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Towards The Use Of A Novel Method: The First Experiences On Measuring The Cognitive Load Of Learned Programming Skills

Year 2013, Volume: 14 Issue: 1, 166 - 184, 01.03.2013

Abstract

Teaching object-oriented programming (OOP) is a difficult task, especially to the beginners. First-time learners also find it difficult to understand. Although there is a considerable amount of study on the cognitive dimension, a few study points out its physiological meaning. Moreover, it has been suggested that neuroscientific studies and methods can enable educational researchers gain an insight into brain and cognitive processes as well. Therefore, this experimental study explored the previously learned OOP skills in terms of cognitive load. By using the functional near-infrared spectroscopy (fNIRS) method, we measured the cognitive load of participants when executing OOP tasks. The average oxygenation changes in prefrontal cortex of the brain represented their total cognitive response to a set of OOP tasks. There were two research questions investigated by this study. The first research question explored whether the average oxygenation changed according to the participants’ academic achievements and task completion status. The second research question was for identifying the instant changes in the oxygenations to find out which programming tasks were more contributing to the oxygenation. Later, we compared the findings with experts’ judgments. We observed that the fNIRS system was an effective and promising technology for monitoring cognitive tasks both in classrooms and in experimental environments.

References

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  • Merriënboer, J.G., & Paas, G.W.C. (1990). Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice. Computers in
  • Human Behavior, Vol.6, pp. 273-289. Modsimmer, (2010). The seminar on Functional Near-Infrared (fNIR) Spectroscopy system. Retrieved from “http://www.modsim.metu.edu.tr/index.php?option= com_content&view=article&id=360&Itemid=50” on November 12, 2010.
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  • Schneider, W. & Detweiler, M. (1987). A connectionist / control architecture for working memory. The Psychology of Learning and Motivation, Vol.21, pp. 53-119.
  • Shiffrin, R.M. (1976). Capacity limitations in information processing, attention and memory. In W.K. Estes (Ed.), Handbook of learning and cognitive processes. Vol. 4, pp. 177-236, Hillsdale, N.J.: Erlbaum.
  • Sohn,Y.W., & Doane, S.M. (1997). Cognitive constraints on computer problem-solving skills. Journal of Experimental Psychology: Applied, Vol.3, pp. 288-312.
  • Sweller, J., Van Merriënboer, Jeroen, J.G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, Vol.10, pp. 251-295.
  • Szucs, D., Soltész, F., Jármi, E & Csépe, V. (2007). The speed of magnitude processing and executive functions in controlled and automatic number comparison in children: An electro-encephalography study. Behavioral and Brain Functions, 3, 23, doi:10.1186/1744- 9081-3-23.
  • Xinogalos, S. (2010). Guidelines for designing and teaching an effective object-oriented design and programming course. Advance Learning, pp. 397-422.
  • Van Nes, F. & De Lange, J. (2007). Mathematics education and neurosciences: Relating spatial structures to the development of spatial sense and number sense. The Montana
  • Mathematics Enthusiast, 4:2, pp. 210–229. Van Nes, F. (2011). Mathematics education and neurosciences: Towards interdisciplinary insights into the development of young children’s mathematical abilities. Educational
  • Philosophy and Theory, Vol. 43, No. 1, 2011. doi: 10.1111/j.1469-5812.2010.00710.x
  • Villringer A. & Chance B. (1997) Non-invasive optical spectroscopy and imaging of human brain function. Trends in Neurosciences, Vol.20, pp. 435-42.
  • Waugh, N.C., & Norman, D.A. (1968). The measure of interference in primary memory.
  • Journal of Verbal Learning and Verbal Behavior, Vol. 7, pp. 617-626. White, G. & Sivitanides M. (2005). Cognitive differences between procedural programming and object oriented programming. Journal of Information Technology and Management, Vol.6, Issue 4, pp. 330-350.
  • Y. W., Soan & S.M., Doane (2003) Roles of working memory capacity and long-term working memory skill in complex task performance. Memory and Cognition, 31, 3, pp.458-466.
Year 2013, Volume: 14 Issue: 1, 166 - 184, 01.03.2013

