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Record W2255536200

Auction Hero: The Design of a Game to Learn and Teach about Computer Security

2011· article· en· W2255536200 on OpenAlexaff
Sonia Chiasson, Manas Modi, Robert Biddle

Bibliographic record

VenueE-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education · 2011
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsHEROComputer scienceSoftware walkthroughComputer securityHuman–computer interactionGame designSoftwareArtificial intelligenceSoftware development
DOInot available

Abstract

fetched live from OpenAlex

Although computer security is a concern for online users, many have difficulty making effective security decisions. Auction Hero is a simulation game modeling life online that aims to improve users’ mental models of computer security and help users make more secure decisions. It attempts to simulate real life by making security a secondary consideration that requires vigilance but that may not always be the user’s main priority. We present our design, the first prototype of the game, and the results of a cognitive walkthrough evaluation.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.291
Teacher spread0.210 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueE-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher EducationSame topicAdvanced Malware Detection TechniquesFrench-language works237,207