Online Learning and Student Engagement: An Analysis of the Effectiveness of Virtual Classrooms
Bibliographic record
Abstract
This study assessed the level of commitment and engagement of students in online classes in order to understand the relationship between the two variables of commitment and engagement. It also evaluated some of the challenges that affect students' engagement in online classes. The study was conducted among the students and teachers from Hanjiang Normal University. It used a mixed-method research approach by utilizing both survey questions and interview questions in which the survey analysis was done using mean, standard deviation, and Pearson's correlation, while the interview questions were evaluated using thematic analysis. 250 students participated in the quantitative survey, while 10 teachers participated in the interview process. Based on the research findings, the students are very committed to relating what they're learning in online classes to real life situations, moderately committed to construct knowledge in online classes, and less committed in applying what they learned to real life situations. The students' participation in individual group activities indicate that they are highly engaged. However, they are less engaged in their participation in online activities. There is no significant relationship between online learning commitment and student engagement. Students often struggle to manage their time effectively which in turn leads to procrastination. Students are given an amount of time to complete a set of tasks and assignments. Based on these findings, a strategic plan was proposed to increase the level of engagement in online classes.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".