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Record W4405674919 · doi:10.24908/pceea.2024.18632

Assessing Computational Thinking in Engineering Education: A Systematic Review

2024· review· en· W4405674919 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typereview
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputational thinkingManagement scienceEngineering ethicsMathematics educationComputer sciencePsychologyEngineering

Abstract

fetched live from OpenAlex

Computational thinking (CT) is an integrated part of engineering education due to the rapidly evolving global economy and the need for a workforce equipped with strong interdisciplinary skills, and an understanding of computing concepts. Assessment plays a critical role in finding out how teaching methods improve CT skills. We systematically reviewed 28 journal articles to analyze CT assessment in engineering education. The reviewed literature predominantly focused on specific engineering disciplines such as biology, mechanical, electrical, and software engineering among undergraduates, particularly first-year students. Among the assessment tools that were reviewed, ECTD is specially designed to assess CT skills in engineering. This comprehensive temporal research showed that scholarly work with an explicit focus on the assessment of CT skills has been emerging mainly in the last five years. The study provides a broad overview of the current state of research and represents an important opportunity for further scientific exploration.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.016
GPT teacher head0.290
Teacher spread0.275 · 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