The Persistence of Anxiety Experienced by New Generation in Online Learning
Bibliographic record
Abstract
Aim/Purpose: To investigate anxiety in online courses and its relationship with overall online courses satisfaction as it may vary with online courses experience. Background: Delivering online courses in higher education institutions continue to increase. Anxieties seem to be persistent. Although there are many technology and internet related anxieties studies, online courses anxieties are relatively scarce. The cause for this anxiety has not been resolved or addressed sufficiently. This study takes part in this quest. Methodology: A fully online course with not face to face interaction was used for the study. A survey methodology approach was used for the anxiety scale measurements. Over 1400 students participated in the survey. Contribution: Students taking online courses continue to be challenged with anxieties. Their experiences (number of courses taken) with online courses may influence their anxieties and satisfaction levels, but that has not been studies. We contribute to this body of literature. Findings: One third of students reported to continue to experience anxiety while taking their online courses. The effect of their anxieties on their satisfaction does not seem to be influenced by their online course experience. Recommendations for Practitioners: Focus on the various elements that may influence anxieties and satisfaction of students while taking courses. For IT designers, interface and point of interactions may be the aspect to pay attention to, while professors would need to consider course pedagogy and its interaction within the IT learning environment. 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. Further study of the effect of online course experience on satisfaction and anxiety is necessary. Impact on Society: Anxiety in learning has many detrimental effects that last a student’s career and personality over their entire life. The impact of reducing anxieties while online learning is significant and tangible especially that online learning is at its initial stages of an exponential growth and will change the world sooner than later. Future Research: Pedagogy for efficient and effective online courses to reduce anxieties and in-crease satisfaction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".