Minneapolis, MN
August 23, 2022
June 26, 2022
June 29, 2022
10
10.18260/1-2--41125
https://peer.asee.org/41125
361
I am an Assistant Professor of Practice at the Ohio State University
Code comprehension is an important skill for programmers because it helps them understand code and develop debugging skills [1]. The process of code comprehension is unlike comprehending natural languages because it involves complex cognitive processing. During cognitive processing, a programmer is required to develop or use the appropriate mental models of programming constructs, which makes code comprehension difficult for novice programmers [2]. Along with cognitive processing, it is important to analyze how students feel during code comprehension because the literature suggests that emotions influence different aspects of cognition such as attention, reasoning, learning, memory, and problem-solving [3]. Novice programmers may experience a variety of emotions while comprehending code. These changes in emotions may subsequently influence their academic performance and retention in computing and engineering [4]. Therefore, in this study, we aim to understand CS1 students’ emotions and cognitive processing during code comprehension. Specifically, we ask the following research questions:
1. What type of cognitive processing do CS1 students perform during code comprehension? 2. What emotions do CS1 students experience during code comprehension? 3. How do programmers’ emotions and cognitive processing interact during code comprehension?
Answers to these research questions would provide us with an in-depth and nuanced understanding of the cognitive events that trigger certain emotions and how students process that information, and vice versa.
In this study, we will employ multi-modal data, collected through biometric sensors and concurrent think-aloud interviews. These data would provide multiple perspectives and a rich understanding of the instructional needs of CS1 students by analyzing their emotions and cognitive processing during code comprehension. These instructional needs may include demonstrating programming concepts with examples, construction of mental models through visualization, debugging strategies, and scaffolding [5]. Based on the instructional needs, appropriate instructional strategies (pedagogical, technological, or content-based) could be designed that may provide students with good learner support [5].
Batra, R., & Atiq, S. (2022, August), Work in Progress: Understanding CS1 Students’ Code Comprehension Behaviors using Multi-modal Data Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41125
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