Impact of Higher-Institution-Organised Training and MOOCs on Academic Staffs’ Innovative Skills Acquisition
Bibliographic record
Abstract
The teaching and learning innovations in this post-COVID pandemic study focus on improving content knowledge, pedagogy, instructional resources, the learning environment, and learning outcomes. Using technologies to provide sustainable interventions is good, but adopting innovative digital pedagogical resources to facilitate instructional activities for the future is challenging because of digital skills required. Lacking this knowledge could lead to inappropriate integration for pedagogical practices and problems for learners. In this research, among the massive open online courses (MOOCs) that the academic staff used to acquire the digital skills for online learning were two Commonwealth of Learning MOOCs: Introduction to Technology-Enabled Learning and Using Open Education Resources for Online Learning. Considering training and standards, this study investigated innovative pedagogical digital skills acquired by academics participating in higher-institution-organised training, compared to those participating in MOOCs in Nigeria. Purposive sampling was used for the descriptive survey. A validated questionnaire, ASOLICQ (0.86 reliability co-efficient), was used for data collection. Data were analysed using frequency, percentage, correlation, and chi-square. Findings revealed a significant difference between levels of skill acquired by staff trained with the two different methods. Based on the findings, recommendations emphasise provision of quality assurance, engaging hands-on practices, participatory learning reflection sessions, and friendly monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".