Teaching and Learning Online During Covid-19 Lockdown, Encouraging and Discouraging?
Bibliographic record
Abstract
The study examined the teaching and learning online during the covid-19 lockdown, in terms of whether it is encouraging or discouraging from the perspectives of students and staff of the Library and Information Science Department in Nigerian universities. Three Library and Information Science(LIS) schools were selected from three different universities. The population was the LIS lecturers and students. FIVE students and FIVE lecturers were selected from each of the three Library schools. This gave a total of 15 students and 15 lecturers which amounts to a total of 30 respondents who represent the sample for the study. A pure qualitative method was adopted using focus group interviews which were conducted for the students and the staff in each of the schools as a method of data collection. The findings show that the experience of lecturers and students in learning and teaching online during the Covid-19 lockdown was encouraging, the lesson is very easy to prepare and interesting and the learners are enthusiastic and wanted others to know they were part of the class. The respondents want learning and teaching online to continue due to its flexibility, interesting nature of teaching online, and a high percentage of participation. The benefits of teaching and learning online during Covid-19 include flexibility, ease of learning and teaching, increased interactivity and class participation, social presence, improvement in critical thinking skills, and high engagement of lecturers and students. The challenges of learning and teaching LIS during Covid-19 are slow bandwidth, poor network, cost of data (lecturers and students bear the cost of data), and unstable electricity, high withdrawal rate, inadequate skills for both lecturers and students due to their first experience, high cost of data and electronic devices among others.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".