<p>This paper introduces a Multi-Aspect Visualization Framework, a language-agnostic interactive platform designed to enhance programming education by addressing the unique challenges and opportunities inherent in the field. Unlike traditional science laboratories, which rely on hardware-based experiments, this platform uses a versatile visualization framework combined with a user-driven control mechanism, allowing in-depth exploration of program execution across various levels of semantics and logic. Key features include a user navigation control interface, a virtual execution engine, and a visual action translator, enabling students and educators to explore programming concepts through visual depictions, such as machine instructions, finite state machines, and control flow levels. These tools offer deep insights into algorithm dynamics and the significance of key variables, with support for visualizations such as trees and pseudocode. The framework's adaptability to multiple programming languages, coupled with its range of visualizations, highlights its potential to significantly improve student understanding and engagement in programming education.</p>

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A Multi-Aspect Visualization Framework for Advancing STEM Pedagogy and SDG4: Digital India's Computer Science OLabs

  • J. Swaminathan,
  • K. R. Kavitha,
  • B. Sibin,
  • Raghu Raman,
  • Prema Nedungadi

摘要

This paper introduces a Multi-Aspect Visualization Framework, a language-agnostic interactive platform designed to enhance programming education by addressing the unique challenges and opportunities inherent in the field. Unlike traditional science laboratories, which rely on hardware-based experiments, this platform uses a versatile visualization framework combined with a user-driven control mechanism, allowing in-depth exploration of program execution across various levels of semantics and logic. Key features include a user navigation control interface, a virtual execution engine, and a visual action translator, enabling students and educators to explore programming concepts through visual depictions, such as machine instructions, finite state machines, and control flow levels. These tools offer deep insights into algorithm dynamics and the significance of key variables, with support for visualizations such as trees and pseudocode. The framework's adaptability to multiple programming languages, coupled with its range of visualizations, highlights its potential to significantly improve student understanding and engagement in programming education.