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Record W2912500398 · doi:10.1187/cbe.17-12-0267

Molecular Concepts Adaptive Assessment (MCAA) Characterizes Undergraduate Misconceptions about Molecular Emergence

2019· article· en· W2912500398 on OpenAlexafffund
A. Gauthier, Stuart G Jantzen, Gaël McGill, Jodie Jenkinson

Bibliographic record

VenueCBE—Life Sciences Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto MississaugaUniversity of TorontoNational Science Foundation
KeywordsAgency (philosophy)PerceptionPsychologyMathematics educationPopulationUndergraduate educationConceptual changeMedical educationMedicineSociology

Abstract

fetched live from OpenAlex

This paper discusses the results of two experiments assessing undergraduate students' beliefs about the random nature of molecular environments. Experiment 1 involved the implementation of a pilot adaptive assessment ( n = 773) and focus group discussions with undergraduate students enrolled in first- through third-year biology courses; experiment 2 involved the distribution of the redesigned adaptive assessment to the same population of students in three consecutive years ( n = 1170). The overarching goal of the study was to provide a detailed characterization of learners' perceptions and beliefs regarding molecular agency, environments, and diffusion and whether or not those beliefs change over time. Our results indicated that advanced learners hold as many misconceptions as novice learners and that confidence in their misconceptions increases as they advance through their undergraduate education. In particular, students' understanding of random/Brownian motion is complex and highly contextual, suggesting that the way in which we teach biology does not adequately remediate students' preconceived notions of molecular agency and may actually reinforce them.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.436
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations14
Published2019
Admission routes2
Has abstractyes

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