IUSE Computational Creativity: Improving Learning, Achievement, and Retention in Computer Science for CS and non-CS Undergraduates

Markeya S. Peteranetz; Duane F. Shell; Leen-Kiat Soh; Elizabeth Ingraham; Abraham Flanigan

Download Paper | Permalink

Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Practice I: Academic Success
Tagged Division: Educational Research and Methods
Page Count: 6
DOI: 10.18260/1-2--30740
Permanent URL: https://peer.asee.org/30740
Download Count: 436

Duane Shell is Research Professor of Educational Psychology at the University of Nebraska-Lincoln. His primary research areas are learning, self-regulation, and motivational influences on behavior and cognition as these are manifest in education and public health settings. Dr. Shell specializes in multivariate, multidimensional analyses of complex relationships between motivation, classroom factors, self-regulation, and learning. He is primary author of the Unified Learning Model. In addition to his primary research, he has 32 years experience as an evaluator on federal, state, and foundation grands.

visit author page

biography

Leen-Kiat Soh University of Nebraska, Lincoln

visit author page

Dr. Leen-Kiat Soh is a Professor at the Computer Science and Engineering Department at the University of Nebraska. His research interests are in multiagent systems, computer-aided education, computer science education, and intelligent image analysis. He has applied his research to smart grids, computer-supported collaborative learning, survey informatics, geospatial intelligence, and intelligent systems, and He is a member of IEEE, ACM, and AAAI.

visit author page

biography

Elizabeth Ingraham University of Nebraska, Lincoln

visit author page

Elizabeth Ingraham is Associate Professor in the School of Art, Art History & Design at the University of Nebraska-Lincoln. A Fellow of the Center for Great Plains Studies, she teaches design and computational creativity at UNL and received the Sorensen Award for excellence in humanities teaching. A sculptor whose work gives form and voice to lived experience, she won the Thatcher Hoffman Smith Award for Creativity for her series of life-size sewn fabric “skins” sculptures. Her recent solo exhibition at the International Quilt Study Center & Museum showcased the result of more than 9,000 miles of travel across Nebraska for her project, "Mapping Nebraska"—a stitched, drawn and digitally imaged cartography of the state (physical and psychological) where she resides. Her research into computational creativity is part of her on-going interest in combining the digital (pixels and code) with the digital (the work of the hand).

visit author page

author page

Abraham Flanigan

Download Paper | Permalink

Abstract

This project is funded through the NSF Improving Undergraduate STEM Education initiative and seeks to enhance undergraduate computer science (CS) education by teaching computational creativity in both CS and non-CS courses. Computational creativity integrates computational thinking and creative thinking so that each can be used to enhance the other in improving student learning and performance in class. Whereas computational thinking brings a structured and analytic approach to problem-solving situations, creative thinking introduces novelty and innovative, non-standard solutions. Computational creativity has been incorporated into numerous undergraduate CS and non-CS courses through a series of Computational Creativity Exercises (CCEs). Previous research indicates CCEs enhance students learning of computational thinking principles and boost course grades, even after controlling for students’ general academic achievement and individual differences in motivation and self-regulation. The effect of CCEs on achievement has been observed for both introductory and upper-level courses and for both men and women. The present quasi-experimental study examined the impact of CCEs on students’ (N = 670) retention in CS courses. CCEs were incorporated into 100- and 200-level CS courses at a single university during the fall of 2015. Students in these classes consented to having their course enrollment data collected for the following three semesters (retention semesters). Students in 100- and 200-level CS courses during the fall of 2014 and spring of 2015 semester also consented to having their ongoing course enrollment data collected and were used as a comparison group. The impact of the CCEs on retention was tested through chi square analysis for each of the three retention semesters. Results indicate students in the implementation semester courses were more likely to continue taking CS in each of the three retention semesters (Cramer’s Vs from 0.144 to 0.193), and the effect was even greater when comparing students who did no CCEs (in either the implementation or control groups) and those who completed at least two CCEs (Cramer’s Vs from 0.153 to 0.254). Thus, these results suggest that in addition to the impact CCEs have on achievement, CCEs also increase the likelihood that CS students will continue to take CS courses.

Citation
Format

Peteranetz, M. S., & Shell, D. F., & Soh, L., & Ingraham, E., & Flanigan, A. (2018, June), IUSE Computational Creativity: Improving Learning, Achievement, and Retention in Computer Science for CS and non-CS Undergraduates Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30740

TY - CPAPER
AB - This project is funded through the NSF Improving Undergraduate STEM Education initiative and seeks to enhance undergraduate computer science (CS) education by teaching computational creativity in both CS and non-CS courses. Computational creativity integrates computational thinking and creative thinking so that each can be used to enhance the other in improving student learning and performance in class. Whereas computational thinking brings a structured and analytic approach to problem-solving situations, creative thinking introduces novelty and innovative, non-standard solutions.
Computational creativity has been incorporated into numerous undergraduate CS and non-CS courses through a series of Computational Creativity Exercises (CCEs). Previous research indicates CCEs enhance students learning of computational thinking principles and boost course grades, even after controlling for students’ general academic achievement and individual differences in motivation and self-regulation. The effect of CCEs on achievement has been observed for both introductory and upper-level courses and for both men and women.
The present quasi-experimental study examined the impact of CCEs on students’ (N = 670) retention in CS courses. CCEs were incorporated into 100- and 200-level CS courses at a single university during the fall of 2015. Students in these classes consented to having their course enrollment data collected for the following three semesters (retention semesters). Students in 100- and 200-level CS courses during the fall of 2014 and spring of 2015 semester also consented to having their ongoing course enrollment data collected and were used as a comparison group. The impact of the CCEs on retention was tested through chi square analysis for each of the three retention semesters. Results indicate students in the implementation semester courses were more likely to continue taking CS in each of the three retention semesters (Cramer’s Vs from 0.144 to 0.193), and the effect was even greater when comparing students who did no CCEs (in either the implementation or control groups) and those who completed at least two CCEs (Cramer’s Vs from 0.153 to 0.254). Thus, these results suggest that in addition to the impact CCEs have on achievement, CCEs also increase the likelihood that CS students will continue to take CS courses.

AU - Markeya S. Peteranetz
AU - Duane F. Shell
AU - Leen-Kiat Soh
AU - Elizabeth Ingraham
AU - Abraham Flanigan
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - IUSE Computational Creativity: Improving Learning, Achievement, and Retention in Computer Science for CS and non-CS Undergraduates
UR - https://peer.asee.org/30740
DO - 10.18260/1-2--30740
ER -