Strategies for Enhancing the Teaching of ICT in Business Education Programmes as Perceived by Business Education Lecturers in Universities in South South Nigeria
Bibliographic record
Abstract
This study assessed the strategies for enhancing the teaching of ICT in Business Education programme as perceived by Business Education lecturers in universities in south south Nigeria. Three research questions and six hypotheses guided the study. The design of this study was a descriptive survey. The population which also served as a sample comprised 134 Business Education lecturers in universities in the south south geopolitical zone of Nigeria. The instrument for data collection was a 66 – item questionnaire. The instrument was validated by experts in Business Education. The internal consistency of the instrument was determined using cronbach alpha, which has a reliability coefficient of 0.93. The data were analysed using mean and standard deviation. The study revealed the prospects of teaching ICT: ICT facilitates interaction between lecturers and students; ICT enhances effective storage of business information; ICT facilitates the retrieval of business information. The study also revealed constraints facing the teaching of ICT such as inadequate ICT facilities/equipment; frequent electricity interruption of ICT facilities and poor implementation of ICT policies. Moreover, the study revealed some strategies for enhancing the teaching of ICT: adequate funding of ICT facilities; provision of adequate ICT equipment; provision of adequate ICT facilities among others. Among the recommendations made were that Business Education lecturers should undergo training and retraining in ICT programme to have more skills and competencies, that adequate ICT facilities should be provided by university authorities to enable lecturers carry out their teaching assignment effectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".