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Student use of learning resources to understand antimicrobial actions: use in hybrid vs on-line format (37.6)

2009· article· en· W942555913 on OpenAlexaff
Lysa Samuel, Bill Muirhead, Julia M. Green-Johnson

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsComputer scienceOnline learningObject (grammar)Learning objectActive learning (machine learning)Mathematics educationMedical educationMultimediaWorld Wide WebPsychologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Increased demand for online computer-based instructional resources has led to the need for effective utilization of learning objects in health biology courses. Our aim was to examine how well learning objects assist students in understanding challenging concepts in antimicrobial activity against infectious disease. Our specific focus was on learning object use in an introductory microbiology and immunology course in two different settings: a fully on-line section and a hybrid section, which integrates online materials with face-to-face lectures. To collect information on use of the learning objects, an anonymous survey was given to both sections. Survey participation rates varied, with a 56% rate from the online course, and only 28% from the hybrid course. Survey data analysis indicated many similarities between online and hybrid sections in learning object utilization, including high usage as study material for examinations. Differences were observed in the positive impact learning resources have on students learning difficult concepts, with the online course showing 100% agreement, and the hybrid course only 76% agreement, suggesting differences in learning object utilization for learning challenging new concepts. Qualitative responses provided further indications for effective learning object integration into differentiated instruction formats. Funded by UOIT’s Teaching Innovation Fund.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.247

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.312
Teacher spread0.255 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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