Students’ perception towards behavioral intention of audio and video teaching styles: An acceptance study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently audio and video material has been used significantly in various online platforms. The audio-video materials enhance the teaching and learning process by facilitating the transformation of the data and providing a richer interactive environment, hence gaining wide intention within the educational realm. However, empirical studies have not examined the acceptance of the audio and video material depending on a conceptual model where acceptance is the key factor. The present study attempts to overcome this gap in the literature review by investigating the effects of media richness, speed and vividness, perceived concentration, perceived ease of use, perceived usefulness on the acceptance of audio-video material. What distinguishes the current study is the fact that content richness is considered as a mediator that affects all other factors in the conceptual model. The data is collected by distributing the online survey to college students. The results provide mostly insight and support for students’ intention to use audio-visual resources in a conceptual model. The technology characteristics of speed and vividness as well as TAM constructs were significant predictors of technology acceptance. However, it is concluded that the external factor of the perceived concentration has no impact on the students’ perception and intention to use audio-visual resources. In the recommendation, some theoretical and practical implications are stated along with the focus on technology designers, change managers, and users.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it