Information Communication Technology Skills and Students’ Engagement in Online Learning Spaces during the Covid-19 Pandemic
Bibliographic record
Abstract
The circumstances surrounding the Coronavirus disease 2019 (COVID-19) pandemic presented a drastic decline in the use of traditional face-to-face methods of teaching and learning in higher education institutions. As the new normal advanced the use of information and communication technology (ICT) devices and skills for online and distance learning and the use of digital libraries, this study investigated the extent to which the access and use of ICT devices and skills have supported students’ engagement in online classes during the school closures that characterised the COVID-19 pandemic era. The study adopted a mixed-methods research design involving the use of questionnaires and online focus group discussions to draw responses from participants from public and private higher education institutions in Lagos and Ogun States, Nigeria. Two research questions and one hypothesis were formulated to guide the study. A researcher-designed questionnaire and a focus group discussion guide were administered online to elicit responses from participants. Data were analysed using descriptive and inferential statistics. Results showed a significant positive relationship between the ICT skills of students and the level of engagement during online classes. It was recommended that lecturers and facilitators of knowledge in online learning facilities should make concerted efforts to up-skill such that the facilitation of learning will be engaging for the students in online facilities even beyond the pandemic era.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".