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+@ARTICLE{Sychev2021mdpi,
+author={Oleg Sychev and Nikita Penskoy and Anton Anikin and Mikhail Denisov and Artem Prokudin},
+title={Improving comprehension: Intelligent tutoring system explaining the domain rules when students break them},
+journal={Education Sciences},
+year={2021},
+volume={11},
+number={11},
+doi={10.3390/educsci11110719},
+art_number={719},
+url={https://www.mdpi.com/2227-7102/11/11/719},
+affiliation={Software Engineering Department, Electronics and Computing Machinery Faculty, Volgograd State Technical University, Volgograd, 400005, Russian Federation},
+abstract={Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower-to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
+correspondence_address1={Sychev, O.; Software Engineering Department, Russian Federation; эл. почта: o_sychev@vstu.ru; Anikin, A.; Software Engineering Department, Russian Federation; эл. почта: anton.anikin@vstu.ru},
+publisher={MDPI},
+issn={22277102},
+language={English},
+abbrev_source_title={Educ. Sci.},
+thanks = {rfbr-20-07-00764}
+}
+@incollection{Sychev2021,
+  doi = {10.1007/978-3-030-86960-1_33},
+  url = {https://doi.org/10.1007/978-3-030-86960-1_33},
+  year = {2021},
+  publisher = {Springer International Publishing},
+  pages = {471--482},
+  author = {Oleg Sychev and Anton Anikin and Mikhail Denisov},
+  title = {Inference Engines Performance in Reasoning Tasks for Intelligent Tutoring Systems},
+  booktitle = {Computational Science and Its Applications {\textendash} {ICCSA} 2021},
+  thanks = {rfbr-20-07-00764}
+}
+@inproceedings{13163,
+    author       = {Oleg Sychev and Dmitrii Sasov and Pavel Chechetkin},
+    title        = {Developing An Understanding Of Variables And Expressions In Introductory Programming Courses},
+    booktitle    = {EpSBS - Volume 102 - NININS 2020},
+    year         = {2021},
+    pages        = {1019-1028},
+    publisher    = {European Publisher},
+    doi          = {10.15405/epsbs.2021.02.02.126},
+    url          = {https://doi.org/10.15405/epsbs.2021.02.02.126},
+    thanks = {rfbr-20-07-00764}
+}
+@InProceedings{10.1007/978-3-030-80421-3_6,
+author="Sychev, Oleg
+and Anikin, Anton
+and Penskoy, Nikita
+and Denisov, Mikhail
+and Prokudin, Artem",
+editor="Cristea, Alexandra I.
+and Troussas, Christos",
+title="CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level",
+booktitle="Intelligent Tutoring Systems",
+year="2021",
+publisher="Springer International Publishing",
+address="Cham",
+pages="52--59",
+abstract="Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom's taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners' thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.",
+isbn="978-3-030-80421-3",
+thanks = {rfbr-20-07-00764}
+}
+@inproceedings{13164,
+    author       = {Oleg Sychev and Mikhail Denisov and Anton Anikin},
+    title        = {Ways To Write Algorithms And Their Execution Traces For Teaching Programming},
+    booktitle    = {EpSBS - Volume 102 - NININS 2020},
+    year         = {2021},
+    pages        = {1029-1039},
+    publisher    = {European Publisher},
+    doi          = {10.15405/epsbs.2021.02.02.127},
+    url          = {https://doi.org/10.15405/epsbs.2021.02.02.127},
+    thanks = {rfbr-20-07-00764}
+}
+@article{Litovkin_2021,
+	doi = {10.1088/1742-6596/1801/1/012009},
+	url = {https://doi.org/10.1088/1742-6596/1801/1/012009},
+	year = 2021,
+	month = {feb},
+	publisher = {{IOP} Publishing},
+	volume = {1801},
+	number = {1},
+	pages = {012009},
+	author = {D V Litovkin and A V Anikin and O A Sychev and T Petrova},
+	title = {{ORM} Diagram as an Intermediate Model for {OWL} Ontology Engineering: Prot{\'{e}}g{\'{e}} {ORM} Plugin Implementation},
+	journal = {Journal of Physics: Conference Series},
+	abstract = {OWL2, a widely-used ontology-representation language, is poorly perceived by humans because OWL2 statements have a low level of abstraction. To solve this issue, various OWL2 ontology editors are used, which allow to group statements and represent them using some visual notation. ORM-diagram is a good candidate for an intermediate model for authoring and understanding of OWL2-ontology as Object-Role Modelling notation supports visually distinguishable constructs, has the high expressive capabilities, and implements the node-link paradigm and the attribute-free approach A Protégé plugin allowing to create an ORM2-diagram using the live error checking approach was implemented. The plugin allows us to form a valid object-oriented diagram model in computer memory using a widely known ontology authoring tool Protégé.},
+thanks = {rfbr-18-07-00032, rfbr-20-07-00764}
+}
