Factors influencing teaching staff’s adoption of Learning Management Systems in three Nigerian universities
Bibliographic record
Abstract
Educational institutions of higher learning in most countries are moving to virtual learning, but the acceptance and deployment of learning management systems (LMSs) by teaching staff in some Nigerian universities are still a problem. Published research that details the use of LMSs by Nigerian academic staff is sparse, hence, this study investigates factors that influence reception and utilization of LMSs by staff who teach at 3 chosen universities in Nigeria using a quantitative correlational approach. The modified Unified Theory of Acceptance and Use of Technology (UTAUT) framed the study. Also, two variables were added to the instrument namely, ‘Design decision’ and ‘Staff performance’ to garner additional data about the usage of LMSs in the circumstances of HEIs in Nigeria. One hundred and twenty-two (122) teaching staff completed the online survey. Regression analyses suggested that effort expectancy contributed most to LMS’s actual use. Moreover, facilitating conditions, performance expectancy, and social influence had a statistically significant effect on LMS actual usage and design decisions. The findings may inform university HEI administrators in countries of developing economies on essential factors to consider when digitizing 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".