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Record W4403325296 · doi:10.1016/j.chbr.2024.100501

Cybersecurity activities for education and curriculum design: A survey

2024· article· en· W4403325296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputers in Human Behavior Reports · 2024
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsUniversité Laval
FundersUnited Arab Emirates University
KeywordsCurriculumSurvey researchEngineeringMedical educationComputer securityPolitical scienceMathematics educationEngineering ethicsEngineering managementComputer sciencePsychologyPedagogyApplied psychologyMedicine

Abstract

fetched live from OpenAlex

Cyber threats are one of the main concerns in this growing technology epoch. To tackle this issue, highly skilled and motivated cybersecurity professionals are increasingly in demand to prevent, detect, respond to, or even mitigate the effects of such threats. However, the world faces a workforce shortage of qualified cybersecurity professionals and practitioners. To address this dilemma, several cybersecurity educational programs have been introduced, such as specialized cybersecurity courses in computer science graduate programs. With the increasing demand, different cybersecurity courses are introduced at the high school level, undergraduate computer science and information systems programs, and even at the government level. Due to the peculiar nature of cybersecurity, educational institutions face many issues when designing a curriculum or cybersecurity activities. In this paper, we study existing cybersecurity curriculum approaches and activities. We also present case studies on cybersecurity education around the globe.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.316
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it