Research Article
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Year 2025, Volume: 7 Issue: 1, 5 - 29, 10.07.2025

Alp Tuğan , Ayşe Hazar Köksal

https://doi.org/10.46474/jds.1613349

Abstract

References

  • Allwood, C. M. (1986). Novices on the computer: A review of the literature. International Journal of Man-Machine Studies, 25(6), 633–658. https://doi.org/10.1016/S0020-7373(86)80079-7 Bailey, J. (2018). Why Love Generative Art?. notre traduction, juillet.
  • Boden, M. A., & Edmonds, E. A. (2009). What is generative art? Digital Creativity, 20(1–2), 21–46. https://doi.org/10.1080/14626260902867915
  • Brown, N. C. C., & Wilson, G. (2018). Ten quick tips for teaching programming. PLoS Computational Biology, 14(4), e1006023. https://doi.org/10.1371/journal.pcbi.1006023
  • Bryant, R., Weiss, R., Orr, G., & Yerion, K. (2011). Using the context of algorithmic art to change attitudes in introductory programming. Journal of Computing Sciences in Colleges, 27(1), 112–119. http://dl.acm.org/citation.cfm?id=2037177
  • Berkeley, E. (1963). Computer Art Contest. Computers and Automation, XII (1), p. 21.
  • Dorin, A., McCabe, J., McCormack, J., Monro, G., & Whitelaw, M. (2012). A framework for understanding generative art. Digital Creativity, 23(3–4), 239–259. https://doi.org/10.1080/14626268.2012.709940
  • Farah, J. C., Moro, A., Bergram, K., Purohit, A. K., Gillet, D., & Holzer, A. (2020). Bringing computational thinking to non-STEM undergraduates through an integrated notebook application. European Conference on Technology Enhanced Learning, 2676. http://ceur-ws.org/Vol-2676/paper2.pdf
  • Galanter, P. (2016). Generative Art Theory. In A Companion to Digital Art, C. Paul (Ed.). https://doi.org/10.1002/9781118475249.ch5
  • Guo, P. J. (2017). Older Adults Learning Computer Programming: Motivations, Frustrations, and Design Opportunities. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 7070–7083. https://doi.org/10.1145/3025453.3025945
  • Guzdial, M. (2006). Teaching computing for everyone. Journal of Computing Sciences in Colleges, 21(4), 6. http://dl.acm.org/ft_gateway.cfm?id=1127390&type=pdf
  • Guzdial, M. (2010). Does contextualized computing education help? ACM Inroads, 1(4), 4–6. https://doi.org/10.1145/1869746.1869747
  • Hansen, S. M. (2019). Mapping Creative Coding Courses: Toward Bespoke Programming Curricula in Graphic Design Education. Eurographics 2019 - Education Papers, 4 pages. https://doi.org/10.2312/EGED.20191024
  • Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83–137. https://doi.org/10.1145/1089733.1089734
  • Liao, L., & Pope, J. W. (2008). Computer literacy for everyone. Journal of Computing Sciences in Colleges, 23(6), 231–238. https://doi.org/10.5555/1352383.1352423
  • Lohiniva, M., & Isomöttönen, V. (2021). Novice Programming Students’ Reflections on Study Motivation during COVID-19 Pandemic. 2021 IEEE Frontiers in Education Conference (FIE), 1-9.
  • Luxton-Reilly, A. (2016). Learning to Program is Easy. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, 284–289. https://doi.org/10.1145/2899415.2899432
  • Macdonald, N. (1981). Computers and People—Vol XXX - 7-8. 30(7–8).
  • McCarthy, L., Reas, C., & Fry, B. (2015). Getting Started with p5.js: Making Interactive Graphics in JavaScript and Processing. Maker Media, Inc.
  • Medeiros, R. P., Ramalho, G. L., & Falcao, T. P. (2019). A Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education. IEEE Transactions on Education, 62(2), 77–90. https://doi.org/10.1109/TE.2018.2864133
  • Mollu, M. (2020). Computational thinking as a problem-solving framework for the 21st century. International Journal of Educational Technology and Society, 23(2), 305–315.
  • Noble, J. J. (2009). Programming interactivity: A designer’s guide to processing, Arduino, and openFrameworks (1st ed). O’Reilly.
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
  • Pearson, M. (2011). Generative art: A practical guide using processing. Manning; Pearson Education.
  • Perevalov, D., & Tatarnikov, I. (2013). Mastering openFrameworks: Creative coding demystified: a practical guide to creating audiovisual interactive projects with low-level data processing using openFrameworks. Packt Publishing.
  • Perevalov, D., & Tatarnikov, I. (2015). openFrameworks Essentials: Create stunning, interactive openFrameworks-based applications with this fast-paced guide. Packt Publishing.
  • Phon-Amnuaisuk, S., & Panjapornpon, J. (2012). Controlling Generative Processes of Generative Art Somnuk Phon-. Procedia Computer Science, 13, 43–52. https://doi.org/10.1016/j.procs.2012.09.112
  • Polanyi, M., & Sen, A. (2009). The Tacit Dimension. University of Chicago Press.
  • Reas, C., & Fry, B. (2007). Processing: A programming handbook for visual designers and artists. MIT Press.
  • Ring, B. A., Giordan, J., & Ransbottom, J. S. (2008). Problem Solving Through Programming: Motivating the Non-Programmer. Consortium for Computing Sciences in Colleges, 23(3), 7.
  • Robins, A., Rountree, J., & Rountree, N. (2003). Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13(2), 137–172. https://doi.org/10.1076/csed.13.2.137.14200
  • Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1), 42. https://doi.org/10.1186/s41239-017-0080-z
  • Shein, E. (2014). Should Everybody Learn to Code? Association for Computing Machinery, 57(2), 16–18. https://doi.org/10.1145/2557447
  • Shiffman, D. (2008). Learning Processing: A beginner’s guide to programming images, animation, and interaction. Morgan Kaufmann/Elsevier.
  • Shiffman, D. (2012). The Nature of Code. Nature of Code.
  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118
  • Winslow, L. E. (1996). Programming pedagogy—a psychological overview. ACM SIGCSE Bulletin, 28(3), 17–22. https://doi.org/10.1145/234867.234872
  • Terzidis, K. (2009). Algorithms for visual design using the processing language. Wiley Pub.
  • Torrence, B. (2021). Tessellations: Mathematics, Art, and Recreation. American Mathematical Monthly, 128(10), 955–959. https://doi.org/10.1080/00029890.2021.1971917
  • Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60(4), 55–62. https://doi.org/10.1145/2994591
  • Yardi, S., & Bruckman, A. (2007). What is computing? Bridging the gap between teenagers’ perceptions and graduate students’ experiences. Proceedings of the Third International Workshop on Computing Education Research - ICER ’07, 39. https://doi.org/10.1145/1288580.1288586

