Introducing an Educational Tool for Learning Branch & Bound Strategy
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Abstract”According to our informal survey, Branch & Bound strategy is considerably difficult to learn compared to other strategies. This strategy consists of several complex algorithmic steps such as Reduced Cost Matrix (RCM) calculation and Breadth First Search. Thus, to help students understanding this strategy, AP-BB, an educational tool for learning Branch & Bound is developed. This tool includes four modules which are Brute Force solving visualization, Branch & Bound solving visualization, RCM calculator, and case-based performance comparison. These modules are expected to enhance student's understanding about Branch & Bound strategy and its characteristics. Furthermore, our work incorporates TSP as its case study and Brute Force strategy as a baseline to provide a concrete impact of Branch & Bound strategy. According to our qualitative evaluation, AP-BB and all of its features fulfil student necessities for learning Branch & Bound strategy.
Keywords” Educational Tool; Branch & Bound; Algorithm Strategy; Algorithm Visualization
AlgoViz.org : The Algorithm Visualization Portal. (n.d.). Retrieved 12 7, 2015, from http://algoviz.org/
Areias, C., & Mendes, A. (2007). A tool to help students to develop programming skills. The 2007 international conference on Computer systems and technologies. Bulgaria: ACM.
Bentrad, S., & Meslati, D. (2011). Visual Programming and Program Visualization- Toward an Ideal Visual Software Engineering System -. ACEEE International Journal on Information Technology , 1 (3), 43-49.
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 (pp. 16-20). Charlotte: ACM.
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 (pp. 176-180). St. Louis: ACM.
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), 21-30.
Cisar, S. M., Pinter, R., Radosav, D., & Cisar, P. (2010). Software visualization: The educational tool to enhance student learning. The 33rd International Convention MIPRO (pp. 990-994). Opatija: IEEE.
Cooper, S., Dann, W., & Pausch, R. (2000). Alice: a 3-D tool for introductory programming concepts. Journal of Computing in Small Colleges , 15 (5), 67-71.
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.
Elvina, & Karnalim, O. (in press). Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner. ComTech: Computer, Mathematics and Engineering Applications , 8 (1).
Gestwicki, P., & Jayaraman, B. (2002). Interactive Visualization of Java Programs. Symposia on Human Centric Computing Languages and Environments (pp. 226-235). Washington: ACM.
Guo, P. J. (2013). Online python tutor: embeddable web-based program visualization for cs education. The 44th ACM technical symposium on Computer science education (pp. 579-584). Denver: ACM.
Halim, S. (n.d.). VisuAlgo. Retrieved 5 12, 2015, from http://visualgo.net/
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.
Joint Task Force on Computing Curricula, Association for Computing Machinery (ACM) and IEEE Computer Society. (2013). Curriculum Guideliness for Undergraduate Degree Programs in Computer Science. New York: ACM.
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: Universitas Islam Indonesia.
Learn programming with CeeBot4. (2008, 9 5). Retrieved 11 5, 2016, from http://www.ceebot.com/ceebot/4/4-e.php
Levitin, A. (2012). Introduction to The Design and Analysis of Algorithms. Pearson.
Naps, T. L., RöíŸling, 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: ACM.
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, 15-32.
Shaffer, C. A., Cooper, M. L., Alon, A. J., Akbar, M., Stewart, M., Ponce, S., et al. (2010). Algorithm Visualization: The State of the Field. ACM Transactions on Computing Education (TOCE) , 10 (3), 1-22.
Velázquez-Iturbide, J. Á., & 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 (pp. 119-123). New York: ACM.
Watts, T. (2004). The SFC editor a graphical tool for algorithm development. Journal of Computing Sciences in Colleges , 20 (2), 73-85.
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