Computer Anxiety and Computer Self-Efficacy as Predictors of Iranian EFL Learners’ Performance on the Reading Section of the TOEFL iBT
Bibliographic record
Abstract
The present study attempted to find out which of the two variables of computer anxiety and computer self-efficacy can best predict Iranian EFL learners’ performance on the reading section of the TOEFL iBT and whether there is any relationship between computer anxiety and computer self-efficacy. To this end, 75 English major participants, both male and female were administered two questionnaires including Computer Anxiety Rating Scale (CARS) and Computer Self-Efficacy Scale (CSES), as well as the reading section of the TOEFL iBT. Also, the participants’ proficiency level was determined using their scores on the Oxford Quick Placement Test (OQPT). This study was carried out at Alzahra University, University of Tehran, and Allame Tabataba'ei University. The collected data were analyzed through multiple regression and correlation procedures. The findings revealed that there are no significant differences between computer anxiety and computer self-efficacy as predictors of Iranian EFL learners’ TOEFL iBT reading comprehension. Therefore, both independent variables were found to be effective in predicting learners’ performance, with the effect of self-efficacy being stronger. Additionally, a significant relationship was found between Iranian EFL learners’ computer anxiety and computer self-efficacy. That is to say, computer anxiety modestly affects self-efficacy and vice versa. The results of the study may be helpful for both teachers and test takers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".