The Use of Python Tutor on Programming Laboratory Session: Student Perspectives

Oscar Karnalim; Mewati Ayub

doi:10.22219/kinetik.v2i4.442

Issue

Vol 2, No 4, November-2017

Issue Published : Sep 29, 2017

The Use of Python Tutor on Programming Laboratory Session: Student Perspectives

https://doi.org/10.22219/kinetik.v2i4.442

Oscar Karnalim

Universitas Kristen Maranatha

http://orcid.org/0000-0003-4930-6249

Mewati Ayub

Universitas Kristen Maranatha

Corresponding Author(s) : Oscar Karnalim

oscar.karnalim@it.maranatha.edu

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol 2, No 4, November-2017
Article Published : Oct 30, 2017

Abstract

Based on the fact that the impact of educational tools can only be accurately measured through student-centered evaluation, this paper proposes a long-term in-class evaluation for Python Tutor, a program visualization tool developed by Guo. The evaluation involves 53 students from 4 Basic Data Structure classes, which were held in the even semester of 2016/2017 academic year. It is conducted based on questionnaire survey asked to the students after they have used Python Tutor in their half of programming laboratory sessions. In general, there are three findings from this work. Firstly, Python Tutor helps students to complete programming laboratory tasks, specifically for Basic Data Structure material. Secondly, Python Tutor helps students to understand general programming aspects which are execution flow, variable content change, method invocation sequence, object reference, syntax error, and logic error. Finally, based on student perspectives, Python Tutor is a helpful tool positively affecting the students.

Keywords

Questionnaire Survey Program Visualization Educational Tool Laboratory Session Programming

Karnalim, O., & Ayub, M. (2017). The Use of Python Tutor on Programming Laboratory Session: Student Perspectives. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2(4), 327 - 336. https://doi.org/10.22219/kinetik.v2i4.442

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References

M. McCracken et al., “A Multi-National, Multi-Institutional Study of Assessment of Programming Skill of First-year CS Students,” ACM SIGSCE Bulletin, Vol. 33, No. 4, 2001.
R. Lister et al., “A Multi-National Study of Reading and Tracing Skills in Novice Programmers,” ACM SIGSCE Bulletin, Vol. 36, No. 4, 2004.
T. Chen et al., “Students Designing Software: A Multi-National, Multi-Institutional Study,” Informatics Education, Vol. 4, No. 1, 2005.
S. M. Cisar, R. Pinter, and D. Radosav, “Effectiveness of Program Visualization in Learning Java: a Case Study with Jeliot 3,” International Journal of Computers, Communications & Control, Vol. 6, No. 4, 2011.
J. Urquiza-Fuentes and J. Á. Velázquez-Iturbide, “A Survey of Successful Evaluations of Program Visualization and Algorithm Animation Systems,” ACM Transactions on Computing Education (TOCE) - Special Issue on the 5th Program Visualization Workshop, Vol. 9, No. 2, 2009.
S. Bentrad and D. Meslati, “Visual Programming and Program Visualization- Toward an Ideal Visual Software Engineering System,” ACEEE International Journal on Information Technology, Vol. 1, No. 3, 2011.
P. J. Guo, “Online Python Tutor: Embeddable Web-Based Program Visualization For CS Education,” in The 44th ACM technical symposium on Computer Science Education, 2013.
O. Karnalim, “Detecting Source Code Plagiarism on Introductory Programming Course Assignments Using a Bytecode Approach,” in The 10th International Conference on Information & Communication Technology and Systems (ICTS), 2016.
H. Toba, E. A. Wijaya, M. C. Wijanto, and O. Karnalim, “Enhanced Unsupervised Person Name Disambiguation to Support Alumni Tracer Study,” Global Journal of Engineering Education, Vol. 19, No. 1, 2017.
M. Ayub and O. Karnalim, “Predicting Outcomes in Introductory Programming Using J48 Classification,” World Transactions on Engineering and Technology Education (WTE&TE), Vol. 15, No. 2, 2017.
E. Kaila, T. Rajala, M. J. Laakso, and T. Salakoski, “Effects of Course-Long Use of a Program Visualization Tool,” in Australasian Computing Education Conference, 2010.
E and O. Karnalim, “Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner,” ComTech: Computer, Mathematics and Engineering Applications, Vol. 8, No. 1, 2017.
O. Karnalim and Elvina, “Interfacing Complexitor: An Empirical-based Educational Tool for Learning Time Complexity,” Journal of IRD (Informatics Research and Development), Vol. 1, No. 1, 2017.
E. T. Y. Ling, “Teaching Algorithms with Web-based Technologies,” Department of Computer Science, School of Computing, National University of Singapore, 2014.
S. Halim, Z. C. Koh, V. B. H. Loh, and F. Halim, “Learning Algorithms with Unified and Interactive Web-Based Visualization,” Olympiads in Informatics, Vol. 6, Pp. 53–68, 2012.
L. Christiawan and O. Karnalim, “AP-ASD1: An Indonesian Desktop-based Educational Tool for Basic Data Structures,” Information Technology and Information System, Vol. 2, No. 1, 2016.
F. C. Jonathan, O. Karnalim, and M. Ayub, “Extending The Effectiveness of Algorithm Visualization with Performance Comparison through Evaluation-integrated Development,” in National Seminar on Information tachnology Appication, 2016.
S. Zumaytis and O. Karnalim, “Introducing An Educational Tool for Learning Branch & Bound Strategy,” Journal of Information Systems Engineering and Business Intelligence, Vol. 3, No. 1, 2017.
A. Moreno, N. Myller, E. Sutinen, and M. Ben-Ari, “Visualizing programs with Jeliot 3,” in The Working Conference on Advanced Visual Interfaces, Galipoli, 2004.
P. Gestwicki and B. Jayaraman, “Interactive Visualization of Java Programs,” in Symposia on Human Centric Computing Languages and Environments, 2002.
T. Rajala, M.-J. Laakso, M. Kaila, and T. Salakoski, “VILLE - A Language Independent Program Visualization Tool,” in The 7th Baltic Sea Conference on Computing Education Research, 2007.

