Learner Support in Distance Education: Unlocking the Potential of Public Libraries in Supporting Teaching and Learning in Open and Distance Learning
Bibliographic record
Abstract
This study examined the nature of services and facilities available and accessible in public libraries to ODL students in sub-Saharan Africa and the challenges these services face. Library services are central in teaching and learning processes because they expose the students to a variety of resources which facilitate in-depth study and lead to development of intended competencies. However, according to Pernell (2002), traditional library services often fail to adapt to the needs of Open and Distance Learning students especially in dual mode universities. This in the end affects students' final grades as well as the quality of education they receive. Using a cross sectional survey, from 422 respondents who include students, staff (both on campus and off campus) and librarians, data were collected though questionnaires, interviews, focus group discussions and documentary analysis. The findings reveal that due to inadequate library resources in study centres where ODL students are meant to receive remote support, the students have been utilizing library resources from the public libraries. This support from public libraries however needs to be acknowledged and fully integrated in the University policy provision for effective collaboration and knowledge sharing to ensure smooth coordination of library activities. This paper seeks to examine the potential of public libraries in supporting distance learners in Makerere University and the need for policy to guide the collaborations and while sharing library resources.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".