Abstract
Assessment is one of the most important issues in terms of learning. A new challenge in the field of assessment came in the long periods of learning on online platforms. In this paper we propose a model for generating evaluation tests consisting of questions that have statements, keywords and answers. The genetic algorithm that generates the tests will use the keywords to rank the tests through the fitness function. Once the test that maximizes the number of keywords in the initially given list is generated, it can be used for evaluation by the teacher through various mobile applications, web, etc. The implementation of the algorithm in the paper was done using the Java language. The visual application at the end of the paper highlights the results of the presented algorithm.
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References
Becheru, A., Popescu, E.: Design of a conceptual knowledge extraction framework for a social learning environment based on social network analysis methods. In: Proceedings ICCC 2017, pp. 177–182 (2017)
Alharbi, S., Venkat, I.: A genetic algorithm based approach for solving the minimum dominating set of queens problem. J. Optim. 2017, 1–8 (2017)
Boopathiraj, C., Chellamani, K.: Analysis of test items on difficulty level and discrimination index in the test for research in education. Int. J. Social Sci. Interdiscipl. Res. 2(2), 189–193 (2013)
Yang, B.W., Adam, J.R., Persky, M.: Using Testing as a Learning Tool. Am. J. Pharm. Educ. 83(9), 7274 (2019)
Colorni, A., Dorigo, M., Maniezzo, V.: A Genetic Algorithm To Solve The Timetable Problem (1994)
Popescu, D.A., Bold, N., Domsa, O.: A generator of sequences of hierarchical tests which contain specified keywords. In: 11th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2016. Timisoara, Romania, 12–14 May (2016)
Popescu, D.A., Nijloveanu, D., Bold, N.: Generator of Tests for Learning Check in Case of Courses that Use Learning Blocks. In: Di Mascio, T., Vittorini, P., Gennari, R., De la Prieta, F., RodrÃguez, S., Temperini, M., Silveira, R.A., Popescu, E., Lancia, L. (eds.) Methodologies and Intelligent Systems for Technology Enhanced Learning, 8th International Conference, pp. 239–244. Springer International Publishing, Cham (2019)
Popescu, D.A., Bold, N., Domsa, O.: Generating assessment tests with restrictions using genetic algorithms. In: 12th IEEE International Conference on Control and Automation, ICCA 2016. Kathmandu, Nepal, 1–3 June (2016)
Popescu, E., Stefan, C., Ilie, S., Ivanović, M.: EduNotes – A mobile learning application for collaborative note-taking in lecture settings. In: Chiu, D.K.W., Marenzi, I., Nanni, U., Spaniol, M., Temperini, M. (eds.) Advances in Web-Based Learning – ICWL 2016, pp. 131–140. Springer International Publishing, Cham (2016)
Guang, C., et al.: A implementation of an automatic examination paper generation system. Math. Comput. Model. 51(11–12), 1339–1342 (2010)
Li, Y., Li, S., Li, X.: Test paper generating method based on genetic algorithm. AASRI Procedia 1, 549–553 (2012)
Liu, D., Wang, J., Zheng, L.: Automatic test paper generation based on ant colony algorithm. J. Softw. 8, 2600–2606 (2013)
Alruwais, N., Wills, G., Wald, M.: Advantages and challenges of using e-assessment. Int. J. Inform. Educ. Technol. 8(1), 34–37 (2018)
Thessen, A.E., Cui, H., Mozzherin, D.: Applications of natural language processing in biodiversity science. Adv. Bioinform. 2012, 1–17 (2012)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA (1996)
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Popescu, D.A., Stanciu, G.C., Nijloveanu, D. (2021). Evaluation Test Generator Using a List of Keywords. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_52
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DOI: https://doi.org/10.1007/978-3-030-80421-3_52
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