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Record W2186483225 · doi:10.37514/wac-j.2005.16.1.06

Dangerous Partnerships: How Competence Testing Can Sabotage WAC

2005· article· en· W2186483225 on OpenAlexaffabout
Doug Brent

Bibliographic record

VenueThe WAC Journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompetence (human resources)BusinessEngineeringPsychologySocial psychology

Abstract

fetched live from OpenAlex

Ensuring that students graduate from post-secondary institutions with good writing skills presents two related challenges: assessment of writing and the teaching of writing. In this essay I want to address a commonly-used solu-tion to these twin challenges: the administration of an institution-wide compe-tence test to place students in WAC courses. I will begin with some of the reasons that this combination of a writing competence test and mandatory WAC courses is an attractive, and therefore commonly used, solution to this challenge of both certifying writing skills and educating those who do not earn certification. In the remainder of the essay, however, I will use a case study of the University of Calgary, and to a lesser extent Laurentian University, to illustrate some serious dangers of this relationship. I don’t want to suggest that competence testing and WAC can never exist in harmony. Like all WAC stories, the stories of the University of Calgary and of Laurentian are enmeshed in local politics that could well be different elsewhere. There may be ways to avoid the pitfalls I describe. But I will be quite candid: my experience has led me to become soured on the idea of combining institution-wide competence testing and WAC. I believe that their seemingly complementary approaches to what appears to be the same problem mask some deeply divided pedagogical assumptions that threaten to undermine the benefits of a WAC program, leading me finally to advise those who would contemplate such a potentially Faustian bargain to use extreme caution or avoid it altogether. I will end with a brief look at an alternative way of gaining traction on the difficult problem of ensuring students graduate with adequate writing pro-ficiency—first year seminars. In first-year seminars students learn and practice academic writing in a content-specific environment, and instructors are less apt to feel burdened by low-performing writers than in a course that links in-struction to universal testing. Why Combining Testing and WAC Looks Attractive Let us set to one side for a moment all the pedagogical and theoretical arguments for and against institution-wide writing competence testing (though I will come back to these arguments briefly later in this essay), and assume for

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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.144
GPT teacher head0.354
Teacher spread0.210 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2005
Admission routes2
Has abstractyes

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