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Record W2043579848 · doi:10.1080/03098260903093646

Teaching Research Methods Courses in Human Geography: Critical Reflections

2010· article· en· W2043579848 on OpenAlexaff
Valorie A. Crooks, Heather Castleden, Ilja van Meerveld

Bibliographic record

VenueJournal of Geography in Higher Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsDalhousie UniversitySimon Fraser University
Fundersnot available
KeywordsTransformative learningScholarship of Teaching and LearningJournaling file systemReflexivityScholarshipTeaching methodMathematics educationProcess (computing)PedagogyPsychologySociologyComputer scienceTeaching and learning centerSocial science

Abstract

fetched live from OpenAlex

Abstract The authors reflect critically on their experiences of teaching research methods/methodology/techniques (MMT) courses in human geography for the first time. Through a highly reflexive process involving journaling, they engage with the broader scholarship of teaching and learning approach. Three themes characterize commonalities in their instructional experiences: (1) prior knowledge, (2) preparation, and (3) confidence. Specifically, they were challenged by needing to teach particular MMTs that they had not previously applied in their own research, by not knowing how best to prepare for teaching such courses, and by a lack of confidence with their approaches that stemmed from numerous issues. Keywords: Research methodsreflexivitymethodologytechniquescholarship of teaching and learninggeography Notes 1 Two of us have since taught our respective courses a second time and the third has since developed and taught at new graduate-level research MMT course. We have utilized our journals as an additional source of data (beyond, for example, end-of-course student evaluations) for course planning and delivery. In one case, one of us has turned her attention to the student learning aspect of the SOTL, inviting students after the course had been completed to engage in a collaborative process to explore connections between their own and each others' transformative learning processes in relation to teaching and research in the discipline.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.191
GPT teacher head0.612
Teacher spread0.421 · 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

Citations21
Published2010
Admission routes1
Has abstractyes

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