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An Exploration of Writing Self-Efficacy and Writing Self-Regulatory Behaviours in Undergraduate Writing

2019· article· en· W2972120706 on OpenAlexaffvenue
Kim Mitchell, Diana E. McMillan, Rasheda Rabbani

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of ManitobaRed River College
Fundersnot available
KeywordsSelf-efficacyPsychologyReading (process)Psychological interventionAnxietyProfessional writingSocial psychologyMedical educationPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

Students will take independent action to improve their writing when they believe those actions will have a positive effect. The data presented focuses on the self-regulatory writing behaviours of nursing students in their third year. The purpose was to explore patterns of writing self-efficacy, anxiety levels, and student grade point average (GPA) in relation to student choices with help seeking, advanced planning of writing, revision habits, and response to feedback. Low writing self-efficacy, high anxiety students sought help from more sources, reported their feedback made them feel negative about their capabilities as writers, and were less likely to report reading and applying feedback to future writing efforts. No patterns of writing self-efficacy or anxiety levels emerged with respect to student revision habits or their choice to begin their assignments in advance of the due date. GPA was also not associated with the writing self-regulatory choices assessed. As the primary writing support for students in the later years of a nursing program, educators should consider interventions that encourage help seeking, facilitate students’ understanding and integration of the feedback they receive into their assignment revisions, and normalize the negative emotions that interfere with the self-efficacy levels required to write well.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.052
GPT teacher head0.352
Teacher spread0.300 · 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 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

Citations25
Published2019
Admission routes2
Has abstractyes

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