Bibliographic record
Abstract
This study examined the inclusivity of online education during the COVID-19 pandemic from the perspectives of LIS undergraduate students across four Nigerian universities. A quantitative method and survey design were employed, targeting undergraduate library and information science (LIS) students. A purposive selection of 20 students from years two, three, and four in the four universities resulted in a sample of 240 students. Data collection was conducted via a questionnaire distributed through Google Forms to students’ group forums, with the first 60 respondents from each university forming the sample. Of the 240 distributed questionnaires, 232 were returned and used for analysis. The findings revealed that online education during the pandemic was only partially inclusive: 70% of respondents indicated that many students could not participate in most online classes due to a lack of access to compatible technology, such as smartphones, tablets, and laptops. Platforms for online classes included Microsoft Teams, Google Classroom, Zoom, Moodle, and social media tools like blogs, Telegram, WhatsApp, and email. Course materials were sent via email and social media, but only 30% of students with access received them. The study concluded that online learning was not inclusive, as 70% of students were sidelined due to a lack of access to necessary devices and internet connectivity. For online learning to be inclusive, all students must be provided with compatible devices and data for connection to live classes. The government must also improve network infrastructure in rural areas to enable participation. This study is pioneering in focusing specifically on the inclusivity of online learning for LIS students during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".