Algorithmic Art Praxis: A Framework for Contextualized Programming Education

Year 2025, Volume: 7 Issue: 1, 5 - 29, 10.07.2025

Alp Tuğan , Ayşe Hazar Köksal

https://doi.org/10.46474/jds.1613349

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.

Keywords

Computational Thinking Algorithmic Art Programming Contextualized Education Problem-solving Visual Learning

References

  • Allwood, C. M. (1986). Novices on the computer: A review of the literature. International Journal of Man-Machine Studies, 25(6), 633–658. https://doi.org/10.1016/S0020-7373(86)80079-7 Bailey, J. (2018). Why Love Generative Art?. notre traduction, juillet.
  • Boden, M. A., & Edmonds, E. A. (2009). What is generative art? Digital Creativity, 20(1–2), 21–46. https://doi.org/10.1080/14626260902867915
  • Brown, N. C. C., & Wilson, G. (2018). Ten quick tips for teaching programming. PLoS Computational Biology, 14(4), e1006023. https://doi.org/10.1371/journal.pcbi.1006023
  • Bryant, R., Weiss, R., Orr, G., & Yerion, K. (2011). Using the context of algorithmic art to change attitudes in introductory programming. Journal of Computing Sciences in Colleges, 27(1), 112–119. http://dl.acm.org/citation.cfm?id=2037177
  • Berkeley, E. (1963). Computer Art Contest. Computers and Automation, XII (1), p. 21.
  • Dorin, A., McCabe, J., McCormack, J., Monro, G., & Whitelaw, M. (2012). A framework for understanding generative art. Digital Creativity, 23(3–4), 239–259. https://doi.org/10.1080/14626268.2012.709940
  • Farah, J. C., Moro, A., Bergram, K., Purohit, A. K., Gillet, D., & Holzer, A. (2020). Bringing computational thinking to non-STEM undergraduates through an integrated notebook application. European Conference on Technology Enhanced Learning, 2676. http://ceur-ws.org/Vol-2676/paper2.pdf
  • Galanter, P. (2016). Generative Art Theory. In A Companion to Digital Art, C. Paul (Ed.). https://doi.org/10.1002/9781118475249.ch5
  • Guo, P. J. (2017). Older Adults Learning Computer Programming: Motivations, Frustrations, and Design Opportunities. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 7070–7083. https://doi.org/10.1145/3025453.3025945
  • Guzdial, M. (2006). Teaching computing for everyone. Journal of Computing Sciences in Colleges, 21(4), 6. http://dl.acm.org/ft_gateway.cfm?id=1127390&type=pdf
  • Guzdial, M. (2010). Does contextualized computing education help? ACM Inroads, 1(4), 4–6. https://doi.org/10.1145/1869746.1869747
  • Hansen, S. M. (2019). Mapping Creative Coding Courses: Toward Bespoke Programming Curricula in Graphic Design Education. Eurographics 2019 - Education Papers, 4 pages. https://doi.org/10.2312/EGED.20191024
  • Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83–137. https://doi.org/10.1145/1089733.1089734
  • Liao, L., & Pope, J. W. (2008). Computer literacy for everyone. Journal of Computing Sciences in Colleges, 23(6), 231–238. https://doi.org/10.5555/1352383.1352423
  • Lohiniva, M., & Isomöttönen, V. (2021). Novice Programming Students’ Reflections on Study Motivation during COVID-19 Pandemic. 2021 IEEE Frontiers in Education Conference (FIE), 1-9.
  • Luxton-Reilly, A. (2016). Learning to Program is Easy. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, 284–289. https://doi.org/10.1145/2899415.2899432
  • Macdonald, N. (1981). Computers and People—Vol XXX - 7-8. 30(7–8).
  • McCarthy, L., Reas, C., & Fry, B. (2015). Getting Started with p5.js: Making Interactive Graphics in JavaScript and Processing. Maker Media, Inc.
  • Medeiros, R. P., Ramalho, G. L., & Falcao, T. P. (2019). A Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education. IEEE Transactions on Education, 62(2), 77–90. https://doi.org/10.1109/TE.2018.2864133
