Students engagement in distant learning: How much influence do the critical factors have for success in academic performance?
Bibliographic record
Abstract
Abstract This research identifies the critical factors of student engagement and distance learning that will improve academic performance during a pandemic. The fuzzy Delphi method and fuzzy analytical hierarchy process method are applied to this research, which is a multicriteria decision‐making technique. A survey is conducted and evaluated based on experts' opinions. The social constructivism theory was selected to be applied here; it supports student engagement and distance‐learning factors' relationships with academic performance. After the analysis, the six most significant factors are evaluated. It is observed that Social isolation (C1), Technology effectiveness (C2), Social status enhancement (C3), Student Competency (C4), Motivation (C5), and Satisfaction (C6) are the highest‐ranking factors that increase academic performance. On the basis of the results, it is suggested that management's primary responsibility is to provide training and guidance to instructors/teachers to enhance, motivate the students, and create opportunities for every student to improve their academic performance in a pandemic situation through distance learning.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".