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Record W2463232812 · doi:10.1177/1475725716659252

A Call for Computational Thinking in Undergraduate Psychology

2016· article· en· W2463232812 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.

Bibliographic record

VenuePsychology Learning & Teaching · 2016
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsMacEwan University
Fundersnot available
KeywordsComputational thinkingCurriculumMathematics educationComputer sciencePsychologyCognitive sciencePedagogy

Abstract

fetched live from OpenAlex

Computational thinking is an approach to problem solving that is typically employed by computer programmers. The advantage of this approach is that solutions can be generated through algorithms that can be implemented as computer code. Although computational thinking has historically been a skill that is exclusively taught within computer science, there has been a more recent movement to introduce these skills within other disciplines. Psychology is an excellent example of a discipline that would benefit from computational thinking skills because of the nature of questions that are typically asked within the discipline. However, there has not been a formal curriculum proposed to teach computational thinking within psychology and the behavioural sciences. I will argue that computational thinking is a fundamental skill that can easily be introduced to psychology students throughout their undergraduate education. This would provide students with the skills necessary to become successful researchers, and would also provide a practical and marketable skill to all psychology graduates.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.355
Teacher spread0.329 · 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