Factors Affecting Students' E-Learning Activities Using Exploratory Factor Analysis
Bibliographic record
Abstract
With the advent of technology, learning becomes more accessible. With the pandemic brought about by Covid 19, the educational system in the Philippines shifted from traditional face-to-face to online learning. Not being prepared for the sudden shift, many students are being affected. In this study, significant factors affecting students’ e-learning are determined. Exploratory Factor Analysis (EFA) was used to determine the factors that affect University students’ e-learning. This statistical technique used to reduce data to a smaller set of summary variables and investigate the phenomenon's underlying theoretical structure, and determine the form of the variable-respondent relationship. Data sets were gathered through Google Form with twenty-two (22) observable variables. A subset of the entire data is the factors affecting students' e-learning activities. Based on the results, there are three underlying factors namely (F1) App Used, Course Content and Design, and Faculty/Student’s Capability Factors, (F2) E-learning, Mental Health and Home Environment Problems, (F3) Social/Media Influence and Student’s Mannerism Factors. Different goodness of fit tests was employed to validate the final model. The final model satisfies all the criteria needed for model validation. Hence, the model is accurate and fits with the variables considered in the study.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".