Integrating ICT Into higher education: onsite and online students’ perceptions in a large vs a small university
Bibliographic record
Abstract
For the past two decades, information and communication technologies (ICT) have transformed the ways professors teach and students learn. This paper aims to compare the perceptions of onsite students (blended mode) and of those taking the same courses on the Internet (online mode) in a large vs a small university. To guide the studies, a moderatortype theoretical research model was developed, out of which eight hypotheses were formulated. The model was tested in a field experiment in two universities located in two different countries in Canada: a large university with an enrollment of 48,000 students and a small university with an enrollment of 4,000 students. To collect data, we used a multimethod approach, that is, a Web survey involving open- and closed-ended questions. The samples were formed of 313 onsite and online students from the large university and of 192 onsite and online students from the small university. The quantitative data analysis was performed using a structural equation modeling software, that is, Partial Least Squares (PLS); the qualitative data were analyzed following a thematic structure using QSR NVivo software. In this paper we present a comparison between the two universities of the quantitative results (closed-ended questions) supported and enriched by the qualitative results of the students (open-ended questions).
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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 | 0.004 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".