Lived Experiences of Higher Education Institution Students in Online Learning Classes in the New Normal
Bibliographic record
Abstract
The challenges brought about by the academic migration to online platform due to COVID-19 pandemic outbreak has long been established. The author of this paper explicitly investigated the attitude of learners towards online classes. This study highlights the issues being encountered by learners enrolled in virtual learning environment along with the importance of having a concrete strategic plan as to how to cope with the current issues being encountered by students to readily adapt to online classes. The results revealed that there were several recurring themes that emerged when asked about their experiences in online classes. Under the key topic (A) effects of online class – (1) difficulty in learning process and (2) affordability and accessibility of time and place. Under the key topic (B) issues and problems experienced in online classes, there were three themes that emerged– (1) issues in internet connectivity, (2) lack of proper resources such as gadgets, and (3) lower productivity rate. In third key topic, (C) the coping mechanisms of students under online learning classes in which there are two themes that emerged – (1) finding alternatives and (2) practice of time management. And in the last key topic, (D) what the participants could recommend improving their learning process under online learning classes. There were two themes that emerged in the focus group discussion – (1) It was concluded in the study that the issues being encountered by students in online classes have an impact not only on their academic performance but also to their psychological well-being.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".