Abstract
The taxonomy of educational objectives allows specifying the level of assessments and making the learning process gradual progress over subsequent levels. Out of six levels for the cognitive domain, the most problematic level for e-learning task is the comprehension level because it includes such tasks as interpreting, summarizing and inferring which is typically manually graded. The lack of comprehension-level assessments in e-learning leads to students attempting to solve more difficult tasks without developing comprehension skills, which complicates the learning process.
The article discusses using subject domain representation in the form of an ontology model which can be used to visualize the concepts and their relations for students, serve as a basis of a question-answering system for assessing student’s comprehension of the subject domain, generating a natural-language explanation of the student’s mistakes and creating adaptive quizzes. The requirements for the ontology to capture the comprehension level of the subject domain are formulated. The article discusses various application for such ontological models in the learning process.
This paper presents the results of research carried out under the RFBR grant 18-07-00032 “Intelligent support of decision making of knowledge management for learning and scientific research based on the collaborative creation and reuse of the domain information space and ontology knowledge representation model”.
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References
Al-Yahya M (2014) Ontology-based multiple choice question generation. Sci World J 2014. https://doi.org/10.1155/2014/274949
Alsubait T, Parsia B, Sattler U (2013) A similarity-based theory of controlling MCQ difficulty. In: 2013 second international conference on e-learning and e-technologies in education (ICEEE), pp 283–288, Sept 2013. https://doi.org/10.1109/ICeLeTE.2013.6644389
Anikin A, Litovkin D, Kultsova M, Sarkisova E (2016) Ontology-based collaborative development of domain information space for learning and scientific research. In: Ngonga Ngomo AC, Křemen P (eds.) Knowledge engineering and semantic web: 7th international conference, KESW 2016, Prague, Czech Republic, 21–23 September 2016, Proceedings, pp. 301–315
Anikin A, Litovkin D, Sarkisova E, Petrova T, Kultsova M (2019) Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research. In: IOP conference series: materials science and engineering, vol 483, p 012074. https://doi.org/10.1088/1757-899x/483/1/012074
Bloom BS, Engelhart MB, Furst EJ, Hill WH, Krathwohl DR (1956) Taxonomy of educational objectives. The classification of educational goals. Handbook 1: cognitive domain. Longmans Green, New York
Cubric M, Tosic M (2011) Towards automatic generation of e-assessment using semantic web technologies. Int J e-Assess 1(1). https://ijea.org.uk/index.php/journal/article/view/16
Demaidi MN, Gaber MM, Filer N (2017) Evaluating the quality of the ontology-based auto-generated questions. Smart Learn Environ 4(1):7. https://doi.org/10.1186/s40561-017-0046-6
Grubišić A, Stankov S, Žitko B (2013) Stereotype student model for an adaptive e-learning system. Int J Comput Electr Autom Control Inf Eng 7(4):440–447
Krathwohl DR (2002) A revision of bloom’s taxonomy: an overview. Theory Into Practice 41(4):212–218. https://doi.org/10.1207/s15430421tip4104_2https://www.tandfonline.com/doi/abs/10.1207/s15430421tip41042
Kultsova M, Anikin A, Zhukova I, Dvoryankin A (2015) Ontology-based learning content management system in programming languages domain. In: Kravets A, Shcherbakov M, Kultsova M, Shabalina O (eds) Creativity in intelligent technologies and data science. Springer, Cham, pp 767–777. https://doi.org/10.1007/978-3-319-23766-4_61
Liu H, Wang P (2014) Assessing text semantic similarity using ontology. J Software 9(2):490–497. https://doi.org/10.4304/jsw.9.2.490-497
Onah D, Sinclair J (2015) Massive open online courses – an adaptive learning framework. In: INTED2015 Proceedings. 9th international technology, education and development conference, IATED, 2–4 March 2015, pp 1258–1266. https://library.iated.org/view/ONAH2015MAS
Palombi O, Jouanot F, Nziengam N, Omidvar-Tehrani B, Rousset MC, Sanchez A (2019) OntoSIDES: ontology-based student progress monitoring on the national evaluation system of french medical schools. Artif Intell Med 96:59–67. https://doi.org/10.1016/j.artmed.2019.03.006
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. CoRR abs/1706.03762. http://arxiv.org/abs/1706.03762
Way A, Gough N (2005) Comparing example-based and statistical machine translation. Nat Lang Eng 11(3):295–309. https://doi.org/10.1017/S1351324905003888
Web 3.0: Implications for online learning. TechTrends 55(1):42–46 (2011). https://doi.org/10.1007/s11528-011-0469-9
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Anikin, A., Sychev, O. (2020). Ontology-Based Modelling for Learning on Bloom’s Taxonomy Comprehension Level. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_4
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