Optimum Allocation of Graduation Projects: Survey and Proposed Solution
DOI:
https://doi.org/10.29304/jqcm.2021.13.1.774Keywords:
student-project allocation survey, traditional allocation problems, Optimum approach for project allocationAbstract
The final year project is an important part of obtaining a Graduation certificate in scientific and engineering universities. To ensure high quality in the completion of graduation projects, the project allocation process must be efficient and fair among students. Optimum allocation of graduation projects ensures a great experience for students and exceptional learning of new technologies from that projects, which may contribute to expanding students' future horizons. In this work, we review a wide range of research for the allocation project strategies that are used in various universities, in addition to the factors that have an effective impact on the selection of graduation projects by students. We also highlight the problems that these strategies tried to solve, in addition to those that they failed to solve. Finally, we present a modern idea to overcome too many problems that were presented in the literature discussion where the proposed system is based mainly on the student development level during the study stage.
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References
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[2] D. F. Manlove and G. O’Malley, “Student-Project Allocation with preferences over Projects,” J. Discret. Algorithms, vol. 6, no. 4, pp. 553–560, 2008, doi: 10.1016/j.jda.2008.07.003.
[3] A. A. Anwar and A. S. Bahaj, “Student project allocation using integer programming,” IEEE Trans. Educ., vol. 46, no. 3, pp. 359–367, 2003, doi: 10.1109/TE.2003.811038.
[4] P. R. Harper, V. De Senna, I. T. Vieira, and A. K. Shahani, “A genetic algorithm for the project assignment problem,” Comput. Oper. Res., vol. 32, no. 5, pp. 1255–1265, 2005, doi: 10.1016/j.cor.2003.11.003.
[5] D. Kazakov, “Co-ordination of student-project allocation,” Manuscript, Univ. York, Dep. Comput. Sci., 2002.
[6] M. Thorn, “A constraint programming approach to the student-project allocation problem,” BSc Honours Proj. report, Univ. York, Dep. Comput. Sci., 2003.
[7] D. J. Abraham, R. W. Irving, and D. F. Manlove, “Two algorithms for the Student-Project Allocation problem,” J. Discret. Algorithms, vol. 5, no. 1, pp. 73–90, 2007, doi: 10.1016/j.jda.2006.03.006.
[8] K. Iwama, S. Miyazaki, and H. Yanagisawa, “Improved approximation bounds for the student-project allocation problem with preferences over projects,” J. Discret. Algorithms, vol. 13, pp. 59–66, 2012.
[9] F. Cooper and D. Manlove, A 32-Approximation algorithm for the student-project allocation problem, vol. 103. 2018.
[10] L. Pan, S. C. K. Chu, G. Han, and J. Z. Huang, “Multi-criteria Student Project Allocation : A Case Study of Goal Programming Formulation with DSS Implementation,” Eighth Int. Symp. Oper. Res. Its Appl. Zhangjiajie, China, pp. 75–82, 2009.
[11] M. El-Sherbiny and Y. Ibrahim, “An artificial immune algorithm with alternative mutation methods: applied to the student project assignment problem,” Int. Conf. Innov. …, vol. 36, no. Iciim, pp. 149–158, 2012, [Online]. Available: http://www.ipcsit.com/vol36/029-ICIIM2012-M0082.pdf.
[12] A. Kwanashie, R. W. Irving, D. F. Manlove, and C. T. S. Sng, “Profile-based optimal Matchings in the Student/Project allocation problem,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8986, pp. 213–225, 2015, doi: 10.1007/978-3-319-19315-1_19.
[13] H. O. Salami and E. Y. Mamman, “A genetic algorithm for allocating project supervisors to students,” Int. J. Intell. Syst. Appl., vol. 8, no. 10, pp. 51–59, 2016, doi: 10.5815/ijisa.2016.10.06.
[14] R. A. Knight and N. Botting, “Organising undergraduate research projects: student-led and academic-led models,” J. Appl. Res. High. Educ., vol. 8, no. 4, pp. 455–468, 2016, doi: 10.1108/JARHE-07-2015-0054.
