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Student Usage of Small Auto-graded MATLAB Coding Exercises

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2018 ASEE Annual Conference & Exposition


Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

The Best of Computers in Education

Tagged Division

Computers in Education

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Paper Authors


Alex Daniel Edgcomb Zybooks

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Alex Edgcomb finished his PhD in computer science at UC Riverside in 2014. Alex works with, a startup that develops interactive, web-native textbooks in STEM. Alex has also continued working as a research specialist at UC Riverside, studying the efficacy of web-native content for STEM education.

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Nikitha Sambamurthy Zybooks

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Nikitha Sambamurthy completed her Ph.D. in engineering education at Purdue University in 2017. Nikitha works with zyBooks, a startup that develops interactive, web-native textbooks for college courses in STEM (science, technology, engineering, and math) disciplines.

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Dasharath Gulvady P.E. MathWorks Orcid 16x16


Santosh Kasula MathWorks

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Santosh Kasula is a Software Engineering Manager for Online Learning Products at MathWorks. MathWorks is the leading developer of mathematical computing software. MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems. Engineers and scientists worldwide rely on these product families to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, financial services, biotech-pharmaceutical, and other industries.

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Instructors are increasingly using small auto-graded coding exercises with immediate feedback to help students learn the MATLAB programming language. Such exercises may require students to write 3 - 10 lines of code. We analyzed student usage of 38 instances of MATLAB coding exercise instances across 1,435 students from seven courses at different universities to determine how students are using the automated MATLAB assessment tool. When instructors suggested completing the exercise (not necessarily requiring or awarding points), we found that student completion rates were on average 83%, with an average per exercise ranging from 64% to 95%. We found that students spent 7.8 minutes on average, matching the 3–10 minutes expected by the exercise authors. We found that students made 4.5 attempts on average per exercise. For some harder exercises, the averages were higher at 12.5 attempts on average and 10.4 minutes on average, suggesting that students were indeed putting forth good effort. Further, we analyzed the students' wrong submissions of exercises that had a high average number of tries. We identified common mistakes by students and shared our best practices for authoring coding exercises.

Edgcomb, A. D., & Sambamurthy, N., & Gulvady, D., & Kasula, S. (2018, June), Student Usage of Small Auto-graded MATLAB Coding Exercises Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31018

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