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The Effects of coding integration on Student Engagement and Academic Achievement in a 5th Grade Mathematics Class

The Effects of Coding Integration on Student Engagement and ...

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作者： Sinead Meehan James Madison University

学位级别：硕士

This action research project investigated the effect coding integration had on student engagement and academic achievement in a fifth-grade mathematics class. Research was conducted on a group of 20 fifth grade students performing on grade level, in a suburban school outside of Philadelphia. Four data collection tools were used: A student survey, teacher observations and reflections, pre and post-test data, and a tally chart. Data was categorized into two domains: student engagement and academic achievement. coding follow-up works using Scratch, Wonder Workshop, and Turtle Academy were provided to students over the course of two mathematical topics created by Pearson Education, Inc., in addition to traditional follow-up works such as worksheets and task cards. Qualitative and quantitative data implied that coding integration had a positive effect on student engagement and overall, students’ perceptions of math class improved. Quantitative data was unable to determine the effect coding had on academic achievement due to consistent participation in the coding activities offered by all students. The findings suggest that coding integration can be used in fifth grade mathematics classes to cover a range of academic content while increasing student engagement and exposing students to 21st century skills.

关键词： coding integration engagement Scratch Wonder Workshop Turtle Academy

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Enhancing students' performance in computer science through tailored instruction based on their programming background 15

Enhancing students' performance in computer science through ...

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15th IEEE Global Engineering Education Conference (IEEE EDUCON)

作者： Ahmaderaghi, Baharak Barlaskar, Esha Pishchukhina, Olga Cutting, David Stewart, Darryl Queeens Univ Belfast Sch Elect Elect Engn & Comp Sci EEECS Belfast Antrim North Ireland

ISBN: (纸本)9798350394023;9798350394030

Computer science including data analytics is a widely popular field, boasting promising career opportunities in the future. Proficiency in programming stands as a fundamental requirement for success in this domain. However, students entering MSc programs in data analytics often possess varying levels of programming background, which can impact their performance in assignments. Recognising and addressing these differences through tailored instruction can improve students' outcomes. This paper explores the importance of considering students' programming backgrounds in the data analytics field and highlights strategies to enhance their performance based on prior knowledge. This study was carried out on two different modules in two different pathways. We have chosen two distinct cohorts and pathways to ensure unbiased conclusions in our study. The initial research was applied to the Database and Programming Fundamentals module for an MSc data analytics cohort, and then we utilized a Deep Learning module for final year computer science undergraduates as a validation cohort. As a conclusion, this study successfully demonstrated a significant increase in student assignment performance through the implementation of tailored instruction based on students' programming backgrounds. Despite receiving positive student feedback and observing excellent and improved performances, it is crucial to acknowledge instances of unsatisfactory student performance as well. Both studies were conducted by the School of Electronics, Electrical Engineering, and Computer Science (EEECS) at Queen's University Belfast (QUB) during the academic year 2021/2022.

关键词： Student performance Programming data science coding integration prerequisite skill

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