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Record W4392721128 · doi:10.22318/icls2023.182613

Assessing Computational Thinking Attitudes in Empirical Research: A Systematic Review

2023· review· en· W4392721128 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

VenueProceedings. · 2023
Typereview
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsCentre for Advancing Health OutcomesUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsDimension (graph theory)Component (thermodynamics)CognitionEmpirical researchPsychologyCognitive dimensions of notationsCognitive psychologyComputer scienceApplied psychologyMathematicsStatistics

Abstract

fetched live from OpenAlex

This study investigates how affective, behavioral, and cognitive components of computational thinking (CT) attitudes are measured in empirical studies.Findings show that (1) surveys were the most commonly used tools for measuring CT attitudes; (2) all three components were measured in CT studies; and (3) the affective component of CT attitudes was less likely to be measured compared to the behavioral and cognitive components.This reframes the assessments of CT attitudes and provides a reference for researchers interested in measuring the attitudinal dimension of CT.Future studies are suggested to explore the alignment among the three components and the relationship between different components and CT skills.

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)0.014
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.003
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 head0.491
GPT teacher head0.552
Teacher spread0.061 · 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