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Record W2609268617 · doi:10.1109/mcse.2017.50

Does a Taste of Computing Increase Computer Science Enrollment?

2017· article· en· W2609268617 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

VenueComputing in Science & Engineering · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsLearning Partnership
FundersLoyola University ChicagoNorthwestern UniversityUniversity of ChicagoNational Science Foundation
KeywordsComputer scienceTasteHistory of computingComputational scienceData scienceParallel computingComputer graphics (images)AlgorithmChemistry

Abstract

fetched live from OpenAlex

The reported study investigated the impact of the Exploring Computer Science (ECS) program on the likelihood that students of all races and genders would pursue further computer science coursework in high school. ECS is designed to foster deep engagement through equitable inquiry around computer science concepts. The course provides experiences that are personally relevant. Using survey research, the authors sought to measure whether the personal relevance of students' course experiences influenced their expectancies of success in and value for the field of computer science and whether those attitudes predicted the probability that students pursued further computer science coursework. The results indicate that students find ECS courses personally relevant, are increasing their expectancies of success and perceived value for the field of computer science, and are more likely to take another computing course.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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