Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-03T00:44:28.068Z Has data issue: false hasContentIssue false
  • English
  • Français

Introducing Computer Science to High School Students Through Logic Programming

Published online by Cambridge University Press:  14 November 2018

TIMOTHY T. YUEN ,
MARITZA REYES  and
YUANLIN ZHANG
TIMOTHY T. YUEN
Affiliation:
University of Texas at San Antonio, San Antonio, TX, USA (e-mail: [email protected])
MARITZA REYES
Affiliation:
University of Texas at Austin, Austin, TX, USA (e-mail: maritza [email protected])
YUANLIN ZHANG
Affiliation:
Texas Tech University, Lubbock, TX, USA (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper investigates how high school students in an introductory computer science (CS) course approach computing in the logic programming (LP) paradigm. This qualitative study shows how novice students operate within the LP paradigm while engaging in foundational computing concepts and skills: students are engaged in a cyclical process of abstraction, reasoning, and creating representations of their ideas in code while also being informed by the (procedural) requirements and the revision/debugging process. As these computing concepts and skills are also expected in traditional approaches to introductory K-12 CS courses, this paper asserts that LP is a viable paradigm choice for high school novices.

Keywords

CS educationhigh school CSdeclarative programminglogic programminganswer set programming
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 2: User-Oriented Logic Programming and Reasoning Paradigms , March 2019 , pp. 204 - 228
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The authors thank Cynthia Perez, Rocky Upchurch, and Edward Wertz for their contributions to this project, and thank Michael Gelfond for valuable discussions and sharing his teaching materials. This work is partially supported by NSF grant CNS-1359359. We thank the anonymous reviewers whose feedback helps to improve the quality of this paper.

