Scott R. Portnoff
Abstract
An fMRI (functional Magnetic Resonance Imaging) study published in 2014 established that comprehension of computer programs occurs in the same regions of the brain that process natural languages---not logic, not math. The unexpectedness of this result was primed in part by the widespread belief that the language aspects of learning how to program are trivial when compared to learning to use programming languages for engineering tasks. In fact, though, the fMRI data is compelling cognitive evidence for the argument that the reason students have been failing introductory programming courses in large numbers---for decades---is because CS educators have underestimated the importance of teaching programming languages as languages per se. Despite the availability of this non-invasive technology for well over two decades, educators have neither researched the cognitive complexities of how programming languages might be acquired, nor tried to seriously understand this process in any degree of depth. Consequently, they have failed to consider what this evidence now implies: (a) that programming languages, despite being artificial languages, are alive in the brains of programmers in much the same way as any natural language that those programmers speak; and (b) that this new information about the cognitive aspects of programming languages has profound pedagogic implications.
- Amirtha, T. This Is Your Brain On Code, According To Functional MRI Imaging. Fast Company, April 21, 2014; http://www.fastcompany.com/3029364/this-is-your-brain-on-code-according-to-functional-mri-imaging. Accessed 2017 November 22.Google Scholar
- Bailey, N., Madden, C. & Krashen, S.D. Is there a `natural `sequence' in adult second language learning? Language Learning, 24, 2 (1974), 235--243.Google ScholarCross Ref
- Bennedsen, J. & Caspersen, M. E. Failure Rates in Introductory Programming. Inroads - The SIGCSE Bulletin, 39, 2 (June 2007), 32--36. Google ScholarDigital Library
- Bransford, J., Sherwood, R., Vye, N. & Rieser, J. Teaching Thinking and Problem Solving: Research Foundations. American Psychologist, 41, 10 (1986), 1078--1089.Google ScholarCross Ref
- California CTC (Commission on Teacher Credentialing). Supplementary Authorization Guideline Book (CTC, July 2016); https://www.ctc.ca.gov/docs/default-source/credentials/manuals-handbooks/supplement-auth.pdf. Accessed 2018 March 8.Google Scholar
- Chomsky, C. The Acquisition of Syntax in Children from 5 to 10. (Cambridge, MA MIT Press, 1969).Google Scholar
- College Board. AP Data: Program Summary Report 2017. (College Board, 2017). https://secure-media.collegeboard.org/digitalServices/pdf/research/2017/Program-Summary-Report-2017.pdf. Accessed 2017 November 22.Google Scholar
- Corina, D. P., Lawyer, L. A., Hauser, P. & Hirshorn, E.. Lexical Processing in Deaf Readers: An fMRI Investigation of Reading Proficiency. PLOS ONE, 8, 1 (2013), 1--10.Google Scholar
- Corder, S. P. The Significance of Learner's Errors. International Review of Applied Linguistics in Language Teaching, 5, 4 (1967), 161--170.Google Scholar
- CSTA Standards Task Force. CSTA, K-12 Computer Science Standards. (New York: CSTA/ACM, 2011); http://c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/Docs/Standards/CSTA_K-12_CSS.pdf. Accessed 2017 November 22.Google Scholar
- Dulay, H. C. & Burt, M. K. Should We Teach Children Syntax? Language Learning, 23, 2 (1973), 245--258.Google ScholarCross Ref
- Eklund, A., Nichols, T. & Knutsson, H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 113, 28 (2016), 7900--7905.Google ScholarCross Ref
- Floyd, B., Santander, T. & Weimer, W. Decoding the representation of code in the brain: An fMRI study of code review and expertise. In ICSE '17 Proceedings of the 39th International Conference on Software Engineering, (2017), 175--86. Google ScholarDigital Library
- Hamrick, Phillip. Ph.D. Dissertation, Georgetown University, Washington D.C. Development of Conscious Knowledge During Early Incidental Learning Of L2 Syntax. (ProQuest, LLC, 2013), 3642262.Google Scholar
- Hesling, I., Dilharreguy, B., Bordessoules, M. & Allard, M. The Neural Processing of Second Language Comprehension Modulated by the Degree of Proficiency: A Listening Connected Speech fMRI Study. The Open Neuroimaging Journal, 6 (2012), 44--54.Google ScholarCross Ref
