Computer Anxiety in E-Learning: The Effect of Computer Self-Efficacy
Bibliographic record
Abstract
It has been reported that as many as fifty percent of adults, including first-year University students, have some sort of computer-related phobia. This report demonstrates that the use of computers still has some unpleasant side effects despite the Internet boom in the past decade. Past research shows that computer anxiety influences how users perceive ease of use of an information system. However, few have investigated the role of computer self-efficacy in mediating computer anxieties on perceived ease of use. Therefore, in this study we base our contribution on the variables of computer self-efficacy and computer anxieties. These two variables are believed to impact an individual’s use of computers and performance for computer-based tasks. Anxiety has been argued to impact computer-based learning by affecting levels of self-efficacy anchored in social learning and outcome expectation theories. Self-efficacy is determined by levels of anxiety such that reduced anxiety and increased experience improves performance indirectly by increasing levels of self-efficacy. In this study, we investigate the influence of computer anxiety on perceived ease of use and the mediating effect of computer self-efficacy on this relationship, within an e-learning context. A survey methodology approach was used in this study using 18 items for 3 constructs (perceived ease of use, anxiety, and self-efficacy). Survey data from 645 university students were analyzed. The psychometric properties of the items and constructs were validated followed by the assessment of mediation of computer self efficacy. Results from the use of a learning management system indicate that computer self-efficacy plays a significant role in mediating the impact of anxiety on perceived ease of use. This role is observed by computer selfefficacy (1) reducing the strength and significance of the impact of anxiety on perceived ease of use and (2) having a strong and significant relationship with computer anxiety. The findings demonstrate the importance of self-efficacy as a mediator between computer anxiety and perceived ease of use of a learning management system (LMS). With the continuous development of richer and more integrated interfaces, anxieties about learning to use the new interface and executing tasks effectively becomes of primary importance. Limitations and suggestions for future research are elaborated.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 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".