This research-track work-in-progress paper contributes to engineering education by documenting progress in developing a new standard Engineering Computational Thinking Diagnostic to measure engineering student success in five factors of computational thinking. Over the past year, results from an initial validation attempt were used to refine diagnostic questions. A second statistical validation attempt was then completed in Spring 2021 with 191 student participants at three universities. Statistics show that all diagnostic questions had statistically significant factor loadings onto one general computational thinking factor that incorporates the five original factors of (a) Abstraction, (b) Algorithmic Thinking, (c) Decomposition, (d) Data Representation and Organization, and (e) Impact of Computing. This result was unexpected as our goal was a diagnostic that could discriminate among the five factors. A small population size caused by the virtual delivery of courses during the COVID-19 pandemic may be the explanation and a third round of validation in Fall 2021 is expected to result in a larger population given the return to face-to-face instruction.
When statistical validation is completed, the diagnostic will help institutions identify students with strong entry level skills in computational thinking as well as students that require academic support. The diagnostic will inform curriculum design by demonstrating which factors are more accessible to engineering students and which factors need more time and focus
in the classroom. The long-term impact of a successfully validated computational thinking diagnostic will be introductory engineering courses that better serve engineering students coming from many backgrounds. This can increase student self- efficacy, improve student retention, and improve student enculturation into the engineering profession. Currently, the diagnostic identifies general computational thinking skill
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Computational Thinking Growth During a First-Year Engineering Course
This full research-track paper demonstrates growth in
computational thinking in a cohort of engineering students
completing their first course in engineering at a large
Southwestern university in the United States. Computational
thinking has been acknowledged as a key aspect of engineering
education and an intrinsic part of multiple ABET outcomes.
However, computing is an area where some students have more
privileges (e.g. access and exposure to meaningful use of
computers) than others. Integrating computing into engineering,
especially early in the curriculum, may exacerbate existing
experiential disadvantages students from excluded social
identities experience. Most introductory engineering programs
have a component of programming and/or computational
thinking. A comprehensive literature review showed that no
existing computational thinking framework fully met the needs of
students and professors in engineering and computer science. As
a result, this team created the Engineering Computational
Thinking Diagnostic (ECTD). This diagnostic was assessed and
improved during the 2019-2020 academic year. Data was
collected from a cohort in a first-year engineering course that
included topics in mathematics, engineering problem solving, and
computation. Pre- and post-test data analysis with 62
participants documents statistically significant student growth in
computational thinking in this course. Significant differences
were not found by gender or a limited racially-based analysis.
This diagnostic is of interest and relevance to all institutions
providing engineering and computing programs. The short-term
impact of this research includes an innovative approach to gauge
student abilities in computational thinking early in a course in
order to add appropriate intervention activities into lesson plans.
The long-term impact is the creation of a measurement of student
learning of computational thinking in engineering for courses
and programs that wish to develop this important skill in their
students.
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- Award ID(s):
- 1917352
- NSF-PAR ID:
- 10283819
- Date Published:
- Journal Name:
- 2020 IEEE Frontiers in Education Conference (FIE)
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Computational thinking is an important skill in the formation of engineers. Many schools of engineering include a programming course during the first-year. In 2019, NSF funded the “Collaborative Research: Research in Improving Computational Thinking in the Formation of Engineers, a Multi-Institutional Initiative.” The project’s goal is to improve the way computational thinking is taught at the college engineering level via the understanding of the multiple factors that affect computational thinking development. The project’s research questions are: · Research Question 1: How does the integration of computing into the foundational engineering courses affect the formation of engineers? · Research Question 2: In what ways do social identities (e.g. gender, ethnicity, first generation college attending, socioeconomic status), choices (e.g. major, transfer status), and other factors impact the engineering student experience with computational thinking? · Research Question 3: In what ways do computational thinking skills develop over time in engineering students? In order to respond to these questions, the research team developed a Computational Thinking Hybrid Framework, an Engineering Computational Thinking Diagnostic (ECTD) and a Position of Stress Questionnaire. Amid COVID-19, the advances of the project include approximately 2000 participants responding to the diagnostic during the Fall of 2019 and the Fall of 2020. With this participation, two cycles of validation have taken place for the ECTD and results are presented in this poster session. The factors validated in this diagnostic are (1) Abstraction, (2) Algorithmic Thinking and Programming, (3) Data Representation, Organization, and Analysis, (4) Decomposition, and (5) Impact of Computing. The positions of stress have been collected for the Fall of 2020 and preliminary results are also presented in this session.more » « less
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null (Ed.)Computational thinking is understood as the development of skills and knowledge in how to apply computers and technology to systematically solve problems. Computational thinking has been acknowledged as one key aspect in the taxonomy of engineering education and implied in multiple ABET student outcomes. Moreover, many introductory engineering courses worldwide have a component of programming or computational thinking. A preliminary study of enculturation to the engineering profession found that computational thinking was deemed a critical area of development at the early stages of instruction. No existing computational thinking framework was found to fully meet the needs of engineers, based on the expertise of researchers at three different institutions and the aid of a comprehensive literature review. As a result, a revised version of a computational thinking diagnostic was developed and renamed the engineering computational thinking diagnostic (ECTD). The five computational thinking factors of the ECTD are (1) Abstraction, (2) Algorithmic Thinking and Programming, (3) Data Representation, Organization, and Analysis, (4) Decomposition, and (5) Impact of Computing. This paper describes the development and revisions made to the ECTD using data collected from first-year engineering students at a Southwestern public university. The goal of the development of the ECTD is to capture the entry and exit skill levels of engineering students in an engineering program.more » « less
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