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Record W4386256646 · doi:10.24908/iqurcp16646

An Introduction to Educational Research through Scholarly Bricolage

2023· article· en· W4386256646 on OpenAlexaffvenue
Xiaomeng Liu, Michelle Searle

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsQueen's University
Fundersnot available
KeywordsBricolageCreativityTest (biology)Presentation (obstetrics)Computer scienceGraphicsVariety (cybernetics)Mathematics educationPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

A variety of different approaches, theories and philosophies can be used to engage in diverse range of projects through bricolage (Sharp, 2019). The use of bricolage employs difference in research methods and stimulates creativity (Kincheloe, 2011). Projects have spanned a range of scholarly activities and included learning about an assessment and evaluation research group and supporting KMB of research group activities through reporting and website design. Gathering information on awards, funding as well as activities and publications from our group members enables us to summarize key information and reflect comprehensive work in visual and written reports. We focused on the content, graphics, structure and navigation aspects of our website. Research within assessment and evaluation plays an important role in teaching and learning process. By conducting data analysis on testing student competency development in an evaluation course, we translated the research question into the language of statistical tests of hypotheses. Paired two-sample t-test and two-way ANOVA test are generated using statistical programming language R. After processing the data, we took authorship roles in a peer-reviewed publication and presentation to write the findings based on the results and interpret the ultimate impact. Our findings have identified the evaluator competencies are improved apparently through the evaluation course where the findings are supported by the data of pre- and post-course scores. Overall, this summer learning opportunity allowed us to piece together a different understanding that demonstrated the power of collaboration when diverse skill sets are brought together to advance and make visible educational ideas. References Kincheloe J. L. (2011). Describing the bricolage: Conceptualizing a new rigor in qualitative research. In Steinberg S. R., Kenneth T. (Eds.), Key works in critical pedagogy (pp. 177–189). Brill Sense. Crossref. https://doi.org/10.1007/978-94-6091-397-6_15 Sharp, H. (2019). Bricolage research in history education as a scholarly mixed-methods design. History Education Research Journal, 16(1), 50-62. https://doi.org/10.18546/HERJ.16.1.05

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.071
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0030.005
Scholarly communication0.0090.009
Open science0.0020.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0710.031

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 head0.456
GPT teacher head0.536
Teacher spread0.080 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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