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Record W2603100236 · doi:10.18260/p.24740

Strategies for Creating Engagement in Civil Engineering Students in Lecture Scenarios

2015· article· en· W2603100236 on OpenAlexaffabout
Alan Chong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStudent engagementPresentation (obstetrics)Engineering educationDisciplineSubject (documents)EngineeringComputer scienceCivil engineeringEngineering ethicsEngineering managementPedagogySociologyWorld Wide WebMedicineSocial science

Abstract

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Abstract Strategies for Creating Engagement in Civil Engineering Students in LectureScenariosTeaching a required engineering communication course to sophomore civil engineers, especially in alarge classroom lecture setting, presents some significant challenges. Students in general face anumber of roadblocks to engagement in lecture settings, including lecture/PowerPoint fatigue,physical fatigue from their demanding workloads, and the distraction of other often more pressingcourse commitments (such as upcoming exams or assignments). These challenges are exacerbatedwhen the subject being taught is not particularly conducive to lecture-style teaching or perceived asrelevant to their disciplinary knowledge base, such as engineering communication. In such scenarios,strategies for creating engagement at the beginning of lectures are crucial to gaining and maintainingstudent attention, and creating the student buy-in that is key to their learning.The discipline of civil engineering, however, is unique from other engineering disciplines inpresenting numerous opportunities for engagement with cultural touchstones relevant to students ofall levels. The general population engages daily with the products of civil engineering by usinginfrastructure, such as roads, buildings, and water systems. Our connection to these artifacts of civilengineering are reflected in products such as popular music, film, and other media which holdcultural currency with students. This presentation explores strategies that take advantage of thisstrong connection between civil engineering and culture to create engagement for civil engineeringstudents in lecture settingsWe examine three strategies developed and piloted during a single semester course on EngineeringCommunication in Civil Engineering. In this presentation, we will first play “Civil EngineeringThemed Musical Trivia,” in which students compete within the lecture classroom to identify the titleand artist for songs with either a titular or lyrical connection to civil engineering. Songs range fromthe highly contemporary - such as Miley Cyrus’ Wrecking Ball and Demi Levato’s Skyscraper - to olderclassics - such as Simon and Garfunkel’s Bridge Over Troubled Water or Joni Mitchell’s Big Yellow Taxi.Second, we examine how both new and old multimedia, such as A Short History of the High-rise — acollaboration between the New York Times and the National Film Board of Canada — or films byEdward Burtynsky, such as Manufactured Landscapes and Watermark, can be introduced prior to lectureto get students thinking about important engineering concepts such as sustainability. Finally, weexplore how local, municipal political issues – such as the highly transportation focused [cityredacted] mayoral race - can be used (in a non-partisan way) to demonstrate the significance of theirchosen discipline to their daily lives.All of these strategies, which take 2-5 minutes at the beginning of the lecture, encourage students tofocus their attention and engage with the material being presented, with the hope that this attentionwill be carried over to the lecture material. During the presentation, these strategies will bedemonstrated to the audience, and their impact on student engagement over the course of a classdiscussed, using data from student evaluations, student-instructor interactions, and lectureexperience.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0040.010
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0140.005

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 head0.059
GPT teacher head0.373
Teacher spread0.315 · 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 designObservational
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".

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Citations1
Published2015
Admission routes2
Has abstractyes

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