Abstract

References

  • Artino, A. R. (2008). Cognitive load theory and the role of learner experience: An abbreviated review for educational practitioners. Association for the Advancement of
  • Computing In Education Journal, 16, 4, pp. 425-439. Atkinson, R. C. & Shiffrin, R.M. (1968). Human memory: A proposed system and its control processes. In K.W. Spence & J.T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory. Vol. 2, pp. 742-775, New York: Academic Press.
  • Ayaz, H., Willems, B., Bunce, S., Shewokis, P. A., Izzetoglu, K., Hah, S., Deshmukh, A. & Onaral, B. (2010). Cognitive workload assessment of air traffic controllers using optical brain imaging sensors. In T. Marek, W. Karwowski & V. Rice (Eds.), Advances in
  • Understanding Human Performance: Neuroergonomics, Human Factors Design, and Special Populations, pp. 21-32, CRC Press, Taylor & Francis Group. Ayaz, H., Shewokis, P.A., Bunce, S., Izzetoglu, K., Willems, B. & Onaral, B. (2011). Optical brain monitoring for operator training and mental workload assessment. NeuroImage, doi:1016/j.neuroimage.2011.06.023.
  • Berninger, V. W. & Corina, D. (1998) Making cognitive neuroscience educationally relevant: Creating bidirectional collaborations between educational psychology and cognitive neuroscience. Educational Psychology Review, 10, 3, pp. 343–354.
  • Bozkurt, A., Rosen , A., Rosen, H. & Onaral, B. (2005). A portable near infrared spectroscopy system for bedside monitoring of newborn brain. BioMedical Engineering OnLine, doi:10.1186/1475-925X-4-29.
  • Bunce S.C., Izzetoglu M., Izzetoglu K., Onaral B. & Pourrezai K. (2006). Functional Near
  • Infrared Spectroscopy: An emerging neuroimaging modality. IEEE Engineering in Medicine and Biology Magazine, pp. 54-62. Campbell, S. R. (2006) Educational Neuroscience: New horizons for research in mathematics education. Proceedings of the 30th Conference of the International Group for the Psychology of Mathematics Education, Vol. 2, pp. 257–264.
  • Doane, S. M., Sohn, Y. W., McNamara, D. S., & Adams D. (2000). Comprehension-based skill acquisition. Cognitive Science, Vol.24, pp. 1-52.
  • Ericsson, K. A., Kinstch, W. (1995). Long-term working memory. Psychological Review, Vol. 102, No. 2, pp. 211-245.
  • Holtzer, R., Mahoney, J.R., Izzetoglu, M., Izzetoglu, K., Onaral B. & Verghese J. (2011). fNIRS study of walking and walking while talking in young and old individuals. Journal of
  • Gerontology, doi:10.1093/gerona/glr068.
  • Izzetoglu, K., Bunce S., Onaral B. & Chance B. (2004). Functional brain imaging using near-infrared during cognitive tasks. International Journal of Human-Computer Interaction, 17 (2), 211-227.
  • Izzetoglu, M., Izzetoglu, K., Bunce S., Ayaz H., Devaraj A. & Onaral B. (2005). Functional near-infrared neuroimaging. IEEE Transactions Neural Systems and Rehabilitation Engineering, 13, pp. 153-159.
  • Izzetoglu M., Bunce S. C., Izzetoglu K., Onaral B. & Pourrezai K. (2007). Functional brain imaging using near-infrared technology: Assessing cognitive activity in real-life situations. IEEE Engineering in Medicine and Biology Magazine, pp. 36-44.
  • Jobsis F.F. (1977). Noninvasive infrared monitoring of cerebral and myocardial sufficiency and circulatory parameters. Science, 198, pp. 1264 -1267.
  • Griffin, S. & Case, R. (1997) Re-thinking the primary school math curriculum: An approach based on cognitive science. Issues in Education, 3, 1, pp. 1-49.
  • Kirschner, P. A., Sweller, J., & Clark, R.E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem- based, experiential, and inquiry-based teaching. Educational Psychologist, 41, pp. 75-86.
