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Writing Effective Autograded Exercises Using Bloom's Taxonomy

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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

Computing and Information Technology Division Technical Session 4

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Computing and Information Technology

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Lina Battestilli North Carolina State University Orcid 16x16

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Lina Battestilli is Teaching Associate Professor of Computer Science at NC State University. She received her Ph.D. in Computer Science from NCSU in August 2005, her masters in Computer Networking in August 2002 also at NCSU and her BS in Electrical Engineering and Minor in Applied Mathematics from Kettering University in 1999.

Prior to joining North Carolina State University in 2012, Dr. Battestilli was a network research engineer at the Next Generation Computing Systems at IBM Research. She worked on the PowerEN Technology, a blur between general purpose and networking processors and hardware accelerators. She identified and studied workloads at the edge of the network that required high-throughput and fast deep-packet processing.

Since 2012, her research has been focused on Computer Science Education, especially in the area of peer collaboration, scaling techniques for large courses, auto-graders and learning analytics. She is also working on software that can be used for teaching and learning. She is investigating techniques and best practices on broadening participation in Computer Science. Women and minorities need to be more involved in tech innovation as companies and teams perform better when there is diversity.

She is also interested in Cloud Networking, Internet Of Things, Software Defined Networks and the design and performance evaluation of networking architectures and protocols, which are areas she worked in while in industry.

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Sarah Korkes North Carolina State University

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Sarah Korkes is a recent graduate of North Carolina State University. She received her B.S. in Computer Science from NCSU in May 2020, and she also minored in Spanish. She is interested in improving Computer Science Education, and has been working in CS Education research since 2018.

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Computer science enrollment continues to grow every year, with class sizes growing into the hundreds. Many instructors in introductory computing courses have turned to auto-graded exercises to ease grading load while still allowing students to practice concepts. As the use of auto-graders becomes more common, it is imperative to ensure that the exercise sets are being written to maximize student benefit. In this paper, we use Bloom's Taxonomy (BT) to create auto-graded exercise sets that scale up from lower to higher levels of complexity. We focus on evaluating learning efficiency, code quality, and student perception of their learning experience. We found that it takes students more submission attempts in the auto-grader when the are given BT Apply/Analyze-type questions that contain some starter code. Students complete the auto-graded assignments with fewer number of submissions when there is no-starter code and they have to write the solution from scratch, i.e. BT Create-type of questions. However, when writing code from scratch, the students' code quality can suffer because the students are not required to actually understand the concept being tested and might be able to find a workaround to pass the tests of the auto-grader.

Battestilli, L., & Korkes, S. (2020, June), Writing Effective Autograded Exercises Using Bloom's Taxonomy Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35711

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