Using Visualization to Reduce the Cognitive Load of Threshold Concepts in Computer Programming

This Full Paper in the Research to Practice Category reports on an empirical empirical study in which novel educational tools and techniques were employed to teach fundamentals of problem decomposition - a cognitive task transcending disciplines. Within the discipline of computer science, problem decomposition is recognized as a foundational activity of software development. Factors that contribute to the complexity of this activity include: (1) recognizing patterns within an algorithm, (2) mapping the understanding of an algorithm to the syntax of a given programming language, and (3) complexity intrinsic to the problem domain itself.Cognitive load theory states that learning outcomes can be positively affected by reducing the extraneous cognitive load associated with learning objectives as well as by changing the nature of what is learned. In the study reported upon here, a novel instructional method was developed to decrease students’ cognitive load. Novel instructional content supported by a custom visualization tool was used in a classroom setting in order to help novice programmers develop an understanding of function-based problem decomposition within the context of a visual domain. Performance on outcome measures (a quiz and assignment) were compared between the new method and the traditional teaching method demonstrated that students were significantly more successful at demonstrating mastery when using the new instructional method.

[1]  John Maloney,et al.  The Scratch Programming Language and Environment , 2010, TOCE.

[2]  H. King Threshold concepts and troublesome knowledge , 2006 .

[3]  Jan H. F. Meyer,et al.  Threshold Concepts and Transformational Learning , 2010 .

[4]  R. Kurzweil,et al.  The law of accelerating returns , 2008 .

[5]  Wen-Hao Huang,et al.  Evaluating learners' motivational and cognitive processing in an online game-based learning environment , 2011, Comput. Hum. Behav..

[6]  Richard Catrambone,et al.  Using Subgoal Learning and Self-Explanation to Improve Programming Education , 2016, CogSci.

[7]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[8]  Betty N Love,et al.  The art of the Wunderlich cube and the development of spatial abilities , 2018, Int. J. Child Comput. Interact..

[9]  J. Sweller Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load , 2010 .

[10]  Mark Guzdial,et al.  Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications , 2012, ICER '12.

[11]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[12]  T. Gog,et al.  Segmentation of Worked Examples: Effects on Cognitive Load and Learning , 2012 .

[13]  R. Land Threshold Concepts and Troublesome Knowledge (1): linkages to ways of thinking and practising within the disciplines , 2003 .

[14]  Ian Goodwin,et al.  Bricklayer: Elementary Students Learn Math through Programming and Art , 2018, SIGCSE.

[15]  R. Kurzweil The age of spiritual machines: when computers exceed human intelligence , 1998 .

[16]  Felix Stalder,et al.  The Age of Spiritual Machines: When Computers Exceed Human Intelligence , 2001, CSOC.

[17]  Ray Land,et al.  Threshold Concepts and Troublesome Knowledge , 2013 .

[18]  Betty Love,et al.  The Bricklayer Ecosystem - Art, Math, and Code , 2016, TFPIE.

[19]  Ami Marowka,et al.  What is the GRID? , 2002, Scalable Comput. Pract. Exp..

[20]  Jan H. F. Meyer,et al.  Threshold concepts and troublesome knowledge (2): Epistemological considerations and a conceptual framework for teaching and learning , 2005 .