Abstract
Over the past 5 years, a significant number of studies focused on computer programming and code writing (software development, code comprehension, program debugging, code optimization, developer training), using the capabilities of brain imaging techniques and of biomarkers. With the use of the aforementioned techniques, researchers have explored the role of programming experience and knowledge, the relation between coding and writing, and the possibilities of improving program debugging with machine learning techniques. In this paper, a review of existing literature and discussion of research issues that should be examined in the future are explored. Research may link the neuroscientific field with training issues in programming, so as to contribute to the learning process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ansari D, Lyons IM (2016) Cognitive neuroscience and mathematics learning: how far have we come? Where do we need to go? ZDM 48(3):379–383
Armoni M, Gal-Ezer J (2014) High school computer science education paves the way for higher education: the Israeli case. Comp Sc Edu 24(2–3):101–122
Berland M, Martin T, Benton T, Petrick Smith C, Davis D (2013) Using learning analytics to understand the learning pathways of novice programmers. J Learn Sci 22(4):564–599
Biró P, Csenoch M, Abari K, Máth J (2016) First year students’ algorithmic skills in tertiary computer science education. Adv Intell Syst Comput 416:351–358
Cedefop (2016) Annual report 2015. Publications Office, Luxembourg. Cedefop Information Series
Crk I, Kluthe T, Stefik A (2015) Understanding programming expertise: an empirical study of phasic brain wave changes. ACM Trans Comput-Hum Interact (TOCHI) 23(1):2
Doukakis S, Papalaskari MA, Vlamos P, Plerou A, Giannopoulou P (2018) Assessing attention in visual and textual programming using neuroeducation approaches. In: 23rd annual ACM conference on innovation and technology in computer science education (ITiCSE’18). ACM, New York, p 392
Duraes J, Madeira H, Castelhano J, Duarte C, Branco MC (2016) WAP: understanding the brain at software debugging. In: Proceedings – international symposium on software reliability engineering, ISSRE, pp 87–92
Ferrari M, McBride H (2011) Mind, brain and education: the birth of a new science. Learn Landscapes 5(1):85–100
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 175–186
Jones SP, Bell T, Cutts Q, Iyer S, Schulte C, Vahrenhold J, Han B (2011) Computing at school. International comparisons. https://www.computingatschool.org.uk/
Lee S, Hooshyar D, Ji H, Nam K, Lim H (2017) Mining biometric data to predict programmer expertise and task difficulty. Clust Comput 21:1–11
Müller SC, Fritz T (2016) Using (bio)metrics to predict code quality online. In: Proceedings of the 38th international conference on software engineering – ICSE ‘16, December 2016, pp 452–463
Nakagawa T, Kamei Y, Uwano H, Monden A, Matsumoto K, German DM (2014) Quantifying programmers’ mental workload during program comprehension based on cerebral blood flow measurement: a controlled experiment. In: Companion proceedings of the 36th international conference on software engineering 2014, pp 448–451
Nouri A (2016) The basic principles of research in neuroeducation studies. Int J Cogn Res Sci Eng Educ 4(1):59–66
Pea RD, Kurland DM (1984) On the cognitive effects of learning computer programming. New Ideas Psychol 2(2):137–168
Pears A, Seidman S, Malmi L, Mannila L, Adams E, Bennedsen J, Devlin M, Paterson J (2007) A survey of literature on the teaching of introductory programming. SIGCSE Bull 39(4):204–223
Sajaniemi J (2008) Psychology of programming: looking into programmers’ heads. Prob Profess 4(May):4–8
Siegmund J, Kästner C, Apel S, Parnin C, Bethmann A, Leich T, Saake G, Brechmann A (2014) Understanding source code with functional magnetic resonance imaging. In: Proceedings of the 36th ACM/IEEE international conference on software engineering, pp 378–389
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Giannopoulou, P., Papalaskari, MA., Doukakis, S. (2020). Neuroeducation and Computer Programming: A Review. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-32622-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32621-0
Online ISBN: 978-3-030-32622-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)