Abstract
The main goal of this paper is to provide an insight into implementation of a model for continual adaptive online knowledge assessment throughout Adaptivity, a web-based application. Adaptivity enables continual and cumulative knowledge assessment process, which comprises of a sequence of at least two (but preferably more) interconnected tests, carried-out throughout a reasonably long period of time (i.e. one semester). It also provides personalized post-assessment feedback, which is based on each student’s current results, to guide each student in preparations for the upcoming tests. In this paper, we provide description of adaptation model, reveal the design of Adaptivity and results of testing of the proposed model.
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References
Graf, S., Kinshuk: Advanced adaptivity in learning management systems by considering learning styles. In: WI-IAT 2009, IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies 2009, Milan, Italy, vol. 3, pp. 235–238 (2009)
Hafidi, M., Bensebaa, T., Trigano, P.: Developing adaptive intelligent tutoring system based on item response theory and metrics. Int. J. Adv. Sci. Technol. 43, 1–14 (2012)
Ahuja, N.J., Sille, R.: A critical review of development of intelligent tutoring systems: retrospect, present and prospect. Int. J. Comput. Sci. Issues 10(2), 39–48 (2013)
Ying, M.H., Yang, H.L.: Computer-aided generation of item banks based on ontology and bloom’s taxonomy. In: Li, F., et al. (eds.) Advances in Web Based Learning - ICWL 2008. LNCS, vol. 5145, pp. 157–166. Springer, Heidelberg (2008)
Huang, Y.M., Lin, Y.T., Cheng, S.C.: An adaptive testing system for supporting versatile educational assessment. Comput. Educ. 52(1), 53–67 (2009)
Chrysafiadi, K., Virvou, M.: Create dynamically adaptive test on the fly using fuzzy logic. In: 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8. IEEE (2018)
Snytyuk, V., Suprun, O.: Adaptive technology for students’ knowledge assessment as a prerequisite for effective education process management. In: ICTERI, pp. 346–356 (2018)
Mangaroska, K., Vesin, B., Giannakos, M.: Elo-rating method: towards adaptive assessment in e-learning. In: 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), vol. 2161, pp. 380–382. IEEE (2019)
Raman, R., Nedungadi, P.: Adaptive learning methodologies to support reforms in continuous formative evaluation. In: 2010 International Conference on Educational and Information Technology, vol. 2, pp. V2–429. IEEE (2010)
Grundspenkis, J., Anohina, A.: Evolution of the concept map based adaptive knowledge assessment system: implementation and evaluation results. J. Riga Techn. Univ. 38, 13–24 (2009)
Hu, D.: How Khan Academy is using Machine Learning to Assess Student Mastery (2011). http://david-hu.com/2011/11/02/how-khan-academy-is-using-machine-learning-to-assess-student-mastery.html. Accessed 22 Feb 2019
VanLehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Educ. 16(3), 227–265 (2006)
Rus, V., Baggett, W., Gire, E., Franceschetti, D., Conley, M., Graesser, A.: Towards learner models based on learning progressions (LPs) in DeepTutor. In: Sottilare, R.A., et al. (eds.) Design Recommendations for Intelligent Tutoring Systems: Volume 1 – Learner Modeling, pp. 183–192. Army Research Laboratory, Orlando (2013)
Chrysafiadi, K., Troussas, C., Virvou, M.: A framework for creating automated online adaptive tests using multiple-criteria decision analysis. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 226–231. IEEE (2018)
Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W., Krathwohl, D.R.: Taxonomy of Educational Objectives, The Classification of Educational Goals, Handbook I: Cognitive Domain. McKay Press, Midland (1956)
Hatzilygeroudis, I., Koutsojannis, C., Papachristou, N.: Adding adaptive assessment capabilities to an e-learning system. In: SMAP 2006, First International Workshop on Semantic Media Adaptation and Personalization, Athens, Greece, pp. 68–73 (2006)
Zlatović, M., Balaban, I.: Personalizing questions using adaptive online knowledge assessment. In: eLearning 2015-6th International Conference on e-Learning, Belgrade, pp. 185–190 (2015)
Zlatović, M., Balaban, I., Kermek, D.: Using online assessments to stimulate learning strategies and achievement of learning goals. Comput. Educ. 91, 32–45 (2015)
Conejo, R., Guzmán, E., Trella, M.: The SIETTE automatic assessment environment. Int. J. Artif. Intell. Educ. 26(1), 270–292 (2016)
Maier, U., Wolf, N., Randler, C.: Effects of a computer-assisted formative assessment intervention based on multiple-tier diagnostic items and different feedback types. Comput. Educ. 95(1), 85–98 (2016)
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Zlatović, M., Balaban, I. (2020). Adaptivity: A Continual Adaptive Online Knowledge Assessment System. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1161. Springer, Cham. https://doi.org/10.1007/978-3-030-45697-9_15
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DOI: https://doi.org/10.1007/978-3-030-45697-9_15
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