MétaCan
Menu
Back to cohort
Record W2539007623 · doi:10.1109/blocks.2015.7368993

Measuring learning in an open-ended, constructionist-based progamming camp: Developing a set of quantitative measures from qualitative analysis

2015· article· en· W2539007623 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConstructionismComputer scienceQuantitative analysis (chemistry)Strict constructionismQualitative analysisSet (abstract data type)Variety (cybernetics)Exploratory analysisQualitative propertyQualitative researchData scienceArtificial intelligenceMachine learningSociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

In this paper we raise the issue of how to assess novice youths' learning of programming in an open-ended, project-based learning environment. One approach could be a way to apply quantitative measures to the analysis of programming education across frequent saves in a variety of open-ended projects. This paper focuses on the first stage of this endeavor: the development of exploratory quantitative measures of youths' learning of computer science concepts through deep qualitative analysis.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.440
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.000
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 head0.232
GPT teacher head0.402
Teacher spread0.170 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicTeaching and Learning ProgrammingFrench-language works237,207