Work in Progress: A Multi-Modal Method for Assessing Student Emotions During Programming Tasks

S. Zahra Atiq

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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: System 1 in Engineering Education and Research
Tagged Division: Educational Research and Methods
Page Count: 5
DOI: 10.18260/1-2--31264
Permanent URL: https://peer.asee.org/31264
Download Count: 450

Paper Authors

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S. Zahra Atiq Purdue University, West Lafayette (College of Engineering)

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Zahra Atiq is a Ph.D. candidate at the School of Engineering Education at Purdue University. She is interested in learning about the non-cognitive/affective and individual/demographic factors that impact students in STEM courses. Specifically, she is interested in understanding the emotions students’ experience while learning computer programming. She is also interested in understanding women's participation in computer science and engineering.

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Abstract

Computer programming is considered a necessary skill for engineering students. As a consequence, programming courses are introduced to undergraduates early in their engineering education. However, learning programming is a difficult undertaking [1]. It is challenging because novice students may not have accurate mental models required to learn programming [1]. Hence, students in a programming course may experience a wide array of emotions that may positively or negatively impact their performance and learning [2]. Literature also suggests that students may be academically proficient, but may still encounter difficulties in a programming course [3]. These challenges associated with learning programming may lead students to drop out of engineering or switch to another engineering major [4]. The aim of this study is to investigate the emotions that first-year engineering students experience during programming tasks. In the spring of 2018, I plan on recruiting 15 to 20 research participants from engineering students who will be taking an introductory programming class. I will collect multi-modal data in a laboratory setting in two stages: 1) a programming task and 2) a retrospective think-aloud interview. In the programming task, student participants will attempt to solve four programming problems in 30 minutes. During this task, I will collect observational data (facial expressions), physiological data (Electrodermal Activity), self-report data (Achievement Emotions Questionnaire – AEQ), and a post-task interview. Three to five days after the programming task, I will conduct a retrospective think-aloud interview. In this interview, the participants will verbalize the thought processes they used and the emotions they experienced while working on the programming problems, the reasons for those emotions, and the self-regulation strategies they used. The retrospective interview will be audio-recorded, transcribed, and analyzed using a theoretical thematic analysis technique. This combination of methods is relatively new in engineering education research. A better understanding of student emotions may help educators design curriculum and pedagogy to mitigate the effects of negative emotions, and to promote positive emotions. This improved curriculum and pedagogy may eventually help students maximize their learning and performance in programming courses. Subsequently, student motivation and interest in programming may also be increased by using this improved and enhanced curriculum and pedagogy.

References

[1] A. Robins, J. Rountree, and N. Rountree, “Learning and teaching programming: A review and discussion,” Comput. Sci. Educ., vol. 13, pp. 137–172, 2003. [2] M. Zeidner, “Anxiety in education,” in International handbook of emotions in education, Routledge, 2014, pp. 265 – 288. [3] M. Meyer and S. Marx, “Engineering Dropouts: A Qualitative Examination of Why Undergraduates Leave Engineering,” J. Eng. Educ., vol. 103, no. 4, pp. 525–548, Oct. 2014. [4] S. Secules, A. Elby, and A. Gupta, “‘Turning away’ from the Struggling Individual Student: An Account of the Cultural Construction of Engineering Ability in an Undergraduate Programming Class,” presented at the 2016 ASEE Annual Conference & Exposition, 2016.

Citation
Format

Atiq, S. Z. (2018, June), Work in Progress: A Multi-Modal Method for Assessing Student Emotions During Programming Tasks Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31264

TY - CPAPER
AB - Computer programming is considered a necessary skill for engineering students. As a consequence, programming courses are introduced to undergraduates early in their engineering education. However, learning programming is a difficult undertaking [1]. It is challenging because novice students may not have accurate mental models required to learn programming [1]. Hence, students in a programming course may experience a wide array of emotions that may positively or negatively impact their performance and learning [2]. Literature also suggests that students may be academically proficient, but may still encounter difficulties in a programming course [3]. These challenges associated with learning programming may lead students to drop out of engineering or switch to another engineering major [4].
The aim of this study is to investigate the emotions that first-year engineering students experience during programming tasks. In the spring of 2018, I plan on recruiting 15 to 20 research participants from engineering students who will be taking an introductory programming class. I will collect multi-modal data in a laboratory setting in two stages: 1) a programming task and 2) a retrospective think-aloud interview. In the programming task, student participants will attempt to solve four programming problems in 30 minutes. During this task, I will collect observational data (facial expressions), physiological data (Electrodermal Activity), self-report data (Achievement Emotions Questionnaire – AEQ), and a post-task interview. Three to five days after the programming task, I will conduct a retrospective think-aloud interview. In this interview, the participants will verbalize the thought processes they used and the emotions they experienced while working on the programming problems, the reasons for those emotions, and the self-regulation strategies they used. The retrospective interview will be audio-recorded, transcribed, and analyzed using a theoretical thematic analysis technique. This combination of methods is relatively new in engineering education research.
A better understanding of student emotions may help educators design curriculum and pedagogy to mitigate the effects of negative emotions, and to promote positive emotions. This improved curriculum and pedagogy may eventually help students maximize their learning and performance in programming courses. Subsequently, student motivation and interest in programming may also be increased by using this improved and enhanced curriculum and pedagogy.

References

[1] A. Robins, J. Rountree, and N. Rountree, “Learning and teaching programming: A review and discussion,” Comput. Sci. Educ., vol. 13, pp. 137–172, 2003.
[2] M. Zeidner, “Anxiety in education,” in International handbook of emotions in education, Routledge, 2014, pp. 265 – 288.
[3] M. Meyer and S. Marx, “Engineering Dropouts: A Qualitative Examination of Why Undergraduates Leave Engineering,” J. Eng. Educ., vol. 103, no. 4, pp. 525–548, Oct. 2014.
[4] S. Secules, A. Elby, and A. Gupta, “‘Turning away’ from the Struggling Individual Student: An Account of the Cultural Construction of Engineering Ability in an Undergraduate Programming Class,” presented at the 2016 ASEE Annual Conference &amp; Exposition, 2016.

AU - S. Zahra Atiq
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Work in Progress: A Multi-Modal Method for Assessing Student Emotions During Programming Tasks
UR - https://peer.asee.org/31264
DO - 10.18260/1-2--31264
ER -