Menu
Publications
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2023-23-4-757-766
Intelligent adaptive testing system
Read the full article
';
Article in Russian
For citation:
Abstract
For citation:
Tagirova L.F., Zubkova Т.М. Intelligent adaptive testing system. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 4, pp. 757–766 (in Russian). doi: 10.17586/2226-1494-2023-23-4-757-766
Abstract
Modern learning is impossible without automated knowledge testing systems. At present, the most progressive are adaptive testing models in which the complexity of tasks varies depending on the correctness of the patient’s answers. This article describes the development of an intelligent adaptive testing system using a fuzzy mathematics device. An intelligent adaptive testing system has been developed; the module that implements the expert system uses the production base of the rules. The input parameters of testing are the percentage of correct responses, the degree of correctness of the response, the duration of the response, and the number of attempts. The output is a change in the current level of training of the student on the basis of which test questions of related complexity are selected. As a method of logical inference, the Mamdani method is used which consists of six operational actions: phazification — conversion of exact values of input variables into values of linguistic variables through belonging functions, this served as the basis for designing a fuzzy base of rules of the expert system; aggregation of sub-conditions — determination of the truth of conditions for each linguistic rule of the fuzzy inference system; activating sub-conclusions — finding the degree of truth of each of the sub-conclusions in the linguistic rule; accumulation of conclusions — finding the belonging function for each of the output linguistic variables; defazzification — finding a numerical value for each of the output linguistic variables. A developed intelligent adaptive testing system (ISAT) is presented that allows, based on the analysis of test results, to determine the current level of training of students, to adapt the material to the level of their training. This system allows you to dynamically present questions of appropriate complexity in real time. When using the developed intelligent adaptive testing system, students will be provided with questions of the appropriate level of complexity, this will allow building an individual learning trajectory. The introduction of a predefined system will ensure the implementation of a personalized approach for organizing the learning process; will increase the accuracy of assessing students’ knowledge. The use of the technology of fuzzy expert systems allows for automated, intelligent control of students’ knowledge.
Keywords: artificial intelligence, expert system, fuzzy logic, fuzzy mathematics, trainee testing, adaptive testing, intelligent system
References
References
- Semenova N.G., Tomina I.P. Development and Application of Electronic Educational Resources in the Context of Education Digital Transformation. Orenburg, OSU, 2022, 139 p. (in Russian)
- Grigoryev A.P., Burlutsky S.G. A neuronet navigational training system. Information and Control Systems, 2017, no. 3, pp. 89–98. (in Russian), https://doi.org/10.15217/issn1684-8853.2017.3.89
- Chumakova E.V., Korneev D.G., Gasparian M.S. Development of adaptive testing method based on neurotechnologies. Open Education, 2022, vol. 26, no. 2, pp. 4–13. (in Russian) https://doi.org/10.21686/1818-4243-2022-2-4-13
- Gusyatnikov V.N., Sokolova T.N., Bezrukov A.I., Kayukova I.V. Adaptive model for testing several competencies based on the Bayes algorithm. Modern high technologies, 2022, no. 1, pp. 40–46. (in Russian). https://doi.org/10.17513/snt.39007
- Choi Y., McClenen C. Development of adaptive formative assessment system using computerized adaptive testing and dynamic Bayesian networks. Applied Sciences, 2020, vol. 10, no. 22, pp. 8196. https://doi.org/10.3390/app10228196
- Gerasimova T.N., Guseva N.V. The use of professional computer-based testing systems in the process of foreign language teaching in military universities. The world of science, culture and education, 2019, no. 3(76), pp. 224. (in Russian)
- Peregudova I.P. Dynamic adaptive testing of educational activities of students studying tenses of english language. International Research Journal, 2020, no. 10-2(100), pp. 40–45. (in Russian). https://doi.org/10.23670/IRJ.2020.100.10.042
- Dyachuk P.P., Brovka N.V., Noskov M.V., Peregudova I.P. Markov mathematical model of dynamic adaptive testing of an active agent. Informatics and Education, 2018, no. 10(299), pp. 29–35. (in Russian). https://doi.org/10.32517/0234-0453-2018-33-10-29-35
- Chastikova V.A., Kolesnik N.M. Adaptive testing system based on the critical approach to building expert systems. Scientific Works of the Kuban State Technological University, 2018, no. 3, pp. 506–517. (in Russian)
- Duplik C.V. Model of adaptive testing on fuzzy mathematics. Open and Distance Education, 2004, no. 4(16), pp. 78–88. (in Russian)
- Oppl S., Reisinger F., Eckmaier A., Helm C. A flexible online platform for computerized adaptive testing. International Journal of Educational Technology in Higher Education, 2017, vol. 14, no. 1, pp. 2. https://doi.org/10.1186/s41239-017-0039-0
- Yang A.C.M., Flanagan B., Ogata H. Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning. Computers and Education: Artificial Intelligence, 2022, vol. 3, pp. 100104. https://doi.org/10.1016/j.caeai.2022.100104
- Huang H.-T.D., Hung Sh.-T.A., Chao H.-Y., Chen J.-H., Lin T.-P., Shih C.-L. Developing and validating a computerized adaptive testing system for measuring the English proficiency of Taiwanese EFL university students. Language Assessment Quarterly, 2022, vol. 19, no. 2, pp. 162–188. https://doi.org/10.1080/15434303.2021.1984490
- Lin G.-H., Huang Y.-J., Lee Sh.-Ch., Huang Sh.-L., Hsieh Ch.-L. Development of a computerized adaptive testing system of the functional assessment of stroke. Archives of Physical Medicine and Rehabilitation, 2018, vol. 99, no. 4, pp. 676–683. https://doi.org/10.1016/j.apmr.2017.09.116
- Istiyono E., Dwandaru W., 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. European Journal of Educational Research, 2020, vol. 9, no. 1, pp. 91–101. https://doi.org/10.12973/eu-jer.9.1.91
- Gorbachenko V.I., Akhmetov B.S., Kuznetcova O.Iu. Intelligence Systems: Fuzzy Systems and Networks. Moscow, Jurajt Publ., 2023, 105 p. (in Russian)
- Tagirova L.F., Cherniakov A.A. Program system for adaptive testing of the distance learning students. Certificate of the state computer program registration RU2022681117, 2022. (in Russian)
- Tagirova L.F., Bachkovskaia Iu.S. Intelligence system of adaptive testing. Certificate of the state computer program registration RU2023662375, 2023. (in Russian)
