Towards The Use Of Information And Communication Technology In Undergraduates Learning: Possession, Perception And Problems In Obafemi Awolowo University, Ile-Ife, Nigeria
Bibliographic record
Abstract
This paper focuses the potentials of information and communication technologies (ICT) on three main areas as related to students learning (possession, perception and problems). It investigated students’ possession, perception and problems (as envisaged or experienced) by students of Obafemi Awolowo University, Ile-Ife on the use of ICT facilities. The study employed survey method. Sample for this study consisted of 500 undergraduates randomly selected from Obafemi Awolowo University, Ile-Ife. The instrument used in this study was a 45 item likert-type questionnaire. Four objectives were formulated for the study, while simple percentages were used in data analysis. It was found that majority of the undergradutes in OAU possess ICT facilities that can be used in learning. The facilities include laptops, ipad, ipod, android phones, blackberry etc, which have capabilities for carrying instructional contents. It was also found that undergraduated perceived that these ICT facilities are useful for instructional purposes. However, they identified lack of power supply, pproblem of access, financial constrains, and phones without MMS capability as problems that might militate against the use of ICT in learning. The paper concludes that the university should harness this opportunity and use ICT facilities fully in teaching and learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".