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Record W4405675058 · doi:10.24908/pceea.2024.18606

Faculty Reflections and Adaptations in a First-Year Block Model

2024· article· en· W4405675058 on OpenAlexafffundvenueabout
Andrew Skelton, M. C. George, Kai Zhuang, Patrick Molicard-Chartier, Jeffrey Harris

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsYork University
FundersYork University
KeywordsBlock (permutation group theory)Computer sciencePsychologyMathematicsGeometry

Abstract

fetched live from OpenAlex

The paper highlights the experiences of faculty members in the transition to a block model in engineering education, where courses are taught sequentially in short blocks, blending in-person and online instruction. The block model pilot at a suburban Toronto campus aimed to improve student outcomes, alleviate student stress from heavy workloads and commuting. Kolb’s cycle of experiential education was used as a framework for understanding faculty experiences. Faculty members adapted their teaching methods for condensed learning sessions, supported by development workshops and ongoing pedagogical counseling. Faculty members identified challenges and successes in redesigning courses, emphasizing the importance of faculty development and student-centered programming. It contributes to the scholarship of teaching and learning by documenting the faculty perspective on block model implementation, informing best practices for engineering educators. The findings highlight the potential of the block model to foster active learning communities and suggest areas for future refinement.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.367
Teacher spread0.305 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes4
Has abstractyes

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