GeNeDis 2018 pp 425-435 | Cite as
Undergraduate Students’ Brain Activity in Visual and Textual Programming
- 1 Citations
- 701 Downloads
Abstract
Eight computer science students, novice programmers, who were in the first semester of their studies, participated in a field study in order to explore potential differences in their brain activity during programming with a visual versus a textual programming language. The students were asked to develop two specific programs in both programming languages (a total of four tasks). Measurements of cerebral activity were performed by the electroencephalography (EEG) imaging method. According to data analysis, it appears that the type of programming language did not affect the students’ brain activity.
Keywords
Computer programming education Textual programming Visual programming NeuroeducationNotes
Acknowledgments
This research was partially supported by Fulbright Foundation, Greece; the American College of Greece; the Institute of Educational Policy, Greece; BiHeLab, Ionian University, Greece; and Villanova University, USA.
References
- Ainsworth SE (2006) DeFT: a conceptual framework for learning with multiple representations. Learn Instr 16(3):183–198CrossRefGoogle Scholar
- Ansari D, Coch D, De Smedt B (2011) Connecting education and cognitive neuroscience: where will the journey take us? Educ Philos Theory 43(1):37–42CrossRefGoogle Scholar
- Barnes DJ, Fincher S, Thompson S (1997) Introductory problem solving in computer science. In: Daughton G, Magee P (eds) 5th annual conference on the teaching of computing. HE Academy for Information and Computer Sciences, Newtownabbey, pp 36–39Google Scholar
- Chao P (2016) Exploring students’ computational practice, design and performance of problem-solving through a visual programming environment. Comput Educ 95:202–215CrossRefGoogle Scholar
- Crk I, Kluthe T, Stefik A (2015) Understanding programming expertise: an empirical study of phasic brain wave changes. ACM Transactions on Computer-Human Interaction (TOCHI) 23(1):2Google Scholar
- Dresler M, Sandberg A, Ohla K, Bublitz C, Trenado C, Mroczko-Wasowicz A et al (2013) Non-pharmacological cognitive enhancement. Neuropharmacology 64:529–543CrossRefGoogle Scholar
- Erwig M, Smeltzer K, Wang, X (2016) What is a visual language? J Vis Lang Comput 38:9–17Google Scholar
- Floyd B, Santander T, Weimer W (2017) Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise. In: IEEE/ACM 39th international conference on software engineering, ICSE 2017, pp. 175–186Google Scholar
- Georgouli K, Sgouropoulou C (2013) Collaborative peer-evaluation learning results in higher education programming-based courses. In: ICBL2013 – International Conference on Interactive Computer aided Blended Learning, pp. 309–314Google Scholar
- Giraffa LMM, Moraes MC, Uden L (2014) Teaching object-oriented programming in first-year undergraduate courses supported by virtual classrooms. In: L. Uden (Ed.), The 2nd International Workshop on Learning Technology for Education in Cloud, pp. 15–26. Springer Proceedings in Complexity. https://doi.org/10.1007/978-94-007-7308-0_2
- Goldman SR (2003) Learning in complex domains: when and why do multiple representations help? Learn Instr 13(2):239–244CrossRefGoogle Scholar
- Henz D, John A, Merz C, Schöllhorn WI (2018) Post-task effects on EEG brain activity differ for various differential learning and contextual interference protocols. Front Hum Neurosci 12(January):1–10Google Scholar
- Larsen-Freeman D (1997) Chaos/complexity science and second language acquisition. Appl Linguist 18:141–165CrossRefGoogle Scholar
- Lui AK, Kwan R, Poon M, Cheung YH (2004) Saving weak programming students: applying constructivism in a first programming course. SIGCSE Bulletin 36(2):72–76CrossRefGoogle Scholar
- Mayer RE (2017) How can brain research inform academic learning and instruction? Educ Psychol Rev 29(4):835–846CrossRefGoogle Scholar
- McGettrick A, Boyle R, Ibbett R, Lloyd J, Lovegrove G, Mander K (2005) Grand challenges in computing: education—a summary. Comput J 48(1):42–48CrossRefGoogle Scholar
- Müller SC, Fritz T (2016) Using (bio)metrics to predict code quality online. Proceedings of the 38th international conference on software engineering – ICSE ‘16, (December), pp. 452–463Google Scholar
- Nouri A (2016) The basic principles of research in neuroeducation studies. International Journal of Cognitive Research in Science, Engineering and Education 4(1):59–66CrossRefGoogle Scholar
- Price TW, Barnes T (2015) Comparing Textual and Block Interfaces in a Novice Programming Environment. In: Proceedings of the eleventh annual International Conference on International Computing Education Research – ICER ‘15. ACM, New York, pp 91–99Google Scholar
- Radevski S, Hata H, Matsumoto K (2015) Real-time monitoring of neural state in assessing and improving software developers’ productivity. Proceedings – 8th international workshop on cooperative and human aspects of software engineering, CHASE 2015, (May), pp. 93–96Google Scholar
- Sharafi Tafreshi Moghaddam Z (2015) On the influence of representation type and gender on recognition tasks of program comprehension (Doctoral dissertation, École Polytechnique de Montréal)Google Scholar
- Siegmund J, Kästner C, Apel S, Parnin C, Bethmann A, Leich T et al (2014) Understanding source code with functional magnetic resonance imaging. Proceedings of the 36th ACM/IEEE international conference on software engineering, pp. 378–389Google Scholar
- Torresan P (2013) On educational neuroscience. An interview with Paul Howard-Jones. Formazione & Insegnamento XI(1):43–49Google Scholar
- Yusuf S, Kagdi H, Maletic JI, Ohio K (2007) Assessing the Comprehension of UML Class Diagrams via Eye Tracking. In: 15th IEEE international conference on program comprehension (ICPC’07), pp. 113–122Google Scholar