A Study on Factors Affecting University Students’ Perception Towards Online Learning Post COVID-19
Bibliographic record
Abstract
COVID-19 has turned the world upside down and transformed the learning experience of the students through online learning. This study presents a comprehensive exploration of the various factors which influence the students' perceptions of their learning experience in the context of online learning. This research integrates the students’ perspective such as their personal satisfaction, classroom interaction, learning preferences and factors such emotional, psychological, physical that affects the efficacy of online learning experience. A mixed methods approach was used to gather both quantitative and qualitative data. The study explored factors influencing students’ perspectives towards their learning experiences with a focus on their personal satisfaction, classroom interaction, learning preferences and the impact on academic performance. A total of 209 participants from various universities took part in this research study. The findings contribute to both theoretical understanding and practical implementation of online learning strategies, essential for both learners and instructors alike. This study also offers insights on potential factors faced by university students during online classes such as adaptability to new formats of learning, motivation, classroom interaction, and access to learning resources that seemed to serve as some of the leading factors identified that affects students' overall learning satisfaction. As online learning revolutionized the way students learn and became an integral part of the education process at all levels, his study provides an evaluation on the benefits and the drawbacks of utilizing online learning as well as the effects on students’ learning preferences as well as academic performances.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".