Exploring Online Student Engagement During COVID-19 Pandemic in Mongolia
Bibliographic record
Abstract
Student online engagement has become a challenge for higher education in Mongolia during the COVID-19 pandemic. This study explores student engagement in online learning of Mongolian higher education during the lockdown period. The study assessed differences in student engagement across gender, the tools students used to study, their level of study, and their field of study. It also determined the associations between variables. The data were collected online using the questionnaire developed by Dixson (2015), the scale to measure student online engagement (OST) with four variables: skills, emotion, participation, and performance. The tests of Kruskal-Wallis, Shapiro-Wilk, Cramer’s V, Point biserial as well as Spearman rho were used for the analyses. The most significant variables to measure student engagement were participation and performance. One of the important results was that the internet access was highly correlated with the performance variable (p=.00). The study did not find any significant differences or correlation for “emotion” expressing the devotion and commitment to the online study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".