+@article{SYCHEV2020,
+title = "Automatic grading and hinting in open-ended text questions",
+journal = "Cognitive Systems Research",
+volume = "59",
+pages = "264 - 272",
+year = "2020",
+issn = "1389-0417",
+doi = "https://doi.org/10.1016/j.cogsys.2019.09.025",
+url = "http://www.sciencedirect.com/science/article/pii/S1389041719304978",
+author = "Oleg Sychev and Anton Anikin and Artem Prokudin",
+thanks = {rfbr-18-07-00032}
+}
+@InProceedings{10.1007/978-981-15-0637-6_31,
+doi="10.1007/978-981-15-0637-6_31",
+author="Litovkin, Dmitry
+and Anikin, Anton
+and Kultsova, Marina",
+editor="Yang, Xin-She
+and Sherratt, Simon
+and Dey, Nilanjan
+and Joshi, Amit",
+title="Interactive Visualization of Ontology-Based Conceptual Domain Models in Learning and Scientific Research",
+booktitle="Fourth International Congress on Information and Communication Technology",
+year="2020",
+publisher="Springer Singapore",
+address="Singapore",
+pages="365--374",
+abstract="The paper presents an approach to knowledge transferring and sharing on the base of the semantic link network (SLN) representing expert knowledge in the explicit form. To provide an efficient SLN understanding, it is represented with a geometric graph which can be interactively visualized using a combination of the appropriate visualization methods. The coupling of these methods allows getting a different level of details of the SLN visualization in accordance with the user needs. The proposed approach is planned being implemented in the knowledge management system for learning and scientific research.",
+isbn="978-981-15-0637-6",
+thanks = {rfbr-18-07-00032, rfbr-18-47-340014}
+}
+@InProceedings{10.1007/978-3-030-29750-3_33,
+doi="10.1007/978-3-030-29750-3_33",
+author="Kultsova, Marina
+and Potseluico, Anastasiya
+and Dvoryankin, Alexander",
+editor="Kravets, Alla G.
+and Groumpos, Peter P.
+and Shcherbakov, Maxim
+and Kultsova, Marina",
+title="Ontology Based Personalization of Mobile Interfaces for People with Special Needs",
+booktitle="Creativity in Intelligent Technologies and Data Science",
+year="2019",
+publisher="Springer International Publishing",
+address="Cham",
+pages="422--433",
+abstract="The paper is devoted to a problem of interface personification for people with special needs on the base of information about their behavior during interaction with mobile applications. This work evolves our previous researches on the development of adaptive user interfaces. Much attention in this paper was given to the investigation of existing approaches to collecting and analyzing the information about user behavior and interaction context data as well as interface adaptation recommendations. The improved interface adaptation mechanism was developed and described based on the ontological representation of the interface patterns and knowledge about users and their interaction with a mobile application. The set of adaptation rules was developed and implemented in the ontology knowledge base. In the paper, we described the improved ontology model and some examples of ontological representation of interface patterns.",
+isbn="978-3-030-29750-3",
+thanks = {rfbr-18-07-00032}
+}
+@InProceedings{10.1007/978-3-030-29750-3_7,
+doi="10.1007/978-3-030-29750-3_7",
+author="Litovkin, Dmitry
+and Anikin, Anton
+and Kultsova, Marina",
+editor="Kravets, Alla G.
+and Groumpos, Peter P.
+and Shcherbakov, Maxim
+and Kultsova, Marina",
+title="Semantic Zooming Approach to Semantic Link Network Visualization",
+booktitle="Creativity in Intelligent Technologies and Data Science",
+year="2019",
+publisher="Springer International Publishing",
+address="Cham",
+pages="81--95",
+abstract="In the paper, we described a semantic zooming approach to the visualization of special kind structures - semantic link networks (SLN), represented as a visual graph. The proposed approach allows decreasing semantic noise in SLN overview and navigation and also simplifies the process of understanding the domain studied with SLN by means of semantic zooming. We proposed priori importance levels of SLN items and semantic zooming scale to visualize the SLN with different details level. We designed an interactive SLN visualization process including the following SLN transformations: filtering SLN items, context collapse and expansion for SLN item, and changing the details in the visualized object representation in the geometric SLN graph. The transformation algorithms were developed, and also examples of SNL semantic zooming were described in details in the paper.",
+isbn="978-3-030-29750-3",
+thanks = {rfbr-18-07-00032, rfbr-18-47-340014}
+}
 @incollection{Anikin2019_2,
   doi = {10.1007/978-3-030-25719-4_4},