Usage Status, Usage Requirements, and Satisfaction in Using Massive Open Online Course (MOOC) for General Education Courses at Rajamangala University of Technology Srivijaya
Bibliographic record
Abstract
The primary objective of the present study was to comprehensively evaluate the utilization, requisites, and contentment associated with the implementation of the Massive Open Online Course (MOOC) system in general education courses at Rajamangala University of Technology Srivijaya. Furthermore, a comparative analysis was conducted to examine potential variations in usage status, requirements, and satisfaction among different demographic groups, including gender, age, year of study, campus, and faculty. A meticulously designed questionnaire was administered to a sample of 279 students selected by stratified random sampling. The findings unequivocally demonstrate the educational advantages of employing the MOOC system, underscoring its effectiveness in augmenting participants’ learning experiences. The study unequivocally identified accessibility, interaction, independence, collaborative learning, learning resources, and teaching methods as pivotal prerequisites for an optimal MOOC system. Moreover, the overall level of contentment among the participants was consistently high. Significantly, the faculty variable exhibited a substantial influence on satisfaction with the MOOC system. This notable disparity in satisfaction may be ascribed to the distinctive learning characteristics, technological proficiency, and educational backgrounds prevalent across diverse faculties. The outcomes of this study make a valuable contribution to the existing body of literature by highlighting the significance of customizing MOOC systems to align with the specific requirements and preferences of students within distinct faculties. Future research endeavors should concentrate on exploring faculty-specific features and devising targeted strategies to optimize satisfaction levels and learning outcomes within MOOC-based educational environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".