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Record W3188103089 · doi:10.21432/cjlt27917

Students’ Learning Experience with a Flipped Introductory Organic Chemistry Course: A Course Designed for Non-Chemistry Majors

2021· article· en· W3188103089 on OpenAlexafffundvenue
Horace Luong, Thomas Falkenberg, Mahdi Rahimian

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsQueen's UniversityUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsFlipped classroomBlended learningMathematics educationPopularityChemistryCourse (navigation)Course evaluationChemistry educationActive learning (machine learning)PsychologyEducational technologyTeaching methodCooperative learningHigher educationPedagogyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The use of flipped classrooms has been gaining popularity across various disciplines as student-centered active learning pedagogy. In this study, an introductory organic chemistry course was delivered through flipped classroom methodologies, incorporating blended learning to deliver content and group work in the classroom. The learning experiences of non-chemistry majors students enrolled in the course are discussed. The majority of the students adjusted to the pedagogy and felt autonomy in their learning. These students also believed that most of the flipped classroom design components were a good use of their time in learning organic 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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.321
Teacher spread0.310 · 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 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

Citations4
Published2021
Admission routes3
Has abstractyes

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