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Record W4401313186 · doi:10.18260/1-2--48022

Student-Generated Infographics and Videos for Learning about Professional Obligations and the Impact of Engineering on Society

2024· article· en· W4401313186 on OpenAlexafffundabout
Lawrence R. Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsMcGill University
FundersMcGill University
KeywordsAccreditationEngineering educationInfographicEngineering ethicsProfessional developmentMultidisciplinary approachEngineeringEngineering managementComputer scienceMedical educationPedagogyPsychologySociologyMedicine

Abstract

fetched live from OpenAlex

Abstract This Complete Evidence-Based Practice paper describes the use of two forms of alternative assessment for students to learn about the professional obligations and responsibilities of engineers as well as the impact of engineering on society. Providing students with opportunities to develop professional skills in both professional and non-professional programs is an important and desirable outcome of higher education. This is especially relevant in engineering, where accreditation bodies such as the Accreditation Board for Engineering and Technology and the Canadian Engineering Accreditation Board, require engineering programs to be designed to emphasize professional skills development, in addition to focusing on scientific and technical knowledge. These professional skills include, but are not limited to, the ability to (1) communicate effectively, (2) function in multidisciplinary teams, and (3) understand professional and ethical responsibility as well as the impact of engineering on society. Over the years, various approaches to provide students with opportunities to develop these skills have been considered, e.g., embedding specific activities in courses such as capstone / design projects or targeted workshops. Student-generated content, such as infographics and videos, have been shown to be effective at promoting active learning, engaging students as they create diverse learning artefacts, promoting critical thinking, and developing digital and communications skills. We describe implementing two alternative assessments-student-generated infographics and videos-in a first-year course on the engineering profession. The topics for the infographic and video address grand engineering challenges and focus on having the students consider the engineer's responsibility to society, how an engineering project might impact society and the environment, and what issues related to ethics, equity, diversity, inclusivity, and accessibility might arise. Working in teams, students perform research, synthesize results, and then communicate them through the creation of an infographic and a video. At the end of the semester, students complete a self-evaluation survey where they are asked to evaluate the usefulness of these two alternative assessments, as well as their perceptions on the professional obligations and the role of the engineer in society. In terms of the infographics and videos, the experience was generally positive; for example, one student commented, 'they provided experience in working with a team to solve open-ended issues, which is extremely important in engineering' and a number of them indicated a desire for similar assessments to be implemented in other engineering courses. Students also reported a better understanding about the role of the engineer in society, how engineering work can impact society, and the responsibilities and obligations of an engineer. Self-evaluation survey results correlated with responses from separate reflective writing exercises given to the students during the semester. Overall, these forms of assessment are useful for enhancing students' learning of the professional obligations and responsibilities of an engineer and the impact of engineering on society.

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)0.000
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 score0.045
Threshold uncertainty score0.249

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 head0.277
Teacher spread0.271 · 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 designSimulation or modeling
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 routes3
Has abstractyes

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