The role of physicality in rich programming environments

Computer science proficiency continues to grow in importance, while the number of students entering computer science-related fields declines. Many rich programming environments have been created to motivate student interest and expertise in computer science. In the current study, we investigated whether a recently created environment, Robot Virtual Worlds (RVWs), can be used to teach computer science principles within a robotics context by examining its use in high-school classrooms. We also investigated whether the lack of physicality in these environments impacts student learning by comparing classrooms that used either virtual or physical robots for the RVW curriculum. Results suggest that the RVW environment leads to significant gains in computer science knowledge, that virtual robots lead to faster learning, and that physical robots may have some influence on algorithmic thinking. We discuss the implications of physicality in these programming environments for learning computer science.

[1]  Mark Guzdial,et al.  A multi-national, multi-institutional study of assessment of programming skills of first-year CS students , 2001, ITiCSE-WGR '01.

[2]  Barry S. Fagin,et al.  Quantitative analysis of the effects of robots on introductory Computer Science education , 2002, JERC.

[3]  Z. C. Zacharia,et al.  Comparing and combining real and virtual experimentation: an effort to enhance students' conceptual understanding of electric circuits , 2007, J. Comput. Assist. Learn..

[4]  Elizabeth V. Howard,et al.  Programming in Pairs with Alice to Improve Confidence, Enjoyment, and Achievement , 2006 .

[5]  Tomi Jaakkola,et al.  Fostering elementary school students' understanding of simple electricity by combining simulation and laboratory activities , 2008, J. Comput. Assist. Learn..

[6]  Gertrude W. Abramson.  Dann, W., Cooper, S. & Pausch, R. (2006). Learning to program with Alice. Upper Saddle River, NJ: Prentice-Hall , 2008 .

[7]  G. Olympiou,et al.  Blending Physical and Virtual Manipulatives: An Effort to Improve Students' Conceptual Understanding through Science Laboratory Experimentation , 2012 .

[8]  Pearl Brereton,et al.  Systematic literature review: teaching novices programming using robots , 2011, IET Softw..

[9]  Jerry B. Weinberg,et al.  Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education , 2006, AI Mag..

[10]  Robert McCartney,et al.  A multi-national study of reading and tracing skills in novice programmers , 2004, ITiCSE-WGR '04.

[11]  Douglas H. Clements,et al.  Metacomponential Development in a Logo Programming Environment , 1990 .

[12]  J. Nunnally.  Psychometric Theory (2nd ed), New York: McGraw-Hill. , 1978 .

[13]  Rosamund Sutherland,et al.  Connecting theory and practice: Results from the teaching of Logo , 1993 .

[14]  Pilar Martínez-Jiménez,et al.  Learning in Chemistry with Virtual Laboratories , 2003 .

[15]  Mordechai Ben-Ari,et al.  Learning computer science concepts with scratch , 2010, ICER '10.

[16]  Eric Rosenbaum,et al.  Scratch: programming for all , 2009, Commun. ACM.

[17]  L. Cronbach.  Coefficient alpha and the internal structure of tests , 1951 .

[18]  David J. Barnes,et al.  Teaching introductory Java through LEGO MINDSTORMS models , 2002, SIGCSE '02.

[19]  Edward R. Sykes,et al.  Determining the Effectiveness of the 3D Alice Programming Environment at the Computer Science I Level , 2007 .

[20]  Randy Pausch,et al.  Learning To Program With Alice , 2004 .

[21]  Richard C. Anderson,et al.  Delay-retention effect with multiple-choice tests , 1972 .

[22]  N. Sanjay Rebello,et al.  Quantum mechanics for everyone: Hands-on activities integrated with technology , 2002 .

[23]  Seymour Papert,et al.  Mindstorms: Children, Computers, and Powerful Ideas , 1981 .

[24]  Richard C. Anderson,et al.  Delay-retention effect in natural classroom settings. , 1975 .