The Impact of Online Learning Challenges on Business Students’ Academic Outcomes: A Modeling Approach
Bibliographic record
Abstract
The rapid shift to online learning during the COVID-19 pandemic introduced significant challenges for both students and educators, including stress and anxiety about the new learning format, assessment methods, and proctoring tools. This empirical study examines the key factors, Learning preference, Test format, Stress, Lockdown browser, Anxiety, and concentration and stress, and their direct and indirect effects on academic performance, as well as their interrelations using Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. Data were collected through a structured survey and analyzed statistically. Learning preference was positively associated with both Test format and, marginally, with Academic performance, while Test format did not significantly influence Academic performance. In contrast, Anxiety showed a negative direct effect on Academic performance, while Lockdown browser showed a positive indirect effect on academic performance and grades. Additionally, Test Format, Stress, and Concentration and stress showed a negative indirect effect on Academic Performance through increased anxiety level. These findings highlight the importance of revising assessment methods to better support students’ emotional well-being, reduce assessment-related stress and anxiety, and therefore, enhance academic 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".