An Engineering Computational Thinking Diagnostic: A Psychometric Analysis

Diaz, Noemi V. Mendoza; Trytten, Deborah A.; Meier, Russ; Yoon, So Yoon

doi:10.1109/fie49875.2021.9637142

Cited by 5 publications

(8 citation statements)

References 5 publications

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“…We support the assertion made in the literature that computational thinking is much more than simple programming skill [11] [12] [13]. In our framework, engineering students mastering computational thinking have mature skills in computational abstraction, solution decomposition, algorithm development and implementation, data representation and analysis, and a respect for how computers impact society [14].…”

Section: Literature Reviewsupporting

confidence: 82%

The Impact of Prior Programming Experience on Computational Thinking in First-Year Engineering Experience.

Mendoza Diaz,

Trytten,

Meier

et al.

2022 CoNECD (Collaborative Network for Engineering &Amp; Computing Diversity) Proceedings

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She obtained her Ph.D. from Texas A&M University in Educational Administration and Human Resource Development and worked as a Postdoctoral Researcher with the Institute for P-12 Engineering Research and Learning-INSPIRE at the School of Engineering Education-Purdue University. She was a recipient of the Apprentice Faculty Grant from the Educational Research Methods ASEE Division in 2009. She also has been an Electrical Engineering Professor for two Mexican universities. Dr. Mendoza is interested in sTEm education, socioeconomically disadvantaged students, Latino studies in engineering and computer aided/instructional technology in sTEm.

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Section: Literature Reviewsupporting

confidence: 82%

The Impact of Prior Programming Experience on Computational Thinking in First-Year Engineering Experience.

Mendoza Diaz,

Trytten,

Meier

et al.

2022 CoNECD (Collaborative Network for Engineering &Amp; Computing Diversity) Proceedings

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“…Since then, there have been several iterations to improve the ECTD based on the psychometric evaluation of items. Table 2 documents each version of the ECTD, the total number of diagnostic questions for each version, and the number of engineering students testing the ECTD for psychometric evaluation [10].…”

Section: A Diagnostic Developmentmentioning

confidence: 99%

Development and Validation of the Engineering Computational Thinking Diagnostic for Undergraduate Students

Mendoza Diaz,

Yoon,

Trytten

et al. 2023

IEEE Access

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Computational thinking is one barrier to enculturating as a professional engineer. We created the Engineering Computational Thinking Diagnostic (ECTD) as an instructional tool that can identify at-risk first-year engineering students. The purpose of this study is to provide construct validity, internal consistency reliability, item characteristics, and criterion validity evidence for this diagnostic. From fall 2020 to fall 2021, 469 students from three institutions in the United States took the diagnostic. The data from 152 students at one institution was used to provide evidence of predictive validity. Exploratory and confirmatory factor analyses resulted in 20 items loading onto one factor in a good model fit range, with the internal consistency reliability coefficient, Cronbach α of 0.86. From item analyses based on classical test theory, the diagnostic items on average tended to be slightly easy but had sufficient discrimination power. The correlation matrix for criterion validity evidence indicated that the diagnostic functions well to differentiate students' computational thinking ability by prior computer science course experience as well as by first-generation status. Predictive validity evidence from regression analyses revealed the statistically significant effect of students' diagnostic scores assessed at the beginning of the first semester on predicting their end of semester course grades. The ECTD can have a broad impact because it provides a tool to gauge the entry-level skills of students, enabling early curriculum interventions to help retention and persistence to graduation. We make the case that the ECTD could contribute to the development of a more diverse workforce in engineering.

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“…Other further details on the demographic data are not considered in the retrospective analyses of strictly performance via grades data. Consideration of demographic data is left for other research that go deeper into the students and course topic 32,33 .…”

Section: Studentsmentioning

confidence: 99%

Student performance impacted from modifying a first-year/semester engineering core course during a global pandemic

Richard

Moore

2022

2022 IEEE Frontiers in Education Conference (FIE)

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He got his Ph.D. at Rensselaer Polytechnic Institute. He studies plasmas using particle and spectral methods, as well as engineering education factors that build critical algorithmic thinking skills in diverse engineering students. Research includes plasma turbulence and plasma jets, and jet engine turbomachinery instabilities, for which he received NASA Performance Cash awards. Dr. Richard is involved in tutoring, mentoring, and outreach and teaches first-year introductory engineering, fluid mechanics, and space plasma propulsion. He has authored/co-authored 45+ peer-reviewed journal and conference papers.

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An Engineering Computational Thinking Diagnostic: A Psychometric Analysis

Cited by 5 publications

References 5 publications

The Impact of Prior Programming Experience on Computational Thinking in First-Year Engineering Experience.

The Impact of Prior Programming Experience on Computational Thinking in First-Year Engineering Experience.

Development and Validation of the Engineering Computational Thinking Diagnostic for Undergraduate Students

Student performance impacted from modifying a first-year/semester engineering core course during a global pandemic

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