The Efficacy of Evolving Technology in Conceptualizing Pedagogy and Practice in Higher Education
Bibliographic record
Abstract
The proliferation of new forms of information and communication technology (ICT) has inundated the learning patterns of students at all levels and particularly at higher education level. The efficacy of teaching the digital generation of learners without a firm grasp of how they learn is like embarking upon a perpetual journey. Invariably, today’s students have been mesmerized by digital gadgets from a very small age and this experience calls for a technology integrated paradigm. Hence, the current study focuses on the influence of evolving technology in conceptualizing pedagogy and practice in higher education. It explores staff members’ technological know-how and how they are able to influence learning at a University in Fiji. An exploratory research design was selected and a survey consisting of Likert scale items was administered. Subsequently, SPSS Statistical software was used for data analytical and reporting purpose. Findings are discussed in collaboration with a robust meta-analysis of literature and they reveal that apart from resources, staff readiness, confidence and motivation play important function in ICT integrated learning. This paper proposes that staff members should use technology and technological gadgets to enhance digital literacy and numeracy that in turn, would create a digitally vibrant society.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".