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Transition from Concepts to Practical Skills in Computer Programming Courses: Factor and Cluster Analysis

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Conference

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

Curricular Issues in Computing and Information Technology Programs II

Tagged Division

Computing & Information Technology

Page Count

12

Page Numbers

24.1280.1 - 24.1280.12

Permanent URL

https://peer.asee.org/23213

Download Count

40

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Paper Authors

biography

Candido Cabo New York City College of Technology/CUNY

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Candido Cabo earned the degree of Ingeniero Superior de Telecomunicacion from the Universidad Politecnica de Madrid in 1982, and a Ph.D. in Biomedical Engineering from Duke University in 1992. He was a post-doctoral fellow at Upstate Medical Center, State University of New York, and a research scientist in the Department of Pharmacology at the College of Physicians and Surgeons of Columbia University. In 2000, he joined New York City College of Technology, City University of New York (CUNY) where he is a Professor in the Department of Computer Systems Technology. Since 2005, he has been a member of the doctoral faculty at the CUNY Graduate Center. His research interests include computer science and engineering education and the use of computational models to understand and solve problems in biology.

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Abstract

Transition from Concepts to Practical Skills in Computer Programming Courses: Factor and Cluster AnalysisComputer programming courses are gateway courses with low passing grades, which may resultin student attrition and transfers out of engineering and computer science degrees. Barriers tosuccess include a good understanding of programming concepts and the ability to apply thoseconcepts to write viable computer programs.In this paper, we analyze the determinants of the transition from concepts to skills in computerprogramming courses using factor and cluster analysis. The purpose of this study is to answertwo questions related to computer programming teaching and learning: 1) Which are thecorrelations and interdependencies in student understanding of different computer programmingconcepts? Which are the cognitive challenges that students find when learning programmingconcepts? 2) How the understanding of different programming concepts relate to practical skillsin computer programming. What determines a successful transition from understanding theconcepts to the ability to write viable computer programs?We grouped student performance computer programming concepts assessments (in a first Javaprogramming course) in seven different categories: assignment operators, repetition structures,selection structures, program design using methods, arrays, classes and Java syntax. Factoranalysis identified two factors (components) grouping the interdependencies and correlationsbetween programming concept categories. A first component correlated with the repetition andselection categories, and could be referred to as the “algorithmic” component. The secondcomponent correlated with the methods, arrays and assignment categories, and could be referredas the “structural” component. Student performance in conceptual categories related to the“algorithmic” factor was significantly better than in conceptual categories related to the“structural” factor. Cluster analysis showed that student performance in the “structural”conceptual component is predictive of the student’s ability to solve practical computerprogramming problems.We conclude that a strong emphasis in the structural components of computer programming (i.e.program design using methods, use of the assignment operator, and use of data structures likearrays) is necessary for a successful transition from concepts to skills in computer programmingcourses.

Cabo, C. (2014, June), Transition from Concepts to Practical Skills in Computer Programming Courses: Factor and Cluster Analysis Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/23213

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