  • Lenf R.D., Orihuella-Espina F., Elwell C.E., Athanasiou T., Delpy D.T., Darzi A. & Yang G.Z. (2011). Assessment of the cerebral cortex during motor task behaviours in adults: A systematic review of functional near infrared spectroscopy (fNIRS) studies. Neuroimage, 54, pp. 2922-2936.
  • León-Carrióna J., Izzetoglu M., Izzetoglu K., Martín-Rodrígueza J.F., Damas-López J., Barroso J.M.M. & Morales M. R. D. (2010). Efficient learning produces spontaneous neural repetition suppression in prefrontal cortex. Behavioural Brain Research, 208, pp. 502- 50
  • Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, pp. 43-52.
  • Merriënboer, J.G., & Paas, G.W.C. (1990). Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice. Computers in
  • Human Behavior, Vol.6, pp. 273-289. Modsimmer, (2010). The seminar on Functional Near-Infrared (fNIR) Spectroscopy system. Retrieved from “http://www.modsim.metu.edu.tr/index.php?option= com_content&view=article&id=360&Itemid=50” on November 12, 2010.
  • OECD (2002). Understanding the brain: Towards a new learning science (Paris, OECD).
  • Schneider, W. & Detweiler, M. (1987). A connectionist / control architecture for working memory. The Psychology of Learning and Motivation, Vol.21, pp. 53-119.
  • Shiffrin, R.M. (1976). Capacity limitations in information processing, attention and memory. In W.K. Estes (Ed.), Handbook of learning and cognitive processes. Vol. 4, pp. 177-236, Hillsdale, N.J.: Erlbaum.
  • Sohn,Y.W., & Doane, S.M. (1997). Cognitive constraints on computer problem-solving skills. Journal of Experimental Psychology: Applied, Vol.3, pp. 288-312.
  • Sweller, J., Van Merriënboer, Jeroen, J.G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, Vol.10, pp. 251-295.
  • Szucs, D., Soltész, F., Jármi, E & Csépe, V. (2007). The speed of magnitude processing and executive functions in controlled and automatic number comparison in children: An electro-encephalography study. Behavioral and Brain Functions, 3, 23, doi:10.1186/1744- 9081-3-23.
  • Xinogalos, S. (2010). Guidelines for designing and teaching an effective object-oriented design and programming course. Advance Learning, pp. 397-422.
  • Van Nes, F. & De Lange, J. (2007). Mathematics education and neurosciences: Relating spatial structures to the development of spatial sense and number sense. The Montana
  • Mathematics Enthusiast, 4:2, pp. 210–229. Van Nes, F. (2011). Mathematics education and neurosciences: Towards interdisciplinary insights into the development of young children’s mathematical abilities. Educational
  • Philosophy and Theory, Vol. 43, No. 1, 2011. doi: 10.1111/j.1469-5812.2010.00710.x
  • Villringer A. & Chance B. (1997) Non-invasive optical spectroscopy and imaging of human brain function. Trends in Neurosciences, Vol.20, pp. 435-42.
  • Waugh, N.C., & Norman, D.A. (1968). The measure of interference in primary memory.
  • Journal of Verbal Learning and Verbal Behavior, Vol. 7, pp. 617-626. White, G. & Sivitanides M. (2005). Cognitive differences between procedural programming and object oriented programming. Journal of Information Technology and Management, Vol.6, Issue 4, pp. 330-350.
  • Y. W., Soan & S.M., Doane (2003) Roles of working memory capacity and long-term working memory skill in complex task performance. Memory and Cognition, 31, 3, pp.458-466.
There are 37 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Murat Pas Uysal This is me

Publication Date March 1, 2013
Submission Date February 27, 2015
Published in Issue Year 2013 Volume: 14 Issue: 1

Cite

APA Uysal, M. P. (2013). Towards The Use Of A Novel Method: The First Experiences On Measuring The Cognitive Load Of Learned Programming Skills. Turkish Online Journal of Distance Education, 14(1), 166-184.