Digital Learning Technology Usage and Teaching Effectiveness of Business Educators in Nigeria’s South-South Universities
Bibliographic record
Abstract
All subjects may be taught well, but a big part of it is how the instructor uses technology to make learning beneficial for the learners. This study investigated the extent to which digital learning technologies’ usage influences the teaching effectiveness of business educators in Nigeria’s South-South universities. Two specific objectives were established and two null hypotheses were tested. The level of significance was set at 0.05. Relevant literature was reviewed. The study adopted a predictive correctional research design. The study participants were 170 business educators from twelve universities in South-South Nigeria. No sample was drawn because the population was manageable. Data was generated using a 24-item Likert scale questionnaire called the "Digital Learning Technologies and Teaching Effectiveness Questionnaire (DLTTEQ). Seven experts from the University of Calabar validated the DLTTEQ. The study’s assumptions were evaluated using simple linear regression. The use of virtual reality simulation and teleconferencing by business educators in Nigeria’s South-South universities was found to significantly predict their teaching effectiveness. Sequel to the research findings, the study recommends that the national government should foster simulation-based education by building a digital learning environment appropriate for Business Education. The insights of this study call for the implementation of international best practices in order to help learners and instructors transition from digital immigrants to electronic natives.
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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 | 0.007 |
| 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 | 0.000 |
| 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".