University Student Satisfaction and Behavioural Engagement During Emergency Remote Teaching
Bibliographic record
Abstract
This study aimed to examine students' online satisfaction in the context of emergency remote teaching. The research was carried out in a concurrent triangulation design from the mixed method. The quantitative data of the study were collected from 2663 students studying at different faculties/schools of a state university in Turkey in the fall semester of the 2020-2021 academic year. Participants consist of students who participated voluntarily according to the convenient sampling method. Qualitative data were collected from 494 students who express their opinion through free text answers. The "e-satisfaction scale" was used to determine students' online learning satisfaction. The number of logins to live course, the number of recorded course view and the number of logins to LMS of students are behavioural engagement indicators. According to the findings, the students have a moderate level of satisfaction. There is a significant difference between both academic achievement and engagement of students with different satisfaction levels. Longing for face-to-face education, the usefulness of the LMS, inadequate assessment, the inefficiency of online learning, technical problems, challenges of the process, and insufficient instructors are opinions frequently mentioned by students. The results obtained in this study not only determine the current situation regarding student satisfaction but also provide important clues about improving online learning.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".