The impact of digital teaching materials on educational engagement and outcomes in science education: The mediating role of technology integration an empirical analysis of private universities in Jordan
Bibliographic record
Abstract
This study aims to evaluate an intervention that moved beyond the dimensions of digital information application (DTMs) in science education in higher education. Using an integrated methodology, there is a large and growing body of evidence for the prominence placed on both perceived experience and quality tutors in science courses in educating institutions with broader ambivalence toward limited Digital Literacy. The model and Hypotheses were tested with data collected from 158 participants. Our results suggest that user perceptions about the value of DIE may be contingent on several factors beyond any intrinsic greater experience in learning and teaching Science. The quality of tutors may lead to a greater perceived usefulness regarding the technology. Also, the level of communication flow can affect how much science students are willing to use technology. Academic institutions will have to reassess the utility of digital information technology as an instrument for improving science education. This research focused on educational contexts where DIE profoundly influences teaching and learning science. Further research could focus on other educational fields, math, or language to broaden the understanding of technology's role in diverse educational contexts.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".