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Record W2965066048 · doi:10.36510/learnland.v12i1.987

Working With Avatars and High Schoolers to Teach Qualitative Methods to Undergraduates

2019· article· en· W2965066048 on OpenAlexvenueno aff
Kristin M. Murphy

Bibliographic record

VenueLEARNing Landscapes · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersCenter for Clinical and Translational Science, University of MassachusettsUniversity of Massachusetts Boston
KeywordsQualitative researchContext (archaeology)Action researchMathematics educationPsychologyAction (physics)Qualitative propertyComputer sciencePedagogySociology

Abstract

fetched live from OpenAlex

Learning to conduct qualitative research is a complex endeavor. In this article, I introduce mixed reality simulations as a scaffolded learning tool to support student mastery of learning and knowing how to conduct qualitative research. Like flight simulators used to train airline pilots prior to flying an actual airplane, mixed reality simulations provide active practice opportunities to interact with avatars in order to practice newly learned skills. I discuss this in the context of my experiences using mixed reality in an undergraduate youth participatory action research methods course as a scaffold before joining research teams with high-school-aged coresearchers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.398
Teacher spread0.349 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2019
Admission routes1
Has abstractyes

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