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Record W4319067661 · doi:10.1021/acs.jchemed.2c00776

A Scaffolded Assessment on Chromatography Theory for Analytical Chemistry Classrooms

2023· article· en· W4319067661 on OpenAlexaff
Yu Pei, Sarah Gulycz, Zhe She, Amanda Bongers

Bibliographic record

VenueJournal of Chemical Education · 2023
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsQueen's University
Fundersnot available
KeywordsSummative assessmentClass (philosophy)ChemistryMathematics educationProcess (computing)Formative assessmentComputer scienceChromatographyPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

We describe a scaffolded assessment activity that introduces students to the essential theories and principles of chromatography. This summative assessment was redesigned to introduce scaffolded and “fill-in-the-blank” questions about the chromatographic separation of mixtures. In addition to analyzing chromatograms and interpreting data, the assignment was designed to help learners define the array of chromatographic terms and to engage students in scientific practices by having them actively reason about several variables and identify relationships between various technical terms to solve multistep calculations. The activity was successfully implemented in a remote analytical chemistry class, with learning gains evidenced by the subsequent midterm exam. Students self-reported their achievement of the intended learning outcomes as well as their enjoyment of the activity and its structure. We found the new scaffolded design to be attractive for helping students develop process skills for problem-solving in analytical chemistry.

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)0.004
metaresearch head score (Gemma)0.016
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: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.021
GPT teacher head0.358
Teacher spread0.337 · 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
GenreMethods

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

Citations1
Published2023
Admission routes1
Has abstractyes

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