Anxiety & Performance in Online Learning
Bibliographic record
Abstract
Aim/Purpose: To investigate the state of anxiety and associated expected performance in online courses at the undergraduate level. Background: Online courses continue to increase dramatically. Computer related anxieties remain an important issue, and, in this context, it has evolved to online learning anxieties with deeper psychological states involved. Consequently, performance is compromised. Methodology: A first semester online course in information technology was used for the study. A survey methodology approach was used for the anxiety scale measurements. A sample of 1377 participants was obtained. Contribution: Although there are many technology and internet related anxieties studies, they are relatively scarce. Characteristics of educational performance as they relate to anxiety have not matured and are still controversial. We contribute to this body of literature. Findings: 30% of students seem to experience some sort of anxiety with online courses. Female students are more anxious about taking online courses than male. Recommendations for Practitioners: Through successive iterations between design and measuring the experience of anxiety, it is important to identify and mitigate sources of anxieties and to design course with greater distribution of marks on more tasks. Recommendation for Researchers: Anxiety in online learning should take front stage as it represents an underlying stream of influence on all research in the field. Impact on Society: It has been shown that the progress of nations depends on the academic performance of its students. As such, studies have also shown that anxiety in learning affects performance. Ultimately this impacts the nation’s progress and quality of life. Future Research: Pedagogy for efficient and effective online courses to reduce anxieties and enhance performance.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".