An Assessment of the Extent of Integration, Application and Utilization of Web-Based Learning Systems in Post Basic Institutions in Nigeria
Bibliographic record
Abstract
This study employed descriptive survey design. It discussed the adoption of, and integration of Web-Based Learning Systems(WBLS) into teaching and learning for distance learning and full time students. It also discussed various concerted efforts at integrating web-based learning into the teaching and learning of Science and Technology Education at the Post-Basic Institutions (STEP-B). The specific objectives of this study are to:(i) investigate WBLS adoption and usage among the students of Obafemi Awolowo University, Ile-Ife for both distance and residential learning,(ii) determine the availability and adequacy of the facilities / infrastructures for WBLS,(iii) assess the level of implementation of WBLS (iv) determine the WBL format adopted by the University,(v) find out student’s acceptance to use the type of WBL format adopted, and (vi)investigate the challenges facing the integration and utilization of WBLS .The study sample consisted of two hundred and fifty undergraduate students and fifty six staff selected from OAU Ife. The research instrument is made of 35items divided into 5 sections. The results showed that adoption and usage of WBLS in OAU Ife was very high. WBLS facilities and infrastructure are inadequate for both full time and distance learners. The students were enthusiastic to accept and use the new WBLS (Academic Blackboard and Multi-site teaching using the blended learning approach). The major challenges as ranked by the students are: techno-phobia, band width problem, epileptic power supply and insufficient infrastructure.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".