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Record W3189970214 · doi:10.21432/cjlt27905

Web Enhanced Flipped Learning: A Case Study

2021· article· en· W3189970214 on OpenAlexaffvenue
Bani Arora, Naman Arora

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBlended learningClass (philosophy)Educational technologyMathematics educationFlipped classroomLearning ManagementMobile deviceFlipped learningMultimediaPsychologyElectronic learningTeaching methodComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This research study is a technology-enhanced flipped learning pilot to observe the students’ engagement and learning in a self-regulated class through their individual feedback. Flipped learning was applied to a segment of the Study Skills course for more than two weeks to 129 students in the foundation year of a Teachers’ College in Bahrain. Divided across four sections, the students worked in small groups, prepared an assigned portion of the course content provided through a Learning Management System (LMS), and presented it to the rest of the class. Students used posters, flash cards, and digital technology in different forms such as PowerPoint slides, mobile phones, and Kahoot!. The reflective individual student responses on this experience were analysed quantitatively and qualitatively. The findings show a favourable response to group work, sharing ideas, saving time through collaboration, and use of technology. It is recommended that the study be extended to a larger sample group, to a larger number of the course topics, and include the use of different technology forms.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.355
Teacher spread0.329 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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