research-article

Exploring how Students Perform in a Theory of Computation Course using Final Exam and Homework Assignments Data

Authors:

Christiane Frede,

Maria KnobelsdorfAuthors Info & Claims

ICER '18: Proceedings of the 2018 ACM Conference on International Computing Education Research

Pages 241 - 249

https://doi.org/10.1145/3230977.3230996

Published: 08 August 2018 Publication History

Abstract

Computer Science (CS) students continue to struggle in Theory of Computation (ToC) courses and empirical research continues to be required that is investigating this situation. In order to gain a differentiated picture and to learn more about potential challenges that CS students face when studying ToC, we used an exploratory data analysis to examine student performance in an undergraduate ToC course. In particular, we used final exam results and homework assignment scores to explore our research field and to develop hypotheses about it comparable to qualitative approaches. Our results indicate that despite their finale grade all students were particularly challenged by formal proof assignments covering ToC concepts. These results bolster the evidence of a need for pedagogical approaches in ToC that address all students and are particularly focused on teaching them formal proof techniques within this domain.

References

[1]

M. Armoni. 2009. Reduction in CS: A (mostly) quantitative analysis of reductive solutions to algorithmic problems. Journal on Educational Resources in Computing (JERIC) 8.4: 11.

Digital Library

[2]

J.T. Behrens and C.-H. Yu. 2003. Exploratory Data Analysis. Handbook of Psychology. One:2: 33--64.

[3]

D. Berque, D. Johnson, and L. Jovanovic. 2001. Teaching theory of computation using pen-based computers and an electronic whiteboard. ACM SIGCSE Bulletin, 33, 3, 169- 172.

Digital Library

[4]

A. Bryman. 2015. Social Research Methods. Oxford university press.

[5]

C. Chatfield. 1986. Exploratory data analysis. European journal of operational research 23.1: 5--13.

[6]

S. Chesñevar, M. González, and A. Maguitman, A. 2004. Didactic strategies for promoting significant learning in formal languages and automata theory. In Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education. ACM: 7--11.

Digital Library

[7]

D, Chudá. 2007. Visualization in Education of Theoretical Computer Science. In Proceedings of the 2007 international conference on Computer systems and technologies. CompSysTech '07. 84.

Digital Library

[8]

T. H. Cormen, C.E. Leiserson, R.L. Rivest and C. Stein. 2009. Introduction to Algorithms. MIT press.

Digital Library

[9]

P. Crescenzi, E. Enström, and V. Kann. 2013. From theory to practice: NP-completeness for every CS student. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education. ITiCSE '13. ACM: 16--21.

Digital Library

[10]

V. Devedzic, J. Debenham, and D. Popvic. 2000. Teaching Formal Languages by an Intelligent Tutoring System. Educational Technology and Society 3, 2, ISSN 1436--4522.

[11]

E. Enström. 2014. On difficult topics in theoretical computer science education. Diss. KTH Royal Institute of Technology.

[12]

C. García-Osorio, I. Mediavilla-Sáiz, J Jimeno-Visitación, and N. García-Pedrajas. 2008. Teaching push-down automata and turing machines. ACM SIGCSE Bulletin, 40, 3.

Digital Library

[13]

H. Habiballa, T. Kmet. 2004. Theoretical branches in teaching computer science. International Journal of Mathematical Education in Science and Technology. 35, 6: 829--841.

[14]

W. Hämäläinen. 2004. Problem-based learning of theoretical computer science. In Proceedings of the 34th ASEE/IEEE Frontiers in Education Conference. S1H/1 -S1H/6 Vol. 3.

[15]

M. Hielscher, C. Wagenknecht. 2006. AtoCC: learning environment for teaching theory of automata and formal languages. In Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education. ITICSE '06. ACM, 306--306.

Digital Library

[16]

J. E. Hopcroft. 1979. Introduction to Automata Theory, Languages, and Computation, Addison-Wesley Publishing Company.

Digital Library

[17]

IEEE Computer Society and ACM: 2013. Computer Science Curricula.

[18]

L. Korte, S. Anderson, H. Pain and J. Good. 2007 Learning by game-building: a novel approach to theoretical computer science education. ITiCSE '07 Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education, pages 53--57.

Digital Library

[19]

M. Knobelsdorf, C. Frede. 2016. Analyzing Student Practices in Theory of Computation in Light of Distributed Cognition Theory. In Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER '16). ACM, New York, NY, USA, 73--81.

Digital Library

[20]

P. Mayring. 2000. Qualitative Content Analysis. Forum: Qualitative Social Research {Online Journal}, 1, 2, Art. 20.

[21]

S. Morgenthaler. 2009. Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics 1.1: 33--44.

Digital Library

[22]

M. Parker and C. Lewis. 2014. What makes big-O analysis difficult: understanding how students understand runtime analysis. Journal of Computing Sciences in Colleges 29.4. 164--174.

