Factors Influencing Media Choice For Interact With Their Students among Lectures of Two Academic Institutions: Case of Iran
Bibliographic record
Abstract
The aim of this paper is to report the findings on the impact of communication technology as channels on interaction between academic staff and their students, conducted at an Iranian higher learning institution. study focused on media choice and it attempted to determine the communication channels most used by academic staffs in interacting with their students and the reasons they chose these channels. It also intended to find out whether there was a significant relationship between communication channels most used by academic staffs and their perception of media richness. The results revealed that though there is the existence of new communication technologies such as the internet which offers faster and cheaper facilities, Face-to-Face communication is the most used and preferred communication channel by academic staffs in interacting with their students. In addition there was significant relationship between communication channel most used by respondents and their perception of media richness and social presence. That’s why with the higher level of social presence, their level of experience with a channel increases. The findings of this study extend two of the most widely investigated media choice theories: Media Richness Theory (MRT) and Social Presence Theory (SPT).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".