Abstract
The article considers the method that allows to calculate the estimation of knowledge level of educational materials by using indicators of semantic importance of key terms for adaptive selection of test tasks in testing. Each test task allows to purposefully check the level of knowledge of separate semantic units of educational materials – semantic terms (words, phrases). It is assumed that increasing the depth of learning of educational material semantic content has the effect of learning less semantically important units of educational materials. Semantic terms are related to the semantic structure of educational material in the form of rubricational system. Test tasks are related to fragments of educational material content, the knowledge level of which they test. In the process of testing, the level of knowledge of each of semantic structure elements of the educational material is consistently adaptively determined, and the final grade is calculated based on these results. As a result of adaptive selection of test tasks, their maximum possible diversity in the final set is reached, because following criteria are considered: test task has not been used yet, relevant fragment of educational content has not been checked, test tasks of corresponding type were used the least, the test task does not contain less important semantic units. The developed method of adaptive semantic testing of knowledge level of educational materials makes possible to use different algorithms for starting testing (regressive, progressive, medianic, etc.) and different algorithms for knowledge level estimation (average, absolute limit, etc.). Applied investigations of the effectiveness of the developed method in comparison with the traditional algorithm for selecting test tasks established, that testing speed increased an average of 20.53% faster test, and to determine the level of knowledge required the use of an average of 19.33% fewer test tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aggarwal, C.C., Zhai, C.: Text Data. Springer (2012)
Baki, I., Sahraoui, H.: Multi-step learning and adaptive search for learning complex model transformations for examples. ACM Trans. Softw. Eng. Methodol. 25(3), 1–37 (2016). https://doi.org/10.1145/2904904
Barmak, O., Krak, I., Mazurets, O., Pavlov, S., Smolarz, A., Wojcik, W.: Research of efficiency of information technology for creation of semantic structure of educational materials. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds.) ISDMCI 2019. AISC, vol. 1020, pp. 554–569. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26474-1_38
Barmak, O., et al.: Information technology for creation of semantic structure of educational materials. Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments Wilga, Poland (11176) (2019). https://doi.org/10.1117/12.2537064
Brusilovsky, P., Rollinger, C., Peyl, C.: Adaptive and intelligent technologies for web-based education. Special issue on intelligent systems and teleteaching. Konstliche Intelligenz 4, 19–25 (1999)
Carpenter, S.K., Pashler, H., Wixted, J.T., Vul, E.: The effects of tests on learning and forgetting. Memory Cogn. 36, 438–448 (2008). https://doi.org/10.3758/MC.36.2.438
Chen, J., Dosyn, D., Lytvyn, V., Sachenko, A.: Smart data integration by goal driven ontology learning. Adv. Big Data 529, 283–289 (2016). https://doi.org/10.1007/978-3-319-47898-2_29
Cho, K.W., Neely, L.H., Crocco, S., Virando, D.: Testing enhances both encoding and retrieval for both tested and untested items. Q. J. Exp. Psychol. 70(7), 1211–1235 (2017). https://doi.org/10.1080/17470218.2016.1175485
Durlach, P.J., Lesgold, A.M.: Adaptive Technologies for Training and Education. Cambridge University Press, New York (2012)
Gierl, M.J., Lai, H., Hogan, L.B., Matovinovic, D.: A method for generation educational test items that are aligned to the common core state standards. J. Appl. Test. Technol. 16(1), 1–18 (2015)
Gutl, C., Lankmayr, K., Weinhofer, J., Hofler, M.: Enhanced approach of automatic creation of test items to foster modern learning setting. Electron. J. e-Learn. 9, 23–38 (2011)
Hu, X., Pedrycz, W., Castillo, O., Melin, P.: Fuzzy rule-based models with interactive rules and their granular generalization. Fuzzy Sets Syst. 307, 1–28 (2017). https://doi.org/10.1016/j.fss.2016.03.005
Istiyono, E., Dwandaru, W.S.B., Setiawan, R., Megawati, I.: Developing of computerized adaptive testing to measure physics higher order thinking skills of senior high school students and its feasibility of use. Eur. J. Educ. Res. 9(1), 91–101 (2020). https://doi.org/10.12973/eu-jer.9.1.91
Kehrer, T., Alshanqiti, A., Heckel, R.: Automatic inference of rule-based specifications of complex in-place model transformations. In: Guerra, E., van den Brand, M. (eds.) ICMT 2017. LNCS, vol. 10374, pp. 92–107. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61473-1_7
Krak, Y., Barmak, O., Mazurets, O.: The practice investigation of the information technology efficiency for automated definition of terms in the semantic content of educational materials, vol. 1631, pp. 237–245. CEUR Workshop Proceedings (2016)
Krak, Y., Barmak, O., Mazurets, O.: The practice implementation of the information technology for automated definition of semantic terms sets in the content of educational materials, vol. 2139, pp. 245–254. CEUR Workshop Proceedings (2018)
Kryvonos, I.G., Krak, I.V., Barmak, O.V., Bagriy, R.O.: New tools of alternative communication for persons with verbal communication disorders. Cybern. Syst. Anal. 52(5), 665–673 (2016). https://doi.org/10.1007/s10559-016-9869-3
Liu, H., Gegov, A., Cocea, M.: Rule-based systems: a granular computing perspective. Granular Comput. 1(4), 259–274 (2016). https://doi.org/10.1007/s41066-016-0021-6
Manziuk, E.A., Barmak, O.V., Krak, I.V., Kasianiuk, V.S.: Definition of information core for documents classification. J. Autom. Inf. Sci. 50(4), 25–34 (2018). https://doi.org/10.1615/JAutomatInfScien.v50.i4.30
Moghimi, M., Varjani, A.: New rule-based phishing detection method. Expert Syst. Appl. 53, 231–242 (2016). https://doi.org/10.1016/j.eswa.2016.01.028
Pasichnyk, R., Melnyk, A., Pasichnyk, N., Turchenko, I.: Method of adaptive control structure learning based on model of test’s complexity. In: Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2011, vol. 2, pp. 692–695. Prague, Czech Republic (2011). https://doi.org/10.1109/IDAACS.2011.6072858
Saed, M.R.: Methods and Applications for Advancing Distance Education Technologies: International Issue and Solutions. IGI Global (2009)
Sorrel, M.A., Abad, F., Nájera, P.: Improving accuracy and usage by correctly selecting: the effects of model selection in cognitive diagnosis computerized adaptive testing. Appl. Psychol. Measure. 45(2), 112–129 (2021). https://doi.org/10.1177/0146621620977682
Yang, C., Potts, R., Shanks, D.R.: Enhancing learning and retrieval of new information: a review of the forward testing effect. Sci. Learn. 3, 8 (2018). https://doi.org/10.1038/s41539-018-0024-y
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mazurets, O., Barmak, O., Krak, I., Manziuk, E., Bahrii, R. (2022). Method for Adaptive Semantic Testing of Educational Materials Level of Knowledge. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-82014-5_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82013-8
Online ISBN: 978-3-030-82014-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)