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Record W2951354326

Facilitating Learning and Research Engagement of 4th year Undergraduate Students: the Outcomes of Student Self-Assessment Survey

2017· article· en· W2951354326 on OpenAlexaffabout
Asako Yoshida

Bibliographic record

VenueQualitative and Quantitative Methods in Libraries · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLikert scaleMedical educationContext (archaeology)PsychologyStudent engagementClass (philosophy)Information literacyScale (ratio)Conceptual frameworkPedagogyMathematics educationMedicineSociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Teaching faculty and a liaison librarian began exploring ways to facilitate undergraduate student engagement in research, in the context of a 4th year research seminar course.  The course was a long established course, ―Seminar in Foods and Nutrition,‖ in the Department of Human Nutritional Sciences, University of Manitoba, Canada.  The class was small, and eight students completed the course during the 2014 Winter semester.  The Research Skills Development Framework (RSD), which was developed at Adelaide University, Australia, was adopted as a conceptual model for collaboratively reorganizing and realigning learning and instructional activities.  The RSD framework was very useful in maintaining the shared interest among the collaborators in facilitating student learning.  An online survey with 19 Likert-scale questions was administered identically at the beginning and end of the course to measure student self-assessment of research skills.  The survey results showed that the efforts in supporting student learning paid off. There were positive learning outcomes in nine research skill areas, and two additional skill areas showed positive trends.  They are all corresponding to information literacy.

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)0.039
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0010.005
Open science0.0010.001
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 head0.380
GPT teacher head0.637
Teacher spread0.257 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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