F5-COMP5-2 - Computational Thinking Growth During a First University Course

3. Research Full Paper
NOEMI V MENDOZA DIAZ1 , RUSS MEIER2, DEBORAH TRYTTEN3, SO YOON YOON4
1 Texas A&M University
2 Milwaukee School of Engineering
3 University of Oklahoma
4 University of Cincinnati

This full research-track paper summarizes the growth in computational thinking in a cohort of engineering students completing a first course in freshman engineering at a large southwestern university in the United States. The course included topics in mathematics, engineering problem solving, and computation. Pre-test and post-test quantitative data analysis from an Engineering Computational Thinking Diagnostic developed and validated by the authors is presented to document student growth in this cohort of more than 800 students. The research contribution is the establishment of a reliable metric of student learning in computational thinking. This metric is of interest and relevance to all institutions providing training in engineering and computing.   

Computational thinking, understood as the development of skills and knowledge in computers and technology, has been acknowledged as one key aspect in the taxonomy of engineering education and an intrinsic part of multiple ABET outcomes. Moreover, most introductory engineering courses worldwide have a component of programming or computational thinking. A preliminary study of enculturation to the engineering profession conducted at the same university found that computational thinking was deemed a critical area of development at the early stages of instruction.

A team of researchers at three different institutions completed a comprehensive literature review and discovered that no existing computational thinking framework fully met the needs of students and professors in engineering and computer science.  As a result, the team created an engineering computational thinking diagnostic (ECTD). This diagnostic was validated during the 2019-2020 academic year and data was collected from the cohort. The goal was to capture the proper skill level of participants as they entered their first semester at university and when they completed the course. The short-term impact of this research is 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 bias-independent measurement of teaching effectiveness.

Keywords: computational thinking, learning and teaching effectiveness, assessment metrics