Emotional Support in Online Teaching and Learning Environment: A Systematic Literature Review (2014–2023)
Bibliographic record
Abstract
The provision of appropriate emotional support is influential in establishing a positive and stimulating online learning environment that can elevate students’ emotional well-being, improve their learning experience and enjoyment, and increase their academic achievements. In the present study we examine 16 papers on emotional support in online teaching and learning environments. We aim to understand (a) the emotional support given to online learners and (b) the effectiveness of emotional support in online teaching and learning environments. The review shows that the emotional support given to online learners includes empathy, understanding, motivation, and encouragement. These verbal and nonverbal emotional supports are mainly from teachers, family members, and peers as well as some online agents or applications. Most of the emotional supports influence online learners’ performance and have a favorable impact on their emotional states. The systematic review shows that there has been little research on technology-based emotional support and synchronous teaching and learning environments. We propose that more research should be carried out in these areas.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".