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Record W1931354252 · doi:10.24908/pceea.v0i0.5742

USING MICRO-VIDEO PROJECTS IN LARGE ENGINEERING CLASSES TO DIFFERENTIATE ASSESSMENT

2015· article· en· W1931354252 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
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsCarleton University
Fundersnot available
KeywordsRubricClass (philosophy)Computer scienceFrame (networking)MultimediaMathematics educationPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

A new assessment tool is introduced thatuses one minute long, individual micro-videopresentations to give students an opportunity todemonstrate mastery of specific engineering conceptsorally instead of in the typical written form. The shorttime-frame of the videos requires students to thinkcritically about the concept and to explain it concisely. Italso reduces assessment time, allowing teachingassistants to grade it quickly, even in a large class.Three micro-video projects were implemented in athird year and fourth year civil engineering course atCarleton University with 180 and 96 students,respectively. The format of the micro-video tool isdiscussed in detail, including the assessment rubric thatwas used. Three anonymous elective student surveys wereconducted at different stages to solicit student opinions.Students generally thought that the micro-videoassignment was valuable, enjoyable, and provided a fairevaluation of their understanding.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.253
Teacher spread0.237 · 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