A case study of integrating Interwise: Interaction, internet self-efficacy, and satisfaction in synchronous online learning environments
Bibliographic record
Abstract
This paper reports research on the implementation of a web-based videoconferencing tool (Interwise) for synchronous learning sessions on an industrial technology course offered through a university in northern Taiwan. The participants included undergraduate students from the same course offered in two different semesters. We investigated students' perceptions of interactions with the instructor and fellow students, their confidence in utilizing the Internet (Internet self-efficacy), and the satisfaction level that students perceived throughout the learning process with Interwise. We also examined the effect of interactions and Internet self-efficacy on student satisfaction. Data collected through paper-based and online surveys were analyzed using correlation and multiple regression. The results revealed that overall, learners perceived Interwise as a tool that was moderately easy to use for synchronous learning. Learners seemed to prefer using the Interwise features, such as emotion icons, talk, or raise hand, to interact with their instructor. Learners had high confidence in gathering data or getting support through the Internet, but low confidence in resolving Internet related problems. Both learner-learner and learner-instructor interactions were significant predictors of student satisfaction, while Internet self-efficacy did not significantly contribute to satisfaction. Learner-instructor interaction was found to be the strongest predictor of student satisfaction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".