging participation. This study examines whether student perceived value, namely social or functional value, satisfaction, and loyalty differs for students participating in a personal response technology enabled classroom experience, versus a more traditional classroom experience. A partial least squares approach was adopted using a sample of 184 students. The use of personal response technology was not found to be positively related to the student experience. In the current context, it appeared to break classroom social patterns resulting in an individualistic, disengaging educational experience. Interestingly, in the traditional, non-technology condition social interaction was enhanced and social value strongly determined students’ perceptions of loyalty. These results suggest that it is the pedagogy, and not the technology that matters in higher education provision. Conclusions, implications and opportunities for future research are presented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".