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Record W4391562024 · doi:10.18260/1-2--40770

Work in Progress: Designing a First-Year Hands-on Civil Engineering Course to Reduce Students Dropout and Improve the Overall College Experience

2024· article· en· W4391562024 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Toronto
FundersUniversidad San Francisco de QuitoIowa State University
KeywordsDropout (neural networks)Course (navigation)Computer scienceWork (physics)Mathematics educationEngineering managementEngineeringPsychologyMechanical engineeringMachine learningAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Civil engineering dropout is a complex construct because it involves many dimensions, some of which come from societal status quo. There is significant research on understanding the barriers, both personal and institutional, that cause student desertion throughout the major. This paper presents a work in progress that analyzes the designing process of a first-year introductory course in civil engineering that aims to reduce students' dropout and improve students' overall college experience. The study showcases the design process of the course including the pedagogy for teaching the course, the course content, the hands-on learning activities, instructor characteristics, and the diverse distribution of course assessment. The course aims to show a taste of the experience expected for the next college years. The authors reflect on the design of the course in a civil engineering department and open the discussion to develop new pedagogical approaches to reduce student dropout in civil engineering schools. Implications for research and practice are provided.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.220
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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