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Quantifying Student Progress Through Bloom’s Taxonomy Cognitive Categories in Computer Programming Courses

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Emerging Computing and Information Technologies II

Tagged Division

Computing & Information Technology

Tagged Topic

Diversity

Page Count

13

Page Numbers

26.1295.1 - 26.1295.13

DOI

10.18260/p.24632

Permanent URL

https://peer.asee.org/24632

Download Count

86

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Paper Authors

biography

Candido Cabo New York City College of Technology/City University of New York

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Candido Cabo is a Professor in the Department of Computer Systems Technology at New York City College of Technology, City University of New York (CUNY). He earned the degree of Ingeniero Superior de Telecomunicacion from the Universidad Politecnica de Madrid (Spain) in 1982, and a Ph.D. in Biomedical Engineering from Duke University (Durham, NC) in 1992. He was a post-doctoral fellow at Upstate Medical Center, State University of New York (Syracuse, NY), and a research scientist in the Department of Pharmacology at the College of Physicians and Surgeons of Columbia University (New York, NY). 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

Quantifying Student Progress through Bloom’s Taxonomy Cognitive Categories in Computer Programming CoursesComputer programming courses are gateway courses with low passing grades, which may resultin student attrition and transfers out of engineering and computer science degrees. Progress instudent learning can be conceptualized by the different cognitive levels or categories described inBloom’s taxonomy, which, from the lowest to the highest order processes, include: knowledge,comprehension, application, analysis, evaluation, and synthesis.The purpose of this study is to gain insight into how students transfer their conceptual knowledgeand comprehension of computer programming concepts (knowledge and comprehensioncategories in Bloom’s taxonomy) into their ability to write computer programs (applicationcategory in Bloom’s taxonomy), using Bloom’s taxonomy as a framework. The followingresearch questions were addressed in this study: 1) Is adequate performance in conceptualunderstanding sufficient for a student to write viable computer programs? 2) How big is the gapbetween conceptual understanding of programming concepts and the ability to apply thoseconcepts to write viable computer programs? 3) Are some concepts more important than othersin determining students’ ability to write viable programs?A total of 62 students who took a first computer programming course using Java participated inthis study from spring 2013 to spring 2014. Novice computer programming students face twobarriers in their progress to become proficient programmers: a good understanding ofprogramming concepts (first two categories in Bloom’s taxonomy) and the ability to apply thoseconcepts (third category in Bloom’s taxonomy) to write viable computer programs. About 35%of students had an acceptable performance in both conceptual understanding of programmingconcepts and ability to write viable programs. About 44% of students had an inadequateperformance in both concepts and programming skills. 16% of students had an adequateunderstanding of computer concepts but were unable to transfer that understanding into writingviable computer programs. Finally, 5% of students were able to produce viable computerprograms without an adequate conceptual understanding. Of the students who had adequateunderstanding of computer concepts, 69% were able to write viable computer programs. Linearregression modeling suggests that conceptual understanding is a good predictor (R squared =74%) of the ability to apply that knowledge to write computer programs. Multiple regressionanalysis shows that some concepts are better predictors of programming skills than others:performance in conceptual assessments on Java syntax, classes and repetition structures arebetter predictors of the ability to write viable programs than performance in conceptualassessments on assignment operators, program design using methods and arrays.In conclusion: 1) Many students (44%) do not reach and adequate level of conceptual knowledgeand understanding and cannot write viable computer programs; 2) Some students (16%) cannottransfer conceptual knowledge and understanding into viable computer programs; 3) Regressionanalysis between student performance in programming concepts and students’ ability to writeviable computer programs can be used to align better the concepts taught and the expectedstudent skills, and to facilitate student progress through the different cognitive levels in Bloom’staxonomy.

Cabo, C. (2015, June), Quantifying Student Progress Through Bloom’s Taxonomy Cognitive Categories in Computer Programming Courses Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24632

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2015 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015