Student Satisfaction with Online Learning in a Blended Course
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it