  • Mollu, M. (2020). Computational thinking as a problem-solving framework for the 21st century. International Journal of Educational Technology and Society, 23(2), 305–315.
  • Noble, J. J. (2009). Programming interactivity: A designer’s guide to processing, Arduino, and openFrameworks (1st ed). O’Reilly.
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
  • Pearson, M. (2011). Generative art: A practical guide using processing. Manning; Pearson Education.
  • Perevalov, D., & Tatarnikov, I. (2013). Mastering openFrameworks: Creative coding demystified: a practical guide to creating audiovisual interactive projects with low-level data processing using openFrameworks. Packt Publishing.
  • Perevalov, D., & Tatarnikov, I. (2015). openFrameworks Essentials: Create stunning, interactive openFrameworks-based applications with this fast-paced guide. Packt Publishing.
  • Phon-Amnuaisuk, S., & Panjapornpon, J. (2012). Controlling Generative Processes of Generative Art Somnuk Phon-. Procedia Computer Science, 13, 43–52. https://doi.org/10.1016/j.procs.2012.09.112
  • Polanyi, M., & Sen, A. (2009). The Tacit Dimension. University of Chicago Press.
  • Reas, C., & Fry, B. (2007). Processing: A programming handbook for visual designers and artists. MIT Press.
  • Ring, B. A., Giordan, J., & Ransbottom, J. S. (2008). Problem Solving Through Programming: Motivating the Non-Programmer. Consortium for Computing Sciences in Colleges, 23(3), 7.
  • Robins, A., Rountree, J., & Rountree, N. (2003). Learning and Teaching Programming: A Review and Discussion. Computer Science Education, 13(2), 137–172. https://doi.org/10.1076/csed.13.2.137.14200
  • Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1), 42. https://doi.org/10.1186/s41239-017-0080-z
  • Shein, E. (2014). Should Everybody Learn to Code? Association for Computing Machinery, 57(2), 16–18. https://doi.org/10.1145/2557447
  • Shiffman, D. (2008). Learning Processing: A beginner’s guide to programming images, animation, and interaction. Morgan Kaufmann/Elsevier.
  • Shiffman, D. (2012). The Nature of Code. Nature of Code.
  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118
  • Winslow, L. E. (1996). Programming pedagogy—a psychological overview. ACM SIGCSE Bulletin, 28(3), 17–22. https://doi.org/10.1145/234867.234872
  • Terzidis, K. (2009). Algorithms for visual design using the processing language. Wiley Pub.
  • Torrence, B. (2021). Tessellations: Mathematics, Art, and Recreation. American Mathematical Monthly, 128(10), 955–959. https://doi.org/10.1080/00029890.2021.1971917
  • Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60(4), 55–62. https://doi.org/10.1145/2994591
  • Yardi, S., & Bruckman, A. (2007). What is computing? Bridging the gap between teenagers’ perceptions and graduate students’ experiences. Proceedings of the Third International Workshop on Computing Education Research - ICER ’07, 39. https://doi.org/10.1145/1288580.1288586
There are 40 citations in total.

Details

Primary Language English
Subjects Multimedia Design, Visual Communication Design (Other), Information Technology in Design, Design (Other)
Journal Section Research Articles
Authors

Alp Tuğan 0000-0003-3673-8675

Ayşe Hazar Köksal 0000-0001-6491-589X

Early Pub Date July 10, 2025
Publication Date July 10, 2025
Submission Date January 4, 2025
Acceptance Date January 22, 2025
Published in Issue Year 2025 Volume: 7 Issue: 1

Cite

APA Tuğan, A., & Köksal, A. H. (2025). Algorithmic Art Praxis: A Framework for Contextualized Programming Education. Journal of Design Studio, 7(1), 5-29. https://doi.org/10.46474/jds.1613349
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