Moderating Effect of Self-Efficacy in the Relationship Between Knowledge, Attitude and Environment Behavior of Cybersecurity Awareness
Bibliographic record
Abstract
Today, cybersecurity is a growing issue in our education society to ensure a safe teaching and learning process for teachers and students. Reports and studies demonstrated that teachers and students are moderately aware of the cybersecurity threat surrounding them that could affect the learning curve and other fatal impacts. This study aims to identify the role of self-efficacy in the relationship between knowledge, attitude, and environment behavior of cybersecurity awareness. The researchers used a correlational design with a quantitative approach by using a questionnaire instrument to collect data from teacher respondents. A total of 350 teachers in took part in this survey voluntarily, distributed using social media applications in the midst of the Covid-19 pandemic. The researchers used the IBM SPSS Statistics to analyze the data descriptively and inferentially. The levels of knowledge, attitude, self-efficacy, and environment behavior of Malaysian school teachers towards cybersecurity awareness are low. Self-efficacy acts as a moderator in influencing the relationship between knowledge, attitude, and environment behavior of cybersecurity awareness. Strategies and programs need to be initiated by stakeholders to increase the self-efficacy of cybersecurity that could assist the positive change of environment behavior among teachers for teaching and learning benefit.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".