UNDERSTANDING STUDENT EXPECTATION AND SATISFACTION TOWARS ONLINE LEARNING IN THE OPEN UNIVERSITY: A STUDY IN ARCHIPELAGIC AREA IN INDONESIA
Bibliographic record
Abstract
The online tutorial is one of the learning methods used by students at the Open University (OP). One main obstacle in delivering online tutorial in OP is the accessibility of the internet, especially for the students living in archipelagic areas in Indonesia. This study aims to analyze the level of student satisfaction towards online tutorial activities at OP, especially for the Distance Learning Unit of OP Ternate that located in an archipelagic area with limited internet facilities. The population was the second-semester students come from various regions in Ternate. Research data were collected by distributing online questionnaires. The sample consisted of 24 respondents. Results showed that, in general, students were satisfied with online tutorial services. The level of student satisfaction towards internet access in the online tutorial was 96.04%, and for the aspect of presenting the learning materials, the satisfaction level was up to 100.77%. These findings indicated that student satisfaction exceeds their expectations. Furthermore, student satisfaction level towards the interaction aspect in the online tutorial was 89.92%. For the class assignment, the student satisfaction level was 97.05%. Keywords: access, assignment, learning material, online tutorial, student satisfaction
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".