Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner


  • Elvina Elvina Maranatha Christian University
  • Oscar Karnalim Maranatha Christian University



Complexitor, educational tool, learning algorithm, time complexity


Based on the informal survey, learning algorithm time complexity in a theoretical manner can be rather difficult to understand. Therefore, this research proposed Complexitor, an educational tool for learning algorithm time complexity in a practical manner. Students could learn how to determine algorithm time complexity through the actual execution of algorithm implementation. They were only required to provide algorithm implementation (i.e. source code written on a particular
programming language) and test cases to learn time complexity. After input was given, Complexitor generated execution sequence based on test cases and determine its time complexity through Pearson correlation. An algorithm time complexity with the highest correlation value toward execution sequence was assigned as its result. Based on the evaluation, it can be concluded this mechanism is quite effective for determining time complexity as long as the distribution of given input set is balanced.

Plum Analytics

Author Biographies

Elvina Elvina, Maranatha Christian University

Faculty of Information Technology

Oscar Karnalim, Maranatha Christian University

Faculty of Information Technology


Areias, C., & Mendes, A. (2007). A tool to help students to develop programming skills. The 2007 international conference on Computer systems and technologies. Bulgaria.

Buck, D., & Stucki, D. J. (2001). JKarelRobot: a case study in supporting levels of cognitive development in the computer science curriculum. The thirty-second SIGCSE technical symposium on Computer Science Education. Charlotte.

Carlisle, M. C., Wilson, T. A., Humphries, J. W., & Hadfield, S. M. (2005). RAPTOR: a visual programming environment for teaching algorithmic problem solving. The 36th SIGCSE technical symposium on Computer science education. St. Louis.

Christiawan, L., & Karnalim, O. (2016). AP-ASD1 An Indonesian Desktop-based Educational Tool for Basic Data Structures. Jurnal Teknik Informatika dan Sistem Informasi (JuTISI) , 2 (1).

Cisar, S. M., Pinter, R., Radosav, D., & Cisar, P. (2010). Software visualization: The educational tool to enhance student learning. The 33rd International Convention MIPRO.

Cooper, S., Dann, W., & Pausch, R. (2000). Alice: a 3-D tool for introductory programming concepts. Journal of Computing in Small Colleges , 15 (5).

Debdi, O., Paredes-Velasco, M., & Velázquez-Iturbide, J. Á. (2015). GreedExCol, A CSCL tool for experimenting with greedy algorithms. Computer Applications in Engineering Education , 23 (5), 790-804.

Guo, P. J. (2013). Online python tutor: embeddable web-based program visualization for cs education. The 44th ACM technical symposium on Computer science education. Denver.

Halim, S. (n.d.). VisuAlgo. Retrieved 5 12, 2015, from

Halim, S., Koh, Z. C., Loh, V. B., & Halim, F. (2012). Learning Algorithms with Unified and Interactive Web-Based Visualization. Olympiads in Informatics , 6, 53-68.

Jonathan, F. C., Karnalim, O., & Ayub, M. (2016). ExtendingThe Effectiveness of Algorithm Visualization with Performance Comparison through Evaluation-integrated Development. Seminar Nasional Aplikasi Teknologi Informasi. Yogyakarta.

Learn programming with CeeBot4. (2008, 9 5). Retrieved 11 5, 2016, from

Ling, E. (2014). Teaching Algorithms with Web-based Technologies. Singapore: B.Comp. Dissertation, Department of Computer Science, School of Computing, National University of Singapore.

Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., et al. (2003). Exploring the role of visualization and engagement in computer science education. ITiCSE-WGR '02 Working group reports from ITiCSE on Innovation and technology in computer science education. New York.

Pearson, K. (1895). Notes on regression and inheritance in the case of two parents. The Royal Society of London.

Radosevic, D., Orehovacki, T., & Lovrencic, A. (2009). Verificator: Educational Tool for Learning Programming. Informatics in Education , 8 (2).

Rajala, T., Laakso, M.-J., Kaila, E., & Salakoski, T. (2008). Effectiveness of Program Visualization : A case study with the ViLLE Tool. Journal of Information Technology Education : Innovation in Practice , 7.

Velázquez-Iturbide, J., & Pérez-Carrasco, A. (2009). Active learning of greedy algorithms by means of interactive experimentation. ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education. New York.

Watts, T. (2004). The SFC editor a graphical tool for algorithm development. Journal of Computing Sciences in Colleges , 20 (2).






Abstract 484  .
PDF downloaded 306  .