The COVID-19 Period: A Crisis for on-Site Learning or an Opportunity for Optimal Distance Learning? Examination of Student Attitudes
Bibliographic record
Abstract
This study, which explores student attitudes to online learning, is based on a psychoanalytic theory (Existence-relatedness-growth, ERG) on relatedness and growth, developed by American psychologist Clayton Alderfer. The purpose of the study was to examine whether online learning is merely a short-term temporary solution necessitated by the COVID-19 crisis, or will it enable a transformation of teaching and learning patterns in educational systems in the post-COVID era? What is students’ personal preference regarding online learning after having inadvertently experienced it? What dimension of online teaching was meaningful for them: social presence, instructional-cognitive presence, emotional-personal presence? The research population consisted of 306 students, with a mean age of 15.5. Only 85% of the students who participated in the study had technological resources for online learning at home. About 41% of the students preferred lessons that combine online teaching with frontal teaching in the classroom. In addition, the dimensions of online teaching reported by students as meaningful were, in descending order, social presence (M = 3.54), emotional-personal presence (M = 2.96), and instructional-cognitive presence (M = 2.73). The research findings might have an effect on policy makers in education with regard to maintaining an “innovative pedagogy” aimed at shaping students’ image in order to prepare them for the new post-COVID era. In this period of global crisis, online learning afforded students innovative learning, where students enhanced their awareness of the significance of social presence, which was more meaningful than the dimension of instructional-cognitive presence. The significance of interpersonal interaction in teaching and learning received support, more so than ever before.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".