Scaffolding Model for Efficient Programming Learning Based on Cognitive Load Theory

Programming learning for beginners requires tremendous amount of exposure to understand the logic in each programming solution using the basic concepts despite the overwhelming syntax it might carries. Learning programming through examples with careful walkthrough builds learners’ confidence to embark with problems of any designs, avoids frustration due to syntax error and unintentional bugs. Scaffolding involves meta-programming approach of building software applications using supported materials that provides some inspiration of how the program could be developed. This research identifies important attributes in programming and proposes a scaffold model to enhance programming learning efficiency especially among novice programmers. The study applies cognitive load theory by providing users with two types of instructional design as learning support to reduce mental effort applied in the working memory i.e. worked-example and goal free programming problem solutions. The model is expected to help instructors in systematically organizing programming materials for any language or programming environment for efficient programming learning. Keywords—programming, scaffolding, learning support, cognitive load, worked-example

[1]  Maomi Ueno,et al.  IRT-Based Adaptive Hints to Scaffold Learning in Programming , 2018, IEEE Transactions on Learning Technologies.

[2]  Walter Cazzola,et al.  Gradually Learning Programming Supported by a Growable Programming Language , 2015, IEEE Transactions on Emerging Topics in Computing.

[3]  Camilo Vieira,et al.  Exploring Design Characteristics of Worked Examples to Support Programming and Algorithm Design , 2015 .

[4]  Benedict du Boulay,et al.  Learning programming via worked-examples: Relation of learning styles to cognitive load , 2014, Comput. Hum. Behav..

[5]  Graham Cooper,et al.  Mindstorms robots and the application of cognitive load theory in introductory programming , 2013, Comput. Sci. Educ..

[6]  Kyle J. Harms.  Applying cognitive load theory to generate effective programming tutorials , 2013, 2013 IEEE Symposium on Visual Languages and Human Centric Computing.

[7]  Fred Paas,et al.  The goal-free effect , 2012 .

[8]  J. Sweller,et al.  Putting Students on the Path to Learning: The Case for Fully Guided Instruction. , 2012 .

[9]  K. Koedinger,et al.  Accounting for Beneficial Effects of Worked Examples in Tutored Problem Solving , 2010 .

[10]  Slava Kalyuga,et al.  Facilitating Flexible Problem Solving: A Cognitive Load Perspective , 2010 .

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

[12]  G. Murugusundaramoorthy,et al.  Certain Subclasses of Starlike Functions of Complex Order Involving Generalized Hypergeometric Functions , 2010, Int. J. Math. Math. Sci..

[13]  T. Jong.  Cognitive load theory, educational research, and instructional design: some food for thought , 2010 .

[14]  Anna Eckerdal,et al.  Novice Programming Students' Learning of Concepts and Practise , 2009 .

[15]  M. Caspersen,et al.  Instructional design of a programming course: a learning theoretic approach , 2007, ICER '07.

[16]  G. Murugusundaramoorthy,et al.  Starlike and convex functions of complex order involving the Dziok-Srivastava operator , 2007 .

[17]  R. Catrambone,et al.  Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations? * , 2006 .

[18]  Nak Eun Cho,et al.  MULTIPLIER TRANSFORMATIONS AND STRONGLY CLOSE-TO-CONVEX FUNCTIONS , 2003 .

[19]  Stuart K Garner.  Learning to Program Using Part-Complete Solutions , 2003 .

[20]  Wendy Doubé,et al.  Applying Cognitive load theory to computer science education , 2003, PPIG.

[21]  S. Kanas,et al.  Subclasses of K-uniformly Convex and Starlike Functions Defined by Generalized Derivative, II , 2001 .

[22]  Stanisława Kanas,et al.  Conic regions and k -uniform convexity , 1999 .

[23]  Kuo-En Chang,et al.  A Programming Learning System for Beginners - A Completion Strategy Approach , 1996, Intelligent Tutoring Systems.

[24]  A. Goodman,et al.  On uniformly convex functions , 1991 .

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

[26]  Peter H. Feiler,et al.  An Incremental Programming Environment , 1981, IEEE Transactions on Software Engineering.

[27]  J. E. Littlewood,et al.  On Inequalities in the Theory of Functions , 1925 .