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Record W2701893690 · doi:10.22329/celt.v10i0.4734

The Development and Delivery of a Multidisciplinary Research Course for First-Year International Science Students

2017· article· en· W2701893690 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMentorshipCurriculumMultidisciplinary approachClass (philosophy)Undergraduate researchProcess (computing)Mathematics educationPsychologyMedical educationPedagogyEngineering ethicsComputer scienceSociologyEngineeringMedicine

Abstract

fetched live from OpenAlex

Students who engage in undergraduate research experiences acquire many benefits, including an understanding of how scientific knowledge is constructed, recognition that knowledge can be complex and uncertain, and the habit of viewing knowledge critically. This paper describes a first-year two-course sequence that provides multidisciplinary opportunities for international science students to engage in the research process and present at a student-led research conference. We describe course goals and structure, and discuss whether the goals were attained using instructor reflections, student performance, and student survey data. We also evaluate the impact of changes to the curriculum between Year 1 and Year 2. In both years, we found that students engaged meaningfully with the research process and began to understand how scientific knowledge is created. We also found that a modular model with front-end support worked better for instructors as compared to a continuous individualized project mentorship model. This modular approach involved structured pre- and post-class assignments within discipline-specific themes containing examples of the research process embedded into the discipline. These discipline-specific modules were followed by modules covering broader research process themes. We encourage instructors who are thinking of delivering a similar research-based course for first-year students to provide support via example research questions and other example templates for student submissions.

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.

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)0.021
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0250.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.106
GPT teacher head0.495
Teacher spread0.390 · 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