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Record W2287950102 · doi:10.1177/2158244015584616

Investigating the Effect of Computer-Administered Versus Traditional Paper and Pencil Assessments on Student Writing Achievement

2015· article· en· W2287950102 on OpenAlexaff
Robert Laurie, Beatrice L. Bridglall, Patrick Arseneault

Bibliographic record

VenueSAGE Open · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsGovernment of New BrunswickUniversity of New Brunswick
Fundersnot available
KeywordsPencil (optics)PunctuationSpellingOrthographySyntaxMathematics educationSignificant differencePsychologyLinguisticsComputer scienceMathematicsNatural language processingStatisticsArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The effect of using a computer or paper and pencil on student writing scores on a provincial standardized writing assessment was studied. A sample of 302 francophone students wrote a short essay using a computer equipped with Microsoft Word with all of its correction functions enabled. One week later, the same students wrote a second short essay using paper and pencil with access to dictionaries. Mean scores were compared for essays on each medium as well as scores on six specific criteria. There was no significant difference between the overall mean scores on the paper and pencil essays and those written using a computer. Significant differences favoring the paper and pencil essays were seen on the ideas, punctuation, and syntax criteria. A significant difference in favor of the computer written essays was seen on the orthography criterion. Possible practical implications and suggestions for future research are discussed.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.167
GPT teacher head0.427
Teacher spread0.260 · 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.

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

Citations3
Published2015
Admission routes1
Has abstractyes

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