- Kidwai, A. Knowledge of Language: Noam Chomsky's Innateness Thesis. Contemporary Education Dialogue, 5,2 (2008), 245--265.Google Scholar
- Kummerfeld, S. K. & Kay, J. The neglected battle fields of Syntax Errors. In Proceedings of the Fifth Australasian Conference on Computing Education, 20, 105--111. (Adelaide, Australia: Australian Computer Society, Inc., 2003). Google ScholarDigital Library
- Lahtinen, E., AlaMutka, K. & Järvinen, H.M. A Study of the Difficulties of Novice Programmers. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (June 27--29, 2005), 14--18. Google ScholarDigital Library
- Lewis-Kraus, G. The Great AI Awakening. New York Times (Magazine). December 14, 2016; http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening. html?_r=0. Accessed 2017 November 22.Google Scholar
- Lo, Mun & Marton, Ference. Towards a science of the art of teaching: Using variation theory as a guiding principle of pedagogical design. International Journal for Lesson and Learning Studies, 1 (2011), 7--22.Google ScholarCross Ref
- Mattern, K., Shaw, E. & Ewing, M. Advance Placement Exam Participation: Is AP Exam Participation and Performance Related to Choice of College Major? (New York, NY: College Board, 2011).Google Scholar
- McGettrick, A., Boyle, R., Ibbett, R., Lloyd, J., Lovegrove, G. & Mander, K. Grand Challenges in Computing: Education---A Summary. The Computer Journal, 48,1 (2005), 42--48. Google ScholarDigital Library
- Monti, M. M., Parsons, L. M. & Osherson, D. N. Thought Beyond Language: Neural Dissociation of Algebra and Natural Language. Association for Psychological Science, 23, 8 (2012), 914--922.Google Scholar
- Morgan, R. & Klaric, J. AP Students in College: An Analysis of Five-Year Academic Careers. (New York, NY: College Board, 2007).Google Scholar
- Moro, Andrea. The Boundaries of Babel: The Brain and the Enigma of Impossible Languages. (Cambridge, MA: MIT Press, 2008).Google ScholarCross Ref
- Pappano, L. Learning to Think Like a Computer. New York Times, April 4, 2017. https://www.nytimes.com/2017/04/04/education/edlife/teaching-students-computer-code.html?_r=0. Accessed 2017 November 22.Google Scholar
- Parnin, C. Scientists Begin Looking at Programmers' Brains: The Neuroscience of Programming. Huffington Post, April 23, 2014; https://www.huffingtonpost.com/chris-parnin/scientists-begin-looking-_b_4829981.html. Accessed 2017 November 22.Google Scholar
- Portnoff, S. M.S. Thesis. California State University, Los Angeles. (1) The case for using foreign language pedagogies in introductory computer programming instruction; (2) A contextualized pre-AP computer programming curriculum: Models and simulations for exploring real-world cross-curricular topics. (ProQuest, LLC, 2016), 262; 10132126. http://pqdtopen.proquest.com/pubnum/10132126.html?FMT=AI. Accessed 2017 November 22.Google Scholar
- Reges, S. & Stepp, M. Building Java Programs. 3rd ed. (Boston: Pearson, 2014). Google ScholarDigital Library
- Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., Saake, G & Brechmann, A. Understanding Understanding Source Code with Functional Magnetic Resonance Imaging. In Proceedings of the 36th International Conference on Software Engineering (2014), 378--389. Google ScholarDigital Library
- Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A. & Brechmann, A. Understanding Programmers' Brains with fMRI. In Frontiers in Neuroinformatics Conference Abstract: Neuroinformatics (2014).Google Scholar
- Tagarelli, K.M. Ph.D. Dissertation, Georgetown University, Washington D.C. The Neurocognition of Adult Second Language Learning: An fMRI Study. (ProQuest, LLC, 2014); 3642262.Google Scholar
- Watson, C., & Li, F. W. Failure Rates in Introductory Programming Revisited. In Proceedings of the 2014 Conference On Innovation & Technology In Computer Science Education. (Uppsala, Sweden: Association for Computing Machinery, June 2014), 39--44. Google ScholarDigital Library
- Woolfolk, A. Educational Psychology. 9th ed. (Boston: Pearson, Allyn and Bacon, 2004).Google Scholar
- Yokoyama, S., Kim, J., Uchida, S., Okamoto, H., Bai, C., Yusa, N. et al. A longitudinal fMRI study of neural plasticity in the second language lexical processing. Neuroscience Research Abstracts 58S (2007), S174.Google Scholar
Index Terms
- The introductory computer programming course is first and foremost a language course
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