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Preliminary Results from Implementing a Data-driven Team Project in an Introductory Risk and Uncertainty Analysis Class for Sophomore Civil and Environmental Engineering Students

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2020 ASEE Virtual Annual Conference Content Access


Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Star Tech: Bringing Data Science and Technologies into the Classroom

Tagged Division

Civil Engineering

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Sotiria Koloutsou-Vakakis University of Illinois at Urbana - Champaign Orcid 16x16

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Dr. Sotiria Koloutsou-Vakakis holds a Diploma degree in Civil-Surveying Engineering (National Technical University of Athens, Greece), a M.A. in Geography (University of California, Los Angeles), and M.S. and Ph.D. degrees in Environmental Engineering (University of Illinois at Urbana-Champaign). She teaches undergraduate and graduate courses on Air Quality, Science and Environmental Policy, and Engineering Risk and Uncertainty. Her recent research is about gaseous emissions of reactive nitrogen from fertilized fields into the atmosphere and impacts on air quality and climate change, and implementing process and project learning in introductory fundamentals classes.

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Preliminary results from implementing a data driven team project in introductory risk and uncertainty analysis class for sophomore civil and environmental engineering students

ABSTRACT Within the scope of department-wide curriculum modernization, use of R and a data driven team project were introduced in a sophomore level, required course for civil and environmental engineering (CEE) students. The class has a total enrollment of approximately 200 students per year, split into two academic semesters. Preliminary outcomes are based on 103 students (Spring 2019 class). Consistent with pedagogical benefits of team projects and with curriculum goals, the motivational goals of developing a class project were to: 1) encourage student involvement and overcome the student indifference barrier for this class, by connecting students in teams for a semester-long common goal, toward meeting ultimate outcome of deeper understanding and learning; 2) promote student confidence in formulating questions and then identifying, acquiring and analyzing real datasets from existing databases to answer the questions students themselves posed; 3) facilitate material comprehension and induce critical thinking; 4) strengthen coding skills by using R’s powerful data oriented computational environment, for addressing real world questions of social or technical interest; 5) strengthen written communication skills; 6) promote teamwork skills; 7) take first exploratory steps into introducing data science early in the CEE curriculum.

Quantitative and qualitative assessment of this first implementation of the data driven team project is based on four sets of available information: 1) Grading of submitted work for each of five project parts, based on a set of rubric criteria; 2) Student peer teammate evaluation, submitted at the end of each project part; 3) Student reflection of their project experience, submitted at the end of the semester; and 4) Informal assessment from instructor interaction with the student teams, during project consultation times throughout the semester. This first implementation is valuable for informing future improvements. Segmentation of the project in parts to follow as the material was covered in class, turned out to be the major challenge for most student teams, especially the ones who chose a topic for which they lacked basic contextual understanding. This was mostly remedied by the time students started working on the final part of the project. Overall, the project contributed to improved student understanding of the material, as evidenced by the quality of writing and interpretation in context of their results in their final paper submissions, quantified by the grades in the respective rubric criteria.

Koloutsou-Vakakis, S. (2020, June), Preliminary Results from Implementing a Data-driven Team Project in an Introductory Risk and Uncertainty Analysis Class for Sophomore Civil and Environmental Engineering Students Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35077

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