Flipped Labs as a Smart ICT Innovation: Modeling Its Diffusion among Interinfluencing Potential Adopters.
Bibliographic record
Abstract
Smart ICT innovation like flipped classroom pedagogy is freeing up face-to-face in-class teaching system for additional problem based learning activities in the class. But the focus of flipped classrooms is more on the theory side with related lab work in science subjects further getting marginalized. In this paper we are proposing Flipped Labs - a method of pedagogy premeditated as a comprehensive online lab learning environment outside the class room by means of tutorials, theory, procedure, animations and videos. Flipped labs have the potential to transform the traditional methods of lab teaching by providing more lab time to students. An ICT educational innovation like flipped labs will not occur in isolation in an environment where two interrelated potential adopters namely teachers and students influence each other and both have to adopt for the innovation to be successful. In this paper we provide the theoretical framework for the diffusion and the adoption patterns for flipped labs using theory of perceived attributes and take into account the important intergroup influence between teachers and students. The results of this analysis indicated that Relative Advantage, Compatibility, Ease of Use, Teacher Influence and Student Influence were found to be positively related to acceptance of flipped labs.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".