Research Article | Open Access | 10.31586/InformationProcesses.0203.01

E-learning Tool for Visualization of Shortest Paths Algorithms

  • Daniela Borissova1,* and Ivan Mustakerov1
    Department. of Information Processes & Decision Support Systems, IICT – Bulgarian Academy of Sciences, Sofia, Bulgaria


Visualizations of algorithms contribute to improving computer science education. The process of teaching and learning of algorithms is often complex and hard to understand problem. Visualization is a useful technique for learning in any computer science course. In this paper an e-learning tool for shortest paths algorithms visualization is described. The developed e-learning tool allows creating, editing and saving graph structure and visualizes the algorithm steps execution. It is intended to be used as a supplement to face-to-face instruction or as a stand-alone application. The conceptual applicability of the described e-learning tool is illustrated by implementation of Dijkstra algorithm. The preliminary test results provide evidence of the usability of the e-learning tool and its potential to support students? development of efficient mental models regarding shortest paths algorithms. This e- learning tool is intended to integrate different algorithms for shortest path determination.

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Ardito C., De Marsico, M., Lanzilotti, R., Levialdi, S., Roselli, T., Rossano,V. & Tersigni, M. Usability of E-Learning Tools, Availableonlineat Utili/p80-ardito.pdf
Ahuja R. K., Magnanti, T. L. & Orlin, J. B. (1993). Network Flows: Theory, Algorithms and Applications. Englewood Cliffs, NJ: Prentice Hall.
Akoumianakis D. (2011). Learning as 'Knowing': Towards Retaining and Visualizing Use in Virtual Settings. Educational Technology & Society, 14 (3), 55-68.
Ozyurt O., Ozyurt H., Baki A., Guven B. & Karal H. (2012). Evaluation of an adaptive and intelligent educational hypermedia for enhanced individual learning of mathematics: A qualitative study. Expert Systems with Applications, 39(15), 12092-12104.
[Nguyen V.A. & Yamamoto A. (2012). Learning from graph data by putting graphs on the lattice. Expert Systems with Applications, 39(12), 11172-11182.
[Karavirta V. (2007). Integrating Algorithm Visualization Systems. Electronic Notes in Theoretical Computer Science, 178(4), pp. 79-87.
Seppala O. & Karavirta V. (2009). Work in Progress: Automatic Generation of Algorithm Animations for Lecture Slides. Electronic Notes in Theoretical Computer Science, 224, 97-103.
Hundhausen C. D., Douglas S. A. & Stasko J. T. (2002). A meta- study of algorithm visualization effectiveness. Journal of Visual Languages and Computing, 13(3), 259-290.
Fouh E., Akbar M. & Shaffer C. A. (2012). The Role of Visualization in Computer Science Education. Computers in the Schools, 29(1-2), 95-117.
Roles J.A. & ElAarag H. (2013). A Smoothest Path algorithm and its visualization tool. Southeastcon, In Proc. of IEEE, DOI: 10.1109/SECON.2013.6567453.
Paramythis A., Loidl S., M?hlbacher J. R., & Sonntag M. (2005). A Framework for Uniformly Visualizing and Interacting with Algorithms. In Montgomerie, T.C., & Parker E-Learning, J.R. (Eds.), In Proc. IASTED Conf. on Education and Technology (ICET 2005), Calgary, Alberta, Canada, 2-6 July 2005, pp. 28- 33.
Nussbaumer A., Dahrendorf D., Schmitz H.-Ch., Kravcik M., Berthold M. & Albert D. (2014). Recommender and Guidance Strategies for Creating Personal Mashup Learning Environments. Computer Science and Information Systems, 11(1), 321-342.
Gillet D., Law E.L.C. & Chatterjee A. (2010). Personal Learning Environments in a Global Higher. Engineering Education Web 2.0 Realm. In Proc. of IEEE EDUCON 2010 Conference. pp. 897- 906.
Wilson S., Liber P.O., Johnson M., Beauvoir P. & Sharples P. (2007). Personal Learning Environments: Challenging the Dominant Design of Educational Systems. Journal of e-Learning and Knowledge Society, 3(2), pp. 27-28.
Godsk M. (2014). Improving Learning in a Traditional, Large- Scale Science Module with a Simple and Efficient Learning Design. European Journal of Open, Distance and e-Learning, 17(2), 142-158.
Guliashki V., Genova K. & Kirilov L. (2013). The Decision Support System WebOptim in an E-Learning Context. In: Proc. of International Conference "Automatics and Informatics'2013", 2013, pp. I-117-I-120.
Dijkstra, E. W. (1959). A Note on Two Problems in Connection with Graphs. Numeriche Mathematik, 1, 269-271.
Dial R., Glover F., Karney D. & Klingman D. (1979). A Computational Analysis of Alternative Algorithms and Labeling Techniques for Finding Shortest Path Trees. Networks, 9(3), 215- 248.
Glover F., Klingman D. & Phillips N. (1985). A New Polynomially Bounded Shortest Paths Algorithm. Operations Research, 33(1), (pp. 65-73).
Gallo G. & Pallottino S. (1988). Shortest Paths Algorithms. Annals of Operations Research, 13(1), 73-79.
Hung M. H. & Dovoky J. J. (1988). A Computational Study of Efficient Shortest Path Algorithms.Computers & Operations Research, 15(6), 567-576.
Mondou J-F, Crainic T. G. & Nguyen S. (1991). Shortest Path Algorithms: A Computational Study with the C Programming Language. Computers & Operations Research, 18(8), 767-786.
Cherkassky B. V., Goldberg A. V. & Radzik T. (1996). Shortest Paths Algorithms: Theory and Experimental Evaluation. Mathematical Programming, Ser. A73(2), 129-174.
Silveira D.S., Melo V.A. & Boaventura Netto. P. O. (2009). AlgoDeGrafos: An Application to Assist in Course Lectures on Graph Theory. CLEI Electronic Journal, 12(1), paper 2.
Goldberg A. V. & Radzik T. (1993). A Heuristic Improvement of the Bellman-Ford Algorithm. Applied Mathematics Letter, 6(3), 3-6.
S?nchez-Torrubia M.G., Torres-Blanc C. & L?pez-Mart?nez M. A. (2009). PathFinder: A Visualization eMathTeacher for Actively Learning Dijkstra?s Algorithm. Electronic Notes in Theoretical Computer Science, 224, pp. 151-158.
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