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Journals / Journal of Design Studio / 2026 / Volume: 7 Issue: 1

Algorithmic Art Praxis: A Framework for Contextualized Programming Education

Alp Tuğan(Özyeğin University, Faculty of Architecture and Design)0000-0003-3673-8675
Ayşe Hazar Köksal(Özyeğin University, Faculty of Architecture and Design)0000-0001-6491-589X
Pages
—
Publication Date
2025-07-10
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This framework underscores the potential of contextualized learning to transform abstract programming concepts into accessible, meaningful educational experiences.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The amalgamation of computational thinking (CT) with contextualized instruction provides a sturdy framework for enriching programming education, especially for novice learners in design-centric higher education programs, where visual and experiential learning modalities prevail.
📊
Key Findings & Data:This research utilizes a rigorously curated online database of algorithmic artworks as a primary source for content analysis and pedagogical investigation.
💡
Conclusion & Impact:This framework underscores the potential of contextualized learning to transform abstract programming concepts into accessible, meaningful educational experiences.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Computational Thinking · Algorithmic Art · Programming

Abstract

The amalgamation of computational thinking (CT) with contextualized instruction provides a sturdy framework for enriching programming education, especially for novice learners in design-centric higher education programs, where visual and experiential learning modalities prevail. CT, recognized as a critical methodology for solving complex programming challenges, underpins the development of the Algorithmic Art Praxis (ALAP) Categories—a structured toolkit designed to bridge abstract computational concepts with tangible, art-based applications. This research utilizes a rigorously curated online database of algorithmic artworks as a primary source for content analysis and pedagogical investigation. Over 2,000 algorithmic artworks from secondary sources were subjected to a rigorous, iterative review process, narrowing the collection to 695 deeply analyzed samples that inform the database’s foundational content. Through this analytical perspective, 18 distinct ALAP Categories were identified, each mirroring fundamental programming principles as exemplified in algorithmic art. These categories establish a structured taxonomy that harmonizes computational thinking activities with contextualized programming education, thereby providing a customized approach to addressing the distinct cognitive and creative requirements of design students. The ALAP toolkit, consisting of the 18 categories, a succinct reference guide, and the curated database, serves as a versatile resource for educators, researchers, and students. Through the integration of computational thinking with algorithmic art, it facilitates the cultivation of programming proficiency in visually oriented learners while promoting engagement through relevance and creativity. This framework underscores the potential of contextualized learning to transform abstract programming concepts into accessible, meaningful educational experiences.

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Cite This Article

Tuğan, A., Köksal, A. H. (2025). Algorithmic Art Praxis: A Framework for Contextualized Programming Education. Journal of Design Studio, Volume: 7 Issue: 1. https://doi.org/10.46474/jds.1613349

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