Perception of Students on Online Exams and How Sequential Exams and the Lockdown Browser Affect Student Anxiety and Performance
Bibliographic record
Abstract
Online education has become increasingly popular over the past few years, especially with the global pandemic forcing students to learn remotely. Although online education offers various benefits, including flexibility, accessibility, and convenience, it presents unique challenges, including the use of Lockdown Browser for sequential online exams that can increase students’ anxiety levels and decrease their performance. In this paper, an empirical study was undertaken to examine the students’ preferences for online exams and how the protracting exams impacting on students’ anxiety and performance taking into consideration factors such as gender, class standing, and the availability of a personal study space. The finding reveals that sequential exams, errors in questions, use of lockdown browser, writing exams in different time zone, and one question per page increase students’ stress and anxiety. The results also suggest that there was a significant difference in anxiety levels between students who received different letter grades, specifically, students who received lower grades reported higher levels of anxiety. However, the gender and delivery of the course did not appear to have a significant impact on anxiety levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".