Learners’ Satisfaction With the Website Performance of an Open and Distance Learning Institution: A Case Study
Bibliographic record
Abstract
This paper explores learners’ awareness of and satisfaction with the e-services that an open university provides its learners through its Website. The paper further highlights the influence of age, gender, and education levels on learners’ awareness and satisfaction levels. A case-study approach was adopted and an online survey was used to collect data from learners in various programs of study at Uttarakhand Open University, India. The questionnaire measured the awareness levels of learners regarding 15 frequently used e-services and their satisfaction levels with the 12 most frequently used e-services that the university offers. Results show that gender, age, and education level have a significant influence on the awareness and satisfaction level of the participants. When maturity level and education level of the participants increased, they are more aware of the e-services provided by the University. In some cases, up to 58% of users were unaware of the university’s e-services, and a large number of respondents were either dissatisfied with or undecided regarding the university’s e-services. Results indicate that infrastructure is required for learners’ optimal use of information and communication technology and the e-services that the university offers, including the provision of Internet connectivity at all of the university’s learning support centers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".