References

Allan, W., Coulter, B., Denner, J., Erickson, J., Lee, I., Malyn-Smith, J. and Martin, F. 2011. Computational Thinking for Youth. White Paper for the ITEST Small Working Group on Computational Thinking (CT).Google Scholar
Alviano, M., Faber, W., Leone, N., Perri, S., Pfeifer, G. and Terracina, G. 2011. The disjunctive datalog system DLV. In Datalog Reloaded. Springer, Berlin, 282–301.CrossRefGoogle Scholar
Balai, E., Gelfond, M. and Zhang, Y. 2013. Towards answer set programming with sorts. In International Conference on Logic Programming and Nonmonotonic Reasoning. Springer, Berlin, 135–147.CrossRefGoogle Scholar
Ball, T. and Zorn, B. 2015. Teach foundational language principles. Communications of the ACM 58, 5, 30–31.CrossRefGoogle Scholar
Barr, V. and Stephenson, C. 2011. Bringing computational thinking to k-12: What is involved and what is the role of the computer science education community? ACM Inroads 2, 1, 48–54.CrossRefGoogle Scholar
Beux, S., Briola, D., Corradi, A., Delzanno, G., Ferrando, A., Frassetto, F., Guerrini, G., Mascardi, V., Oreggia, M., Pozzi, F., Solimando, A. and Tacchella, A. 2015. Computational thinking for beginners: A successful experience using Prolog. In CILC. 31–45.Google Scholar
Brennan, K. and Resnick, M. 2012. New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association, Vancouver, Canada. 1–25.Google Scholar
Brewka, G., Eiter, T. and Truszczyński, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92–103.CrossRefGoogle Scholar
Chakravarty, M. M. and Keller, G. 2004. The risks and benefits of teaching purely functional programming in first year. Journal of Functional Programming 14, 1, 113–123.CrossRefGoogle Scholar
Chen, T.-Y., Monge, A. and Simon, B. 2006. Relationship of early programming language to novice generated design. ACM SIGCSE Bulletin 38, 1, 495–499.CrossRefGoogle Scholar
Clocksin, W. and Mellish, C. S. 2003. Programming in PROLOG. Springer-Verlag, Berlin Heidelberg.CrossRefGoogle Scholar
CollegeBoard. 2017. AP computer science principles including the curriculum framework. Advanced Placement Program, The College Board.Google Scholar
Cooper, S., Dann, W. and Pausch, R. 2000. Alice: A 3-d tool for introductory programming concepts. Journal of Computing Sciences in Colleges 15, 107–116 (Consortium for Computing Sciences in Colleges).Google Scholar
Corbin, J. and Strauss, A. 2008. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage Publications, Inc, Thousand Oaks, CA, USA.Google Scholar
CSTA. 2012. CSTA k-12 computer science standards. Computer Science Teachers Association.Google Scholar
Dovier, A., Benoli, P., Brocato, M. C., Dereani, L. and Tabacco, F. 2016. Reasoning in high schools: Do it with ASP! In CILC, 205–213.Google Scholar
Feaster, Y., Segars, L., Wahba, S. K. and Hallstrom, J. O. 2011. Teaching CS unplugged in the high school (with limited success). In Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education. ACM, 248–252.Google Scholar
Febbraro, O., Reale, K. and Ricca, F. 2011. ASPIDE: Integrated development environment for answer set programming. In Logic Programming and Nonmonotonic Reasoning– 11th International Conference, LPNMR 2011, Vancouver, Canada, 16–19 May 2011. Proceedings, 317–330.Google Scholar
Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T. and Schneider, M. 2011. Potassco: The potsdam answer set solving collection. AI Communications 24, 2, 107–124.Google Scholar
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Ginsburg, H. 1997. Entering the Child’s Mind: The Clinical Interview in Psychological Research and Practice. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Grover, S. and Pea, R. 2013. Computational thinking in K-12 a review of the state of the field. Educational Researcher 42, 1, 38–43.CrossRefGoogle Scholar
Guzdial, M. 2004. Programming environments for novices. Computer Science Education Research 2004, 127–154.Google Scholar
Hemmendinger, D. 2010. A plea for modesty. ACM Inroads 1, 2, 4–7.CrossRefGoogle Scholar
K-12 CS Framework Committee. 2016. K-12 computer science framework. URL: http://www.k12cs.org. [Accessed on January 8, 2017].Google Scholar
Kahn, K. 2004. Toontalk-steps towards ideal computer-based learning environments. A Learning Zone of One’s Own: Sharing Representations and Flow in Collaborative Learning Environments. IOS Press, Amsterdam. 253–270.Google Scholar
Kelleher, C. and Pausch, R. 2007. Using storytelling to motivate programming. Communications of the ACM 50, 7, 58–64.CrossRefGoogle Scholar
Kleinberg, J. and Tardos, E. 2006. Algorithm Design. Pearson Education, India.Google Scholar
Kowalski, R. A. 1982. Logic as a computer language for children. In ECAI, 2–10.Google Scholar
Kowalski, R. A. 1987. Logic as a Computer Language for Children. Wiley-Interscience, New York, NY, USA.Google Scholar
Kowalski, R. A. 2014. Logic programming. Computational Logic, Volume 9 (Handbook of the History of Logic). North Holland, Amsterdam.Google Scholar
Levesque, H. J. 2012. Thinking as Computation: A First Course. The MIT Press, Cambridge.CrossRefGoogle Scholar
Lye, S. Y. and Koh, J. H. L. 2014. Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior 41, 51–61.CrossRefGoogle Scholar
Marcopoulos, E., Reotutar, C. and Zhang, Y. 2017. An online development environment for answer set programming. CoRR abs/1707.01865.Google Scholar
Mendelsohn, P., Green, T. and Brna, P. 1990. Programming languages in education: The search for an easy start. Psychology of Programming. Elsevier, Amsterdam. 175–200.CrossRefGoogle Scholar
Nichol, J., Dean, J. and Briggs, J. 1988. Prolog, Children and Students. Nichols Publishing Co., New York, NY, USA.Google Scholar
Papert, S. 1980. Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc., New York, NY, USA.Google Scholar
Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., Devlin, M. and Paterson, J. 2007. A survey of literature on the teaching of introductory programming. ACM SIGCSE Bulletin 39, 4, 204–223.CrossRefGoogle Scholar
Resnick, M. and Rosenbaum, E. 2013. Designing for tinkerability. Design, Make, Play: Growing the Next Generation of STEM Innovators. Routledge, London. 163–181.Google Scholar
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B. and Kafai, Y. 2009. Scratch: Programming for all. Communications of the ACM 52, 11, 60–67.CrossRefGoogle Scholar
Scherz, Z. and Haberman, B. 1995. Logic programming based curriculum for high school students: The use of abstract data types. In ACM SIGCSE Bulletin. Vol. 27. ACM, 331–335.Google Scholar
Sherin, B., diSessa, A. A. and Hammer, D. 1993. Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments 3, 2, 91–118.CrossRefGoogle Scholar
Smith, M. 2016. Computer science for all. URL: https://www.whitehouse.gov/blog/2016/01/30/computer-science-all.Google Scholar
Sterling, L. and Shapiro, E. Y. 1994. The Art of Prolog: Advanced Programming Techniques. MIT press, Cambridge.Google Scholar
Stutterheim, J., Swierstra, W. and Swierstra, D. 2013. Forty hours of declarative programming: Teaching Prolog at the junior college Utrecht. arXiv:1301.5077.Google Scholar
Vilner, T., Zur, E. and Gal-Ezer, J. 2007. Fundamental concepts of CS1: Procedural vs. object oriented paradigm– a case study. In ACM SIGCSE Bulletin. Vol. 39. ACM, 171–175.Google Scholar
Voogt, J., Fisser, P., Good, J., Mishra, P. and Yadav, A. 2015. Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies 20, 4, 715–728.CrossRefGoogle Scholar
Wing, J. 2011. Research notebook: Computational thinking– what and why? The Link Magazine, Spring. Carnegie Mellon University, Pittsburgh, PA, USA. URL: http://link.cs.cmu.edu/article.php?a=600.Google Scholar
Zhang, Y. 2015. Lecture notes for teaching computer science using asp. URL: http://www.myweb.ttu.edu/yuazhang/TexPREP/ASP.pdf.Google Scholar