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Record W1990281660 · doi:10.1109/icalt.2014.118

Computational Thinking, Code Complexity, and Prior Experience in a Videogame-Building Assignment

2014· article· en· W1990281660 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Alberta
FundersDivision of Undergraduate EducationNatural Sciences and Engineering Research Council of Canada
KeywordsScratchComputational thinkingComputer scienceCurriculumCode (set theory)Mathematics educationEmbeddingMultimediaSimple (philosophy)PedagogyArtificial intelligenceProgramming languagePsychology

Abstract

fetched live from OpenAlex

Computational-thinking skills are an essential intellectual amplifier for all scientific and professional disciplines. Embedding these skills in the K-12 and University curricula is necessary for training the next generation of thinkers. A widely adopted approach to doing so is through simple and visual programming languages like Scratch and engaging assignments like video-game construction. In this work, we report on an empirical study we conducted with senior undergraduate education students aiming to understand how prior experience enables students to better develop their computation-thinking skills through a Scratch-based video-game assignment.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.399

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 communication0.0000.000
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 head0.042
GPT teacher head0.315
Teacher spread0.273 · 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

Quick stats

Citations16
Published2014
Admission routes2
Has abstractyes

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