Siberian Federal University 
- Issue
- Journal of Siberian Federal University. Engineering & Technologies. 2022 15 (1)
- Authors
- Uglev, Viktor A.; Sychev, Oleg A.; Anikin, Anton V.
- Contact information
- Uglev, Viktor A.: Siberian Federal University Zheleznogorsk, Russian Federation; uglev-v@yandex.ru; Sychev, Oleg A.: Volgograd State Technical University Volgograd, Russian Federation; oasychev@gmail.com; Anikin, Anton V.: Volgograd State Technical University Volgograd, Russian Federation; anton@anikin.name
- Keywords
- data mining; e-learning; intelligent decision making; intelligent tutoring systems; e-assessment; digital educational footprint; Bloom’s taxonomy; level of competence development; ontologies; question generation; adaptive learning
- Abstract
The paper describes the problems of generation and modeling of learning assessments, their grading, and analyzing the process of their completion by learners using data mining methods. The authors propose to enhance the digital footprint of learner’s interactions with e-assessment systems with the information allowing determining demonstrated level competence development and the causes of learners’ mistakes and hypo. The learner’s level of competence development can be evaluated using expert estimates and the Certainty Factor. Generating assessments and determining the causes of learner’s mistakes can be done using ontological models of subject domains, built on the comprehension level of Bloom’s taxonomy. The paper explores the properties of such models. Examples of applying the proposed approaches to intelligent decision making during the learning process of different subject domains are shown. This leads to formulating the requirements for subject domain models for intelligent tutoring systems
- Pages
- 121–136
- DOI
- 10.17516/1999-494X-0378
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/145391
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).