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Record W4281392383 · doi:10.1021/acs.jchemed.1c01008

Virtual Poster Session Designed for Social Cognitive Learning in Undergraduate Chemistry Research

2022· article· en· W4281392383 on OpenAlexaff
Amanda Bongers

Bibliographic record

VenueJournal of Chemical Education · 2022
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsQueen's University
Fundersnot available
KeywordsSession (web analytics)Undergraduate researchChemistry educationComputer scienceMathematics educationChemistryEngineering physicsHuman–computer interactionMultimediaPsychologyEngineeringMedical educationWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

Research poster sessions are an excellent example of how scientists rely not only on technical skills but also on interpersonal interactions, communication, and other behaviors learned from participating in social environments. This process of learning is described by social cognitive theory, and particularly its aspects of self-regulation, self-reflection, and self-efficacy. Social learning and cognitive apprenticeship models were used to design a virtual poster session for upper-year students doing research thesis projects. Interactions (reactions, comments, and calls) among students, faculty, and graduate students were examined through a social network analysis of the session, and the emerging communication patterns were related to students’ abilities to observe, model, and articulate behaviors in the virtual setting. A survey of student experiences provided insight into the session’s outcomes and students’ self-efficacy beliefs after the session. The poster session succeeded at creating a virtual space for social learning, reflecting on social norms in science, and for asking questions about research through a cognitive apprenticeship model.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.108
GPT teacher head0.496
Teacher spread0.388 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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