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Record W4389120922 · doi:10.21432/cjlt28012

Exploring Blended Learning Designs for Community College Courses Using Community of Inquiry Framework

2023· article· en· W4389120922 on OpenAlexaffvenue
Elena Chudaeva, Cynthia S. Blodgett, Guilherme Loth, Thuvaragah Somaskantha

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsPrincess Margaret Cancer CentreAthabasca UniversityCrandall UniversityGeorge Brown College
Fundersnot available
KeywordsBlended learningCommunity of inquiryBlackboard (design pattern)Flexibility (engineering)MultimethodologyMathematics educationInstructional designComputer scienceCommunity collegeTeaching methodPsychologyEducational technologyMedical educationCognitionMathematics

Abstract

fetched live from OpenAlex

The goal of this single-phase and convergent mixed methods study was to compare the differences in the effectiveness of the Community of Inquiry (CoI) presences of a community college blended block instructional model with the in-person counterpart. Data were gathered from the Community of Inquiry Survey, Blackboard LMS reports, and course evaluation surveys. The results indicate that students had a better overall experience with the blended course. The blended block model provided flexibility while achieving course goals. Further, findings reveal differences in all three CoI presences between the two course formats with more student awareness of the presences in the in-person 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 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)0.013
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.238
GPT teacher head0.383
Teacher spread0.145 · 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 designQualitative
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

Citations3
Published2023
Admission routes2
Has abstractyes

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