[15] H. Chang, “Turning an undergraduate class into a professional research community,” Teach. High. Educ., vol. 10, no. 3, pp. 387–394, Jul. 2005, doi: 10.1080/13562510500122339.
[16] P. Plan, “A Web-based Project Allocation System,” 2016.
[17] R. Calvo-Serrano, G. Guillén-Gosálbez, S. Kohn, and A. Masters, “Mathematical programming approach for optimally allocating students’ projects to academics in large cohorts,” Educ. Chem. Eng., vol. 20, pp. 11–21, 2017, doi: 10.1016/j.ece.2017.06.002.
[18] S. Modi, N. M. Shagari, and B. Wadata, “Implementation of Stable Marriage Algorithm in Student Project Allocation,” Asian J. Res. Comput. Sci., vol. 1, no. 4, pp. 1–9, 2018, doi: 10.9734/ajrcos/2018/v1i424749.
[19] V. Sanchez-Anguix, R. Chalumuri, R. Aydoğan, and V. Julian, “A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance,” Appl. Soft Comput. J., vol. 76, pp. 1–15, 2019, doi: 10.1016/j.asoc.2018.11.049.
[20] S. Hussain, K. A. A. Gamage, M. H. Sagor, F. Tariq, L. Ma, and M. A. Imran, “A systematic review of project allocation methods in undergraduate transnational engineering education,” Educ. Sci., vol. 9, no. 4, 2019, doi: 10.3390/educsci9040258.
[21] V. Paunovic, S. Tomic, I. Bosnic, and M. Zagar, “Fuzzy approach to Student-Project Allocation (SPA) problem,” IEEE Access, vol. 7, pp. 136046–136061, 2019, doi: 10.1109/ACCESS.2019.2941730.
[22] P. Kenekayoro, P. Mebine, and B. G. Zipamone, “Population Based Techniques for Solving the Student Project Allocation Problem,” Int. J. Appl. Metaheuristic Comput., vol. 11, no. 2, pp. 192–207, 2020, doi: 10.4018/ijamc.2020040110.
[23] O. Zughoul et al., “Comprehensive Insights into the Criteria of Student Performance in Various Educational Domains,” IEEE Access, vol. 6, no. 1, pp. 73245–73264, 2018, doi: 10.1109/ACCESS.2018.2881282.
[24] J. R. Kider, “Some Properties of Algebra Fuzzy Metric Space 2 . Basic Properties of Algebra fuzzy metric space,” vol. 12, no. 2, pp. 43–56, 2020.
[25] A. Famili, W.-M. Shen, R. Weber, and E. Simoudis, “Data preprocessing and intelligent data analysis,” Intell. data Anal., vol. 1, no. 1, pp. 3–23, 1997.
[26] S. A. Alasadi and W. S. Bhaya, “Review of data preprocessing techniques in data mining,” J. Eng. Appl. Sci., vol. 12, no. 16, pp. 4102–4107, 2017.
[27] A. S. Albahri, R. A. Hamid, O. S. Albahri, and A. A. Zaidan, “Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods,” Artif. Intell. Med., vol. 111, no. October 2020, p. 101983, 2021, doi: 10.1016/j.artmed.2020.101983.
[28] H. T. Phan, V. C. Tran, N. T. Nguyen, and D. Hwang, “Decision-making support method based on sentiment analysis of objects and binary decision tree mining,” in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2019, pp. 753–767.
[29] O. A. Raheem and E. S. Alomari, “An Adaptive Intrusion Detection System by using Decision Tree,” J. AL-Qadisiyah Comput. Sci. Math. Vol, vol. 10, no. 2, 2018.
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Published
2021-03-13
How to Cite
Gani, M. A., tayyeh, D. K., & Hamid, D. R. A. (2021). Optimum Allocation of Graduation Projects: Survey and Proposed Solution. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(1), Comp Page 58 – 66. https://doi.org/10.29304/jqcm.2021.13.1.774
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Computer Articles
