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Record W1769341240 · doi:10.24908/pceea.v0i0.5748

Using Textbook Readings, YouTube Videos, and Case Studies for Flipped Classroom Instruction of Engineering Design

2015· article· en· W1769341240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsBlackboard (design pattern)Flipped classroomClass (philosophy)Mathematics educationComputer scienceMultimediaPsychologyArtificial intelligenceSoftware engineering

Abstract

fetched live from OpenAlex

Flipped classroom instruction places the transfer of information outside of the class and focuses on the application of the information in the class. Applying flipped classroom instruction to engineering design courses is challenging because design is open-ended.Three approaches were tested for second year and fourth year students taught by the same instructor in six course offerings. All course offerings used case studies. Three offerings were taught using traditional methods such as blackboard notes or PowerPoint presentations. The other course offerings used flipped classroom instruction that applied assigned textbook readings or assigned, instructor-created YouTube videos. A statistical assessment of the final exam scores show that flipped classroom instruction using assigned textbook readings result has a negative impact on final exam performance. YouTube videos and case studies have positive impacts on final exam performance.

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.

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)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.105
GPT teacher head0.360
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