Från whiteboard till pekskärm : En studie av universitetslärares upplevelser av interaktiva klassrum
Bibliographic record
Abstract
Information technology (IT) have for several decades been used in university education. An increasing number of classrooms today are built around a concept which uses IT in collaboration with the room itself. However, little is known about the experience of university teachers when working in such classrooms. This study examines the views and opinions of teachers at a Swedish university regarding using and interacting with these classrooms. Furthermore, we identify possible underlying factors that influence these views. Using data from qualitative interviews we apply Technology acceptance model (TAM) and Activity theory (AT) used in both education and human computer interaction to identify how different factors interact to form these opinions. Our study finds that teachers experience a lack of proper training in the use of classrooms as a concept and tend to stay in established norms of how education is to be conducted. These results leads to questions whether education in the use of these classrooms is adequate for teachers or if education needs to focus more on outcomes of the concept and changing established norms rather than to focus on the use of technology. Our study also shows that teachers do not view the classrooms as a whole where artefacts enable and form each other. Rather they view the physical room, the technology and themselves as separate entities that operate separately from each other.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".