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Record W4246337384 · doi:10.1128/jmbe.v21i2.2143

Increased Scaffolding and Inquiry in an Introductory Biology Lab Enhance Experimental Design Skills and Sense of Scientific Ability

2020· article· en· W4246337384 on OpenAlexaff
Tess L. Killpack, Sara M. Fulmer, Julie A. Roden, Jocelyne L. Dolce, Christa D. Skow

Bibliographic record

VenueJournal of Microbiology and Biology Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCurriculumMathematics educationComputer scienceData collectionPsychologyPedagogyMathematics

Abstract

fetched live from OpenAlex

We present a model for the process of redesigning the laboratory curriculum in Introductory Organismal Biology to increase opportunities for meaningful inquiry and increase student recognition of their scientific skill development. We created scaffolded modules and assignments to allow students to build and practice key skills in experimental design, data analysis, and scientific writing. Using the Tool for Interrelated Experimental Design, we showed significantly higher gains in experimental design scores in the redesigned course and a more consistent pattern of gains across a range of initial student scores compared with the original format. Students who completed the redesigned course rated themselves significantly higher in experimental design, data collection, and data analysis skills compared with students in the original format. Scores on the Laboratory Course Activity Survey were high for both formats and did not significantly differ. However, on written course evaluations, students in the redesigned course were more likely to report that they engaged in "real science" and their "own experiments." They also had increased recognition of their specific analytical and writing skill development. Our results demonstrate that intentional, scaffolded instruction using inquiry modules can increase experimental design skills and sense of scientific ability in an introductory biology course.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.332
Teacher spread0.292 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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