References

M. McCracken et al., “A Multi-National, Multi-Institutional Study of Assessment of Programming Skill of First-year CS Students,” ACM SIGSCE Bulletin, Vol. 33, No. 4, 2001.

R. Lister et al., “A Multi-National Study of Reading and Tracing Skills in Novice Programmers,” ACM SIGSCE Bulletin, Vol. 36, No. 4, 2004.

T. Chen et al., “Students Designing Software: A Multi-National, Multi-Institutional Study,” Informatics Education, Vol. 4, No. 1, 2005.

S. M. Cisar, R. Pinter, and D. Radosav, “Effectiveness of Program Visualization in Learning Java: a Case Study with Jeliot 3,” International Journal of Computers, Communications & Control, Vol. 6, No. 4, 2011.

J. Urquiza-Fuentes and J. Á. Velázquez-Iturbide, “A Survey of Successful Evaluations of Program Visualization and Algorithm Animation Systems,” ACM Transactions on Computing Education (TOCE) - Special Issue on the 5th Program Visualization Workshop, Vol. 9, No. 2, 2009.

S. Bentrad and D. Meslati, “Visual Programming and Program Visualization- Toward an Ideal Visual Software Engineering System,” ACEEE International Journal on Information Technology, Vol. 1, No. 3, 2011.

P. J. Guo, “Online Python Tutor: Embeddable Web-Based Program Visualization For CS Education,” in The 44th ACM technical symposium on Computer Science Education, 2013.

O. Karnalim, “Detecting Source Code Plagiarism on Introductory Programming Course Assignments Using a Bytecode Approach,” in The 10th International Conference on Information & Communication Technology and Systems (ICTS), 2016.

H. Toba, E. A. Wijaya, M. C. Wijanto, and O. Karnalim, “Enhanced Unsupervised Person Name Disambiguation to Support Alumni Tracer Study,” Global Journal of Engineering Education, Vol. 19, No. 1, 2017.

M. Ayub and O. Karnalim, “Predicting Outcomes in Introductory Programming Using J48 Classification,” World Transactions on Engineering and Technology Education (WTE&TE), Vol. 15, No. 2, 2017.

E. Kaila, T. Rajala, M. J. Laakso, and T. Salakoski, “Effects of Course-Long Use of a Program Visualization Tool,” in Australasian Computing Education Conference, 2010.

E and O. Karnalim, “Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner,” ComTech: Computer, Mathematics and Engineering Applications, Vol. 8, No. 1, 2017.

O. Karnalim and Elvina, “Interfacing Complexitor: An Empirical-based Educational Tool for Learning Time Complexity,” Journal of IRD (Informatics Research and Development), Vol. 1, No. 1, 2017.

E. T. Y. Ling, “Teaching Algorithms with Web-based Technologies,” Department of Computer Science, School of Computing, National University of Singapore, 2014.

S. Halim, Z. C. Koh, V. B. H. Loh, and F. Halim, “Learning Algorithms with Unified and Interactive Web-Based Visualization,” Olympiads in Informatics, Vol. 6, Pp. 53–68, 2012.

L. Christiawan and O. Karnalim, “AP-ASD1: An Indonesian Desktop-based Educational Tool for Basic Data Structures,” Information Technology and Information System, Vol. 2, No. 1, 2016.

F. C. Jonathan, O. Karnalim, and M. Ayub, “Extending The Effectiveness of Algorithm Visualization with Performance Comparison through Evaluation-integrated Development,” in National Seminar on Information tachnology Appication, 2016.

S. Zumaytis and O. Karnalim, “Introducing An Educational Tool for Learning Branch & Bound Strategy,” Journal of Information Systems Engineering and Business Intelligence, Vol. 3, No. 1, 2017.

A. Moreno, N. Myller, E. Sutinen, and M. Ben-Ari, “Visualizing programs with Jeliot 3,” in The Working Conference on Advanced Visual Interfaces, Galipoli, 2004.

P. Gestwicki and B. Jayaraman, “Interactive Visualization of Java Programs,” in Symposia on Human Centric Computing Languages and Environments, 2002.

T. Rajala, M.-J. Laakso, M. Kaila, and T. Salakoski, “VILLE - A Language Independent Program Visualization Tool,” in The 7th Baltic Sea Conference on Computing Education Research, 2007.

Issue

Vol 2, No 4, November-2017

The Use of Python Tutor on Programming Laboratory Session: Student Perspectives

Corresponding Author(s) : Oscar Karnalim

Abstract

Keywords

Download Citation

References

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