Transforming Education with Emerging Technologies in Higher Education: A Systematic Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study attempts to understand what is known about key theme findings in transforming education with emerging technologies in higher education by examining existing literature. Based on 5 selection criteria, 24 quality articles were included in the review. Thematic analysis methods are used to analyze and identify key themes in the data. The findings indicate that teachers who have used emerging technologies in teaching, they point out the key factors for transforming education with emerging technologies, including teachers' interest, institutional perspective, teachers' perceptions of the benefits of emerging technologies. They also report a dichotomy between the technologies used for teaching in higher education institutions and the technologies owned and used by students in social life as a major challenge. Teachers believe that the open communication and teamwork environment can be enhanced by using emerging technologies. Pedagogical innovation, empowering educators are essential requirements for teaching with emerging technologies. The 7 findings from this study should be used to guide initiatives for teacher career development to improve the effectiveness of education with emerging technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 it