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Record W4285233321 · doi:10.37590/able.v42.abs66

Improving Scientific Writing

2022· article· en· W4285233321 on OpenAlexaff
Maryam Moussavi, Natasha Pestonji-Dixon

Bibliographic record

VenueAdvances in Biology Laboratory Education · 2022
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematics educationPsychology

Abstract

fetched live from OpenAlex

Effective written scientific communication is a major goal of science education, however undergraduate students typically report difficulty in how to approach and plan scientific writing. To support and develop student writing skills and lessen this difficulty, I have created a series of worksheets designed to guide student writing for each component of a scientific article including support around the structure and logic of scientific arguments/explanations. This study explores student perception of these worksheets in an undergraduate introductory molecular biology lab. Student perception was collected via a series of surveys on each individual worksheet as well as a longer end-of-term survey on all of the worksheets as a whole. Analysis of these surveys indicated that students appreciated these worksheets and found that the worksheets helped them organize their thoughts prior to writing, felt more prepared to write their research paper, and will use some techniques noted/demonstrated in these worksheets in future scientific writing. This study shows that scientific writing worksheets can scaffold student learning when there is a clear and specific structure to follow with integration between the worksheets and the writing process.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.422
Teacher spread0.405 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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