Student Satisfaction with Online Learning in a Blended Course
Bibliographic record
Abstract
As online and blended learning become widespread in higher education, educators and institutions have become interested in understanding the factors that influence students' satisfaction.In this study, we used Pekrun's control-value theory of achievement emotions to examine the influence of eight characteristics of online learning on students' emotions and satisfaction with their online learning experience as well as the influence of students' emotions on their satisfaction.Twenty-nine graduate students taking a required blended course completed a series of questionnaires on characteristics of online learning, their emotions concerning their online learning, and their satisfaction with the online learning experience.The results indicated that: (1) students' reports of high understandability and illustration in the course were related to greater enjoyment and lower levels of anger, anxiety, and boredom; (2) higher levels of course expectation, difficulty, fast pace, and lack of clarity were related to greater experiences of negative emotions such as anger, anxiety, and boredom; (3) higher levels of understandability, illustration, enthusiasm, and fostering attention led to increased student satisfaction; and (4) higher levels of enjoyment and lower levels of anger and boredom increased students' satisfaction with the online learning experience.Educational implications of these results for designing online learning environments and suggestions for future research are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".