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Record W2765406927 · doi:10.4018/ijopcd.2018010105

A Pathway Towards Implementation of Blended Learning in a Medium Sized Canadian University

2017· article· en· W2765406927 on OpenAlexaffabout
Maurice Taylor, Shehzad Ghani, Sait Ataş, Michael Fairbrother

Bibliographic record

VenueInternational Journal of Online Pedagogy and Course Design · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBlended learningHigher educationMeaning (existential)PedagogyEmpirical researchSociologyMathematics educationPsychologyEducational technologyPolitical science

Abstract

fetched live from OpenAlex

As blended learning increases in higher education, there still remains a dearth of empirical evidence that focuses on how institutions actually adopt such initiatives. The purpose of this instrumental case study was to explore the key factors that led to the adoption and implementation of a blended learning initiative in one medium sized Canadian university. Three research questions guided the study that was nested in the Community of Inquiry framework. Data sources included a total of 83 semi-structured interviews with students, professors, and administrators; 32 student and instructor artefacts and documents and three sets of researcher field notes. Findings indicate that a university-wide initiative needs to integrate both the lived experiences of undergraduate and graduate students in blended learning which are very different. Adoption also recognizes that as professors come to understand the meaning of blended learning, their knowledge needs and teaching practices change. These changes need to be reflected in as training is developed. In addition, widespread implementation involves several critical factors that happen at both the institutional and individual faculty level. The discussion focuses on several key markers that need to be considered along a university-wide pathway towards blended learning.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
models splitAgreement compares identical category sets and study designs across arms.

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 score0.924
Threshold uncertainty score0.965

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 communication0.0000.000
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 head0.039
GPT teacher head0.400
Teacher spread0.361 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreEmpirical · Commentary

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

Citations8
Published2017
Admission routes2
Has abstractyes

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