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Record W1585917131

The use of unfamiliar words: writing and CS education

2008· article· en· W1585917131 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

VenueJournal of computing sciences in colleges · 2008
Typearticle
Languageen
FieldComputer Science
TopicInformation Systems Education and Curriculum Development
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaMount Royal University
Fundersnot available
KeywordsReading (process)Computer scienceMathematics educationInstitutionCode (set theory)Word (group theory)Professional writingMultimediaPsychologyLinguisticsProgramming languageSociologySet (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

Communication skills are often cited as among the most important skills for Computer Science (CS) professionals [1, 2], so it may seem somewhat incongruous that other than writing code and associated program documents, CS students are rarely given writing tasks in their CS courses. This paper will examine some possible reasons for why that might be, and what benefits could be realized through providing students with opportunities for more and varied forms of writing. A brief review of strategies used at various institutions is outlined, and a new strategy that has been implemented twice at the author's former institution is described, where students are asked to produce short, 250-500 word reading responses to various assigned readings. The details of the assignment, as well as the intended outcomes will be outlined. The concept proposed in this paper was implemented in two semesters of the same course, and some initial student reactions are outlined with suggestions for further examination and development.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.294
Teacher spread0.247 · 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