MétaCan
Menu
Back to cohort
Record W2794158568 · doi:10.1039/c7rp00220c

Developing communication confidence and professional identity in chemistry through international online collaborative learning

2018· article· en· W2794158568 on OpenAlexaffabout
Darlene Skagen, Brett McCollum, Layne A. Morsch, Brandon Shokoples

Bibliographic record

VenueChemistry Education Research and Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsMount Royal UniversityCalgary Laboratory Services
Fundersnot available
KeywordsIdentity (music)ChemistryPsychologyChemistry educationMathematics educationProfessional developmentPedagogyMedical educationMedicineSocial psychologyPhysics

Abstract

fetched live from OpenAlex

The use of online collaborative assignments (OCAs) between two flipped organic chemistry classrooms, one in Canada and the other in the United States, was examined for impact on learners. The intervention was designed to support content mastery, aid in increasing students’ communication skills through chemistry drawing and verbalization, facilitate emergence of professional identity, and promote development of appreciation for chemistry as an international language. A mixed-methods approach consisting of interviews, student written reflections, and questionnaires was used to evaluate the impact of the OCAs. Students described their experience of the OCAs in terms of: chemistry communication confidence; engaged learning; chemistry learning; relationships; and professional identity.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.126
GPT teacher head0.582
Teacher spread0.456 · 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 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

Citations35
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueChemistry Education Research and PracticeSame topicHigher Education Learning PracticesFrench-language works237,207