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Record W3015831627 · doi:10.1002/bmb.21350

Using anticipated learning outcomes for backward design of a molecular cell biology Course‐based Undergraduate Research Experience

2020· article· en· W3015831627 on OpenAlexaff
Melissa J. Hills, Kimberley Harcombe, Nina Bernstein

Bibliographic record

VenueBiochemistry and Molecular Biology Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSummative assessmentFormative assessmentVariety (cybernetics)Mathematics educationPlan (archaeology)Undergraduate researchAdaptation (eye)Medical educationComputer scienceCourse (navigation)PsychologyEngineeringBiologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Anticipated learning outcomes (LOs) were defined and used for the backward design of a Course-based Undergraduate Research Experience (CURE). These LOs reflect the inquiry-based nature of CUREs and capture key knowledge and skills inherent to scientific practice and essential in research. The LOs were used to plan a formative and summative assessment strategy to support and evaluate student achievement. A research question was identified that aligned with the learning goals of the course, provided an opportunity for discovery and iteration, and introduced a variety of molecular, cellular, and biochemical techniques. The course is offered to students in the final year of their degree and delivered over a 12-week period with two 3-hr labs each week. These LOs, and the rigorous assessment strategy used to support them, could be adapted to different projects. Likewise, the laboratory exercises are presented as a series of modules highlighting opportunities for adaptation to a variety of schedules.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.059
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.145
GPT teacher head0.437
Teacher spread0.291 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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