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Record W2133004698 · doi:10.1145/2512276.2512281

Using video game development to engage undergraduate students of assembly language programming

2013· article· en· W2133004698 on OpenAlexafffund
Jalal Kawash, Robert D. Collier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Calgary
FundersUniversity of CalgaryU.S. Consumer Product Safety Commission
KeywordsSubject (documents)Computer scienceClass (philosophy)CurriculumMathematics educationPosition paperMultimediaPsychologyPedagogyWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

It is widely accepted that the instruction of programming in assembly language is often a challenging and frustrating experience, both to educators and undergraduate students. Although little can be done to simplify the curriculum, it is absolutely crucial that frustration not compel students to abandon the subject. Our use of game development in a second-year course affords a unique opportunity to present this complex subject, without omission, in such a way as to create an experience that most students find entertaining. The results of a class survey indicated that 65% of participants agree or strongly agree that the experience was enjoyable (with only 11% in disagreement). We conclude that this ensures a sufficiently engaging experience that offsets the tedium inherent to the subject. The consensus of most students was that the complexity of video game design does not detract from their enjoyment of the course and contrarily has a positive impact on their learning overall. This position is supported by additional survey results.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.059
GPT teacher head0.401
Teacher spread0.342 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2013
Admission routes2
Has abstractyes

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