Phishing Awareness through Game-Based Learning: A Mobile-Responsive Web Application for Middle School Learners
Bibliographic record
Abstract
This study aimed to design a phishing-focused learning approach for middle school learners using a game-based educational format. The objective of this project is to (a) study and develop an educational game to enhance knowledge about phishing emails, (b) compare the academic results before and after learning, and (c) evaluate the acceptability of the educational game to enhance knowledge about phishing emails. The sample group used in this study was 130 from seventh-grade students in School A, Nonthaburi Province, Thailand. To create a game-based learning model focused on phishing emails, the researchers opted for a spot-the-difference game format. The game leveraged the fact that learners were already acquainted with the game’s rules and had prior experience playing it. Furthermore, the game is web-based, enabling learners to engage with it at their convenience, regardless of location or time. The research instruments were (a) a phishing-awareness educational game, (b) preand post-assessment tools to evaluate knowledge gain, and (c) a questionnaire measuring students’ acceptance of game-integrated instruction. The study’s results indicated a high level of learner approval for the phishing-focused instructional model that employed gamified techniques. This acceptance was evident in terms of both the perceived ease of use and the convenience associated with the learning process. Additionally, learners reported significant benefits derived from engaging with the game, including various elements that effectively supported and enhanced their learning outcomes related to phishing emails, resulting in a marked improvement compared to their prior knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".