BOARD # 199: Comparing Computational Thinking Learning and Engagement in First-Grade Boys and Girls: A Study of Algorithm Design and Debugging (Work-In-Progress)

Bárbara Fagundes; Tamara J Moore

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: WIP Poster Session: Emerging Research and Practices in Pre-College Engineering Education
Tagged Division: Pre-College Engineering Education Division (PCEE)
Tagged Topic: Diversity
Page Count: 10
DOI: 10.18260/1-2--55553
Permanent URL: https://peer.asee.org/55553
Download Count: 13

Paper Authors

biography

Bárbara Fagundes Purdue University orcid.org/0000-0001-8138-1471

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I hold a Ph.D. in Engineering Education and an M.S. in Computer Science, focusing on integrating computational thinking into pre-college education. My experience includes developing and implementing engineering and computer science curricula and actively participating in professional development for teachers to establish inclusive and innovative learning environments. At Purdue University’s Center for Instructional Excellence (CIE), I work as a postdoctoral researcher, collaborating on faculty development, mentoring undergraduate students, and supporting curriculum initiatives.

My passion lies in promoting STEM education, advocating for increased participation in STEM fields. Alongside my primary research, I am interested in human-computer interaction, AI in education, educational robotics, and user experience (UX) design, focusing on how technology can improve teaching and learning for all learners.

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Tamara J Moore Purdue University at West Lafayette (PWL) (COE) orcid.org/0000-0002-7956-4479

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Tamara J. Moore, Ph.D., is a Professor of Engineering Education and University Faculty Scholar at Purdue University, as well as the Executive Co-Director of the INSPIRE Research Institute for Precollege Engineering. Dr. Moore's research is focused on the integration of STEM concepts in K-12 and postsecondary classrooms in order to help students make connections among the STEM disciplines and achieve deep understanding. Her work investigates engineering design-based STEM integration, computational thinking, and integration of high-level content in K-14 spaces. She is creating and testing innovative, interdisciplinary curricular approaches that engage students in developing models of real-world problems and their solutions.

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Abstract

With the fast pace of technological developments and the growing need for qualified professionals in Science, Technology, Engineering, and Mathematics (STEM), promoting Computer Science (CS) education in primary school has become imperative. However, the persistent gender gap in STEM and the still limited number of individuals entering these fields, despite the high industry demand, remain significant challenges. Consequently, Computational Thinking (CT) has become a critical component that young students must develop. However, there is still little understanding of how children develop CT skills and engage with CT-related tasks. Hence, this study responds to these 21st-century needs and aims to offer valuable insights into how first-grade students demonstrate their understanding and application of CT principles. Through programming lessons accommodating their unique developmental stage, the research focuses on their ability to develop and debug algorithms in coding challenges using plugged activities involving Educational Robots (ER) and a block-based programming language platform. It also examines engagement patterns across cognitive, behavioral, emotional, and social dimensions, investigating potential biological sex differences while considering the broader implications for the gender gap. The study utilized a naturalistic inquiry approach, incorporating qualitative analysis of students’ work, classroom observations, and video data to gain meaningful insights into students’ conceptual understanding of CT principles and whether differences in engagement are influenced by biological sex or other factors inherent in the learning process, such as individual learning styles. The findings show that while there were slight differences in how students engaged with and approached CT tasks, they were more related to learning styles, individual traits, and how they respond to interpersonal interactions with peers rather than biological sex. Both boys and girls were highly cognitively engaged — e.g., problem-solving and algorithmic thinking — with social dynamics significantly influencing collaboration and overall engagement. Emotional responses, such as excitement and frustration, were present in all groups. Additionally, boys and girls exhibited similar behavioral engagement through active participation, persistence in debugging, and eagerness to solve coding tasks. The findings highlight that even at an early age, students can grasp and apply foundational CT concepts present in algorithm development and debugging. However, curricula should be strategically designed considering students’ developmental stages. The study suggests the young age of the students might explain the lack of significant sex-based differences. At this age, students are less influenced by social gender norms, which could explain the minimal differences in engagement patterns. While this study focuses on biological sex differences, it is essential to consider how societal gender norms might influence these engagement patterns as students age and begin to internalize such norms. The early exposure to computational thinking is an opportunity to foster environments that challenge traditional gender stereotypes before they become established.

Citation
Format

Fagundes, B., & Moore, T. J. (2025, June), BOARD # 199: Comparing Computational Thinking Learning and Engagement in First-Grade Boys and Girls: A Study of Algorithm Design and Debugging (Work-In-Progress) Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--55553

TY  - CPAPER
AB  - With the fast pace of technological developments and the growing need for qualified professionals in Science, Technology, Engineering, and Mathematics (STEM), promoting Computer Science (CS) education in primary school has become imperative. However, the persistent gender gap in STEM and the still limited number of individuals entering these fields, despite the high industry demand, remain significant challenges. Consequently, Computational Thinking (CT) has become a critical component that young students must develop. However, there is still little understanding of how children develop CT skills and engage with CT-related tasks. 
Hence, this study responds to these 21st-century needs and aims to offer valuable insights into how first-grade students demonstrate their understanding and application of CT principles. Through programming lessons accommodating their unique developmental stage, the research focuses on their ability to develop and debug algorithms in coding challenges using plugged activities involving Educational Robots (ER) and a block-based programming language platform. It also examines engagement patterns across cognitive, behavioral, emotional, and social dimensions, investigating potential biological sex differences while considering the broader implications for the gender gap. 
The study utilized a naturalistic inquiry approach, incorporating qualitative analysis of students’ work, classroom observations, and video data to gain meaningful insights into students’ conceptual understanding of CT principles and whether differences in engagement are influenced by biological sex or other factors inherent in the learning process, such as individual learning styles. The findings show that while there were slight differences in how students engaged with and approached CT tasks, they were more related to learning styles, individual traits, and how they respond to interpersonal interactions with peers rather than biological sex. Both boys and girls were highly cognitively engaged — e.g., problem-solving and algorithmic thinking — with social dynamics significantly influencing collaboration and overall engagement. Emotional responses, such as excitement and frustration, were present in all groups. Additionally, boys and girls exhibited similar behavioral engagement through active participation, persistence in debugging, and eagerness to solve coding tasks. 
The findings highlight that even at an early age, students can grasp and apply foundational CT concepts present in algorithm development and debugging. However, curricula should be strategically designed considering students’ developmental stages. The study suggests the young age of the students might explain the lack of significant sex-based differences. At this age, students are less influenced by social gender norms, which could explain the minimal differences in engagement patterns. While this study focuses on biological sex differences, it is essential to consider how societal gender norms might influence these engagement patterns as students age and begin to internalize such norms. The early exposure to computational thinking is an opportunity to foster environments that challenge traditional gender stereotypes before they become established. 
AU  - Bárbara Fagundes
AU  - Tamara J Moore
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD # 199: Comparing Computational Thinking Learning and Engagement in First-Grade Boys and Girls: A Study of Algorithm Design and Debugging (Work-In-Progress)
UR  - https://peer.asee.org/55553
DO  - 10.18260/1-2--55553
ER  -