MétaCan
Menu
Back to cohort
Record W2045565787 · doi:10.1145/2676723.2677235

Reconsidering the Impact of CS1 on Novice Attitudes

2015· article· en· W2045565787 on OpenAlexaboutno aff
Amber Settle, John Lalor, Theresa A. Steinbach

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Mathematics educationQuarter (Canadian coin)Computer scienceField (mathematics)PsychologyArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Student success in an introductory programing course is crucial, both because it influences retention and because student attitudes and habits in a first course can have a lasting impact on student success in computer science as a field. In this paper we present results about student attitudes and habits before and after a CS1 class. Statistically significant attitude differences were found in three areas: students were less likely to report they were good at programming, more likely to agree they are challenged by programming problems they can't understand immediately, and are less likely to report that computer science allows them to be creative. Statistically significant differences in female and first-quarter responses were also found.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.182
GPT teacher head0.341
Teacher spread0.159 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicTeaching and Learning ProgrammingFrench-language works237,207