The Impact of Internet Experience and Attitude on Student Preference for Blended Learning
Bibliographic record
Abstract
The purpose of this study is to investigate student experience with the Internet, and their attitudes towards using it, inan attempt to determine the impact of these experiences and attitudes on their view of the implementation of blendedlearning. Data from 142 Saudi students at a leading university in Saudi Arabia were collected via an onlinequestionnaire. The results reveal that students have both experience with and positive attitudes towards using theInternet. Demographic variables had no effect on these attitudes, but experience variables showed significant effects.Interestingly, there were mixed interactions regarding student study year; negatively with Internet experience andpositively with preference for the implementation of blended learning. Neither experience with the Internet norprogram of study appeared to influence student preference for blended learning but age, study year, and attitudestowards the Internet were associated with positive attitudes towards blended learning. Importantly, students in thepresent study supported the implementation of blended learning, but not entirely online learning. Student attitudestowards the Internet in general appeared to influence their attitude to learning approaches that use the Internet inblended learning environments. Discussion of these results is presented with suggested implications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".