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Record W2793146308 · doi:10.4018/ijavet.2018010101

Looking Back and Looking Forward

2018· article· en· W2793146308 on OpenAlexaffabout
Maurice Taylor, Norman Vaughan, Shehzad Ghani, Sait Ataş, Michael Fairbrother

Bibliographic record

VenueInternational Journal of Adult Vocational Education and Technology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsMount Royal UniversityUniversity of Ottawa
Fundersnot available
KeywordsBlended learningAndragogyFlexibility (engineering)Flipped classroomPedagogyHigher educationPsychologySociologyFaculty developmentProfessional developmentMathematics educationEducational technologyAdult educationPolitical scienceManagement

Abstract

fetched live from OpenAlex

Using an engagement in research approach this article explores the landscape of blended learning in higher education over the last decade by comparing the results of a critical literature review by Vaughan to an instrumental case study that identified key factors that led to the implementation of a blended learning initiative in one medium sized Canadian university. Findings indicate that although students still prefers the time flexibility of blended learning, there are major differences between undergraduate and graduate students and their motivation for choosing this pedagogy. Professors also find increased teacher-student interactions using a blended learning format but acknowledge more support for course redesign and better professional development and training. From an administrator's viewpoint, one of the main challenges occurred at the individual faculty level in trying to communicate the definition of blended learning to professors. As a way of looking forward, interviews with experts from various Ontario universities and a survey of university personnel from across the country provided some initial insights. A discussion situates the findings using the theoretical lens of andragogy, self-directed learning, the community of inquiry framework, and points to a possible range of additional research questions for 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

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.251

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 head0.008
GPT teacher head0.344
Teacher spread0.335 · 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 designNot applicable
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

Citations33
Published2018
Admission routes2
Has abstractyes

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