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Record W2117841205 · doi:10.1080/08993400601069820

Implementations of the CC′01 human – computer interaction guidelines using Bloom's taxonomy

2007· article· en· W2117841205 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

VenueComputer Science Education · 2007
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersNational Science Foundation
KeywordsImplementationCurriculumComputer scienceMainstreamMillerTaxonomy (biology)Library scienceAdvice (programming)SociologySoftware engineeringPedagogyProgramming languageEcologyLawPolitical science

Abstract

fetched live from OpenAlex

In today's technology-laden society human – computer interaction (HCI) is an important knowledge area for computer scientists and software engineers. This paper surveys existing approaches to incorporate HCI into computer science (CS) and such related issues as the perceived gap between the interests of the HCI community and the needs of CS educators. It presents several implementations of the HCI subset of the CC′01 curricular guidelines, targeting CS educators with varying degrees of HCI expertise. These implementations include course/module outlines from freshman to graduate levels, suggested texts, and project ideas and issues, such as programming languages and environments. Most importantly, each outline incorporates Bloom's taxonomy to identify the depth of knowledge to be mastered by students. This paper condenses collaborative contributions of 26 HCI/CS educators aiming to improve HCI coverage in mainstream CS curricula. © 2007 Taylor & Francis.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.124
GPT teacher head0.418
Teacher spread0.294 · 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