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<p>The purpose of this design and development research study was to explore high school Advanced Placement (AP) Computer Science Principles (CSP) students’ perceived programming self-efficacy following participation in an authentic, tool-based programming intervention. The intervention utilized Integrated Development Environments (IDEs) and Application Programming Interfaces (APIs) associated with Google tools that students commonly use. Some AP CSP courses rely on programming platforms that, while effective for foundational learning, provide limited exposure to the kinds of programming environments and tools students may encounter in academic, professional, and personal contexts. To address this gap, an instructional unit titled Real-World Programming with Google Apps Script (RWP) was designed, refined, and evaluated to determine how authentic programming experiences can strengthen students’ computer programming self-efficacy. We explored how the intervention impacted high school students’ beliefs of programming self-efficacy. Students with higher self-efficacy are better equipped to approach challenges and persist through difficulties. Computer programming self-efficacy has been shown to positively predict students’ computer science (CS) academic performance and affect a students’ decision to remain in CS studies. Grounded in Self-Efficacy Theory and Constructionism, Merrill’s First Principles of Instruction was used to guide the development of the intervention to ensure that learners engaged in meaningful, problem-centered tasks. The design emphasized hands-on interaction with a cloud-based IDE and opportunities to create computational solutions using Google Workspace APIs. Prior to participating in the unit, students had completed AP CSP using code.org’s curriculum. The mixed-methods study collected pre/post self-efficacy surveys, student reflections, computational artifacts, and instructor journals. Results showed a statistically significant increase in students’ overall programming self-efficacy after completing the unit. Follow-up engagement surveys revealed that all participants felt more confident applying computational methods to real-world problems, and many cited demonstrations, Google Workspace integrations, and optional AI-assisted programming as key contributors to their increased confidence. Findings suggest that the intervention can support the development of programming self-efficacy. Implications and recommendations for future research are discussed.</p>

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Design and Evaluation of a Real-World Programming Unit in AP Computer Science Principles: Student Self-Efficacy and Perceptions

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Monika S. Richardson,
  • Albert D. Ritzhaupt

摘要

The purpose of this design and development research study was to explore high school Advanced Placement (AP) Computer Science Principles (CSP) students’ perceived programming self-efficacy following participation in an authentic, tool-based programming intervention. The intervention utilized Integrated Development Environments (IDEs) and Application Programming Interfaces (APIs) associated with Google tools that students commonly use. Some AP CSP courses rely on programming platforms that, while effective for foundational learning, provide limited exposure to the kinds of programming environments and tools students may encounter in academic, professional, and personal contexts. To address this gap, an instructional unit titled Real-World Programming with Google Apps Script (RWP) was designed, refined, and evaluated to determine how authentic programming experiences can strengthen students’ computer programming self-efficacy. We explored how the intervention impacted high school students’ beliefs of programming self-efficacy. Students with higher self-efficacy are better equipped to approach challenges and persist through difficulties. Computer programming self-efficacy has been shown to positively predict students’ computer science (CS) academic performance and affect a students’ decision to remain in CS studies. Grounded in Self-Efficacy Theory and Constructionism, Merrill’s First Principles of Instruction was used to guide the development of the intervention to ensure that learners engaged in meaningful, problem-centered tasks. The design emphasized hands-on interaction with a cloud-based IDE and opportunities to create computational solutions using Google Workspace APIs. Prior to participating in the unit, students had completed AP CSP using code.org’s curriculum. The mixed-methods study collected pre/post self-efficacy surveys, student reflections, computational artifacts, and instructor journals. Results showed a statistically significant increase in students’ overall programming self-efficacy after completing the unit. Follow-up engagement surveys revealed that all participants felt more confident applying computational methods to real-world problems, and many cited demonstrations, Google Workspace integrations, and optional AI-assisted programming as key contributors to their increased confidence. Findings suggest that the intervention can support the development of programming self-efficacy. Implications and recommendations for future research are discussed.