Digital Library

[23]

N. Pillay. 2010. Learning difficulties experienced by students in a course on formal languages and automata theory. SIGCSE Bulletin 41.4: 48--52.

Digital Library

[24]

S. H. Rodger, B. Bressler, T. Finley and S. Reading. 2006. Turning automata theory into a hands-on course. In Proceedings of the 37th SIGCSE technical symposium on Computer science education. SIGCSE '06. ACM. 379--383.

Digital Library

[25]

A. Schäfer, J. Holz, T. Leonhardt, U. Schröder, P. Brauner and Martina Ziefle. 2013. From boring to scoring - a collaborative serious game for learning and practicing mathematical logic for computer science education. Computer Science Education 23: 87 111.

[26]

S. Sigman. 2007. Engaging students in formal language theory and theory of computation. ACM SIGCSE Bulletin. Vol. 39. No. 1. ACM.

Digital Library

[27]

M. Sipser. 2012. Introduction to the Theory of Computation. Cengage Learning.

[28]

J. W. Tukey. 1977. Exploratory data analysis. Vol. 2

[29]

J. W. Tukey, F. Mosteller and D.C. Hoaglin. 2000. Understanding robust and exploratory data analysis. Wiley Classics Library ed New York.

[30]

O. Zukunft. 2016. Empfehlungen für Bachelor- und Masterprogramme im Studienfach Informatik an Hochschulen. Bonn. Gesellschaft für Informatik e.V.

[31]

R. Verma. 2005. A visual and interactive automata theory course emphasizing breadth of automata. In Proceedings of the 10th Annual SIGCSE Conference on innovation and Technology in Computer Science Education. ITiCSE '05. ACM, 325--329.

Digital Library

[32]

M. Wermelinger, A. Dias. 2005. A prolog toolkit for formal languages and automata, ACM SIGCSE Bulletin. 37, 3

Digital Library

[33]

Z. Zingaro. 2008. Another approach for resisting student resistance to formal methods, ACM SIGCSE Bulletin, 40, 4.

Digital Library

Cited By

Cordova J(2023)The Generation of a Large Bank of Randomized Questions in a Discrete Structures CourseJournal of Computing Sciences in Colleges10.5555/3636971.363697239:2(10-18)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.5555/3636971.3636972
Frede CKnobelsdorf M(2020)A differentiated picture of student performance in introductory courses to theory of computationComputer Science Education10.1080/08993408.2020.180994631:3(315-339)Online publication date: 4-Oct-2020
https://doi.org/10.1080/08993408.2020.1809946
Cabatuan M(2019)Dismath: Discrete Math Gamified Educational Tool in Logic with Minimax Algorithm and Pruning2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )10.1109/HNICEM48295.2019.9072894(1-6)Online publication date: Nov-2019
https://doi.org/10.1109/HNICEM48295.2019.9072894

Index Terms

Exploring how Students Perform in a Theory of Computation Course using Final Exam and Homework Assignments Data
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

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cover image ACM Conferences

ICER '18: Proceedings of the 2018 ACM Conference on International Computing Education Research

August 2018

307 pages

ISBN:9781450356282

DOI:10.1145/3230977

General Chairs:
Lauri Malmi
Aalto University, Finland
,
Ari Korhonen
Aalto University, Finland
,
Robert McCartney
University of Connecticut, USA
,
Andrew Petersen
University of Toronto Mississauga, Canada
,
Program Chairs:
Lauri Malmi
Aalto University, Finland
,
Robert McCartney
University of Connecticut, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 August 2018

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ICER '18

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ICER '18: International Computing Education Research Conference

August 13 - 15, 2018

Espoo, Finland

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ICER '18 Paper Acceptance Rate 28 of 125 submissions, 22%;

Overall Acceptance Rate 189 of 803 submissions, 24%

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Cited By

Cordova J(2023)The Generation of a Large Bank of Randomized Questions in a Discrete Structures CourseJournal of Computing Sciences in Colleges10.5555/3636971.363697239:2(10-18)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.5555/3636971.3636972
Frede CKnobelsdorf M(2020)A differentiated picture of student performance in introductory courses to theory of computationComputer Science Education10.1080/08993408.2020.180994631:3(315-339)Online publication date: 4-Oct-2020
https://doi.org/10.1080/08993408.2020.1809946
Cabatuan M(2019)Dismath: Discrete Math Gamified Educational Tool in Logic with Minimax Algorithm and Pruning2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )10.1109/HNICEM48295.2019.9072894(1-6)Online publication date: Nov-2019
https://doi.org/10.1109/HNICEM48295.2019.9072894

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