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Record W4399489501 · doi:10.22318/icls2024.217962

Using an Evidence-Centered Design Approach to Examine the Alignment of Computer Science Curricula with Standards

2024· article· en· W4399489501 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

VenueProceedings. · 2024
Typearticle
Languageen
FieldComputer Science
TopicInformation Systems Education and Curriculum Development
Canadian institutionsLearning Partnership
FundersNational Science Foundation
KeywordsCurriculumComputer scienceEngineering ethicsMathematics educationSoftware engineeringHuman–computer interactionEngineeringPsychologyPedagogy

Abstract

fetched live from OpenAlex

In the early stages of K-12 Computer Science (CS) curriculum development, standards were not yet established, and the primary objective, especially in younger grades, was to spark students' interest in CS.While this remains a vital goal, the development of the CS standards underscores the importance of standards-aligned curriculum, ensuring equitable, content rich CS education for all students.We show that standards alignment is most useful when it includes details about which aspects of the standards a curriculum aligns with.This paper describes our process of decomposing five middle school CS standards into granular learning targets using an evidence-centered design approach and mapping the learning targets onto individual lessons from one widely popular middle school CS curriculum.We discuss the potential implications of this work on curriculum design, curriculum selection, and teacher professional learning.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.104
GPT teacher head0.329
Teacher spread0.225 · 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