Examination of the Impact of Learning Management System on University Undergraduate Students’ Academic Performance
Bibliographic record
Abstract
The study examined the impact of Learning Management System (LMS) on university undergraduate student’s academic performance. The study adopted quasi-experimental design, the data for the study were collected through the use of online questionnaire and students’ academic achievement scores in Test and Measurement. The study sample comprised one hundred and four (104) university undergraduate students of Ajayi Crowther University Oyo. The result of the study revealed that there was positive relationship between the use of LMS and students’ academic performance. The findings of the study also revealed that there was significant difference in academic performance of both male and female university undergraduate students taught with LMS and those that were taught with traditional method. The study further revealed that the usage of LMS is hindered with some factors among which are low levels of commitment of the lecturers to the use of LMS, lack of ICT based learning strategy as well as inability of lecturers to provide the needed technical support. The study equally revealed that effective usage of LMS can be recorded by organizing ICT training for both the lecturers and students and improvement of infrastructural facilities will go a long way in improving the effective usage of LMS for teaching and learning process. Based on the findings of the study, it was recommended that the use of LMS should be encouraged in Nigeria tertiary institutions, lecturers and students should be encouraged to improve their computer literacy skills for effective usage of LMS, government and curriculum development agencies should incorporate learning management system usage into tertiary institutions’ curriculum as one of the modes of instructional delivery and that learning management system facilities should be adequately provided in Nigeria tertiary institutions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".