An Empirical Study of Factors Affecting Mobile Wireless Technology Adoption for Promoting Interactive Lectures in Higher Education
Bibliographic record
Abstract
<p>Use of mobile technology are widespread, particularly among the younger generation. There is a huge potential for utilizing such technology, particularly in lecture sessions with large number of students, serving as an interaction tool between the students and lecturer. The challenge is to identify adoption factors to ensure effective adoption of the technology to promote interactivity between students and lecturer in the classroom. This paper aims to examine factors supporting use of mobile wireless technology during lectures to promote interactivity between students and lecturers in Malaysia’s higher education institutions. Survey involving higher education students in Malaysia was conducted with a sample size of 302. Factor analysis results identified five factors: independent variables System Usefulness (SU), User System Perception (USP), User Uncertainty Avoidance (UUA), System and Information Quality (SIQ), and dependent variable Mobile Wireless Technology Adoption for Interactive Lectures (MWT_AIL). All independent variables are positively associated to MWT_AIL, with UUA and SIQ having higher level of significance compared to SU and USP. Respondents were selected from higher learning institutions from urban areas in Malaysia. Therefore results obtained are not representative of the entire higher education landscape in Malaysia and future studies are warranted to include higher learning institutions located in rural areas. It is hoped that findings from this study will serve as a catalyst for future researches to be conducted in this area, particularly among higher education researchers seeking ways to utilize technology effectively to enhance the learning experiences of the students in the classroom.</p>
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".