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Record W4206584129 · doi:10.26434/chemrxiv.12360788

A Study of Hybridization Understanding from Algorithmic to Conceptual – Is Algorithmic an End Point for Students?

2020· preprint· en· W4206584129 on OpenAlexaff
Gianna J. Manchester, Julia Winter, Sean P. Hickey, Sarah E. Wegwerth

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsDow Chemical (Canada)
FundersNational Science Foundation
KeywordsGrounded theoryCoding (social sciences)Think aloud protocolQualitative researchPoint (geometry)PsychologyComputer scienceMathematics educationMedical educationMedicineMathematicsHuman–computer interactionSociologySocial science

Abstract

fetched live from OpenAlex

<p>This paper details the results of a qualitative study examining the reasoning students use to solve common hybridization theory assessment questions and their mental images of hybrid and atomic orbitals. The data were collected through think-aloud interviews as students worked through a five-question questionnaire. Prior to recruitment, the study was deemed to be exempt from IRB review by Sterling IRB. Prior to start of interviews participants provided verbal consent. The resulting transcripts and answers were analyzed following the practices of grounded theory and constant comparative analysis. Coding schemes can be found in the Supplementary Information section. Results, conclusions, and implications for teaching are presented in the manuscript.</p>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.558
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.114
GPT teacher head0.354
Teacher spread0.239 · 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 designQualitative
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

Citations0
Published2020
Admission routes1
Has abstractyes

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