Abstract
The design of a good assessment is related to compliance with several requirements. A good design ensures the validity and correctness of the assessment process within education. This paper presents a modality of designing an assessment test in respect of the degree of difficulty of the items and the test in an integrated academic environment, meaning that the items used for assessment are collected at an academic consortium level and they can be used in an integrated way for all the similar faculties from the academic group of institutions. The presented model is composed of two main components: the item collector and the test generator. The generation of tests is made automatically, by using evolutionary algorithms (genetic-based) and machine learning (ML) methods. An implementation of the model for the genetic-based algorithm is also presented.
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References
Beckmann, J., Birney, D., Goode, N.: Beyond psychometrics: the difference between difficult problem solving and complex problem solving. Front. Psychol. 8, 1739 (2017). https://doi.org/10.3389/fpsyg.2017.01739
Blšták, M., Rozinajova, V.: Automatic question generation based on sentence structure analysis using machine learning approach. Nat. Lang. Eng. 28, 1–31 (2021). https://doi.org/10.1017/S1351324921000139
Bold, N.: Item Dataset (2023). https://github.com/nicolaebold/cim_get
Campbell, D.: Task complexity: a review and analysis. Acad. Manage. Rev. 13, 40–52 (1988). https://doi.org/10.5465/AMR.1988.4306775
Das, B., Majumder, M., Phadikar, S., Sekh, A.A.: Automatic question generation and answer assessment: a survey. Res. Pract. Technol. Enhanced Learn. 16(1), 1–15 (2021). https://doi.org/10.1186/s41039-021-00151-1
Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of the first International Conference on Genetic Algorithms and their Applications, pp. 136–140. Psychology Press (2014)
Gkatzia, D., Mahamood, S.: A snapshot of NLG evaluation practices 2005–2014 (2015). https://doi.org/10.18653/v1/W15-4708
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 60–65. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/W14-4111. https://aclanthology.org/W14-4111
Lee, C.K.H.: A review of applications of genetic algorithms in operations management. Eng. Appl. Artif. Intell. 76, 1–12 (2018)
Mesmer, H., Cunningham, J., Hiebert, E.: Toward a theoretical model of text complexity for the early grades: learning from the past, anticipating the future. Reading Res. Q. 47, 235–258 (2012). https://doi.org/10.1002/rrq.019
Mitkov, R., Ha, L., Varga, A., Rello, L.: Semantic similarity of distractors in multiple-choice tests, pp. 49–56 (2009). https://doi.org/10.3115/1705415.1705422
Mostow, J., Chen, W.: Generating instruction automatically for the reading strategy of self-questioning, vol. 200, pp. 465–472 (2009). https://doi.org/10.3233/978-1-60750-028-5-465
Nguyen, H.A., Bhat, S., Moore, S., Bier, N., Stamper, J.: Towards generalized methods for automatic question generation in educational domains. In: Hilliger, I., Munoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol. 13450, pp. 272–284. Springer, Berlin (2022). https://doi.org/10.1007/978-3-031-16290-920
Popescu, A.D., Bold, N., Nijloveanu, D.: A method based on genetic algorithms for generating assessment tests used for learning. Polibits 54, 53–60 (2016). https://doi.org/10.17562/PB-54-7
Popescu, D.A., Bold, N.: The development of a web application for assessment by tests generated using genetic-based algorithms. CEUR Workshop Proceedings (2016)
Quirós, P., Lasheras, F.S.: Methodology for the projection of population pyramids based on monte Carlo simulation and genetic algorithms. Appl. Intell. 1–18 (2023)
Robinson, P.: Task complexity, task difficulty, and task production: exploring interactions in a componential framework. Appl. Linguist. 22, 27–57 (2001). https://doi.org/10.1093/applin/22.1.27
Sancho-Asensio, A., et al.: Improving data partition schemes in smart grids via clustering data streams. Expert Syst. Appl. 41(13), 5832–5842 (2014). https://doi.org/10.1016/j.eswa.2014.03.035
Sharma, S., Kumar, V.: Application of genetic algorithms in healthcare: a review. Next Gener. Healthc. Inf., 75–86 (2022)
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Doru-Anastasiu, P., Daniela-Maria, C., Nicolae, B. (2023). On an Integrated Assessment for the Students Within an Academic Consortium. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_46
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