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+@ARTICLE{Denisov2021256,
+author={Denisov, M. and Anikin, A. and Sychev, O. and Katyshev, A.},
+title={Program execution comprehension modelling for algorithmic languages learning using ontology-based techniques},
+journal={Advances in Intelligent Systems and Computing},
+year={2021},
+volume={1184},
+pages={256-269},
+doi={10.1007/978-981-15-5859-7_25},
+url={https://link.springer.com/chapter/10.1007%2F978-981-15-5859-7_25},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation; Software Engineering School, Volgograd, Russian Federation},
+abstract={In this paper, we propose an ontology-based approach to model a program execution comprehension so to be able to explain to the novice programmer the essence of his/her error. We have studied the algorithmic languages model operating with actions and basic control structures (“sequence,” “branching,” and “looping”) and designed the rules to capture any deviation from the permissible. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.},
+correspondence_address1={Anikin, A.; Software Engineering SchoolRussian Federation; эл. почта: Anton@Anikin.name},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={21945357},
+language={English},
+abbrev_source_title={Adv. Intell. Sys. Comput.},
+thanks = {rfbr-18-07-00032}
+}
+@ARTICLE{Sychev2021501,
+author={Sychev, O. and Kamennov, Y.},
+title={Eligibility of English Hypernymy Resources for Extracting Knowledge from Natural-Language Texts},
+journal={Advances in Intelligent Systems and Computing},
+year={2021},
+volume={1310},
+pages={501-507},
+doi={10.1007/978-3-030-65596-9_61},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-65596-9_61},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={A common subtask of knowledge acquisition from natural-language texts is classifying words and recognizing entities and actions in the text. It is used in the analysis of both scientific and narrative texts. Thesauri and lexical databases containing hypernymy relationship between synsets may be a useful resource for entity and action recognition. In this study, we compared the performance of three major English thesauri containing hypernymy relationship in different forms - WordNet, Roget’s Thesaurus, and FrameNet - on 6 word-meaning categories that are used for the analysis of narrative and scientific natural-language texts. The results show that WordNet contains more words than FrameNet, and is more suitable for scientific texts, but FrameNet contains better-defined hypernyms and shows better precision for many narrative natural-language tasks, especially for verbs. Roget’s Thesaurus performance is average between WordNet and FrameNet in most word-meaning categories Enhancing FrameNet by adding more lexical units to existing frames would allow creating a powerful resource for entity and action recognition in text analysis. Fixing WordNet problems require revising its system of hypernyms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
+correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: oasychev@gmail.com},
+editor={Samsonovich A.V., Gudwin R.R., Simoes A.d.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={21945357},
+isbn={9783030655952},
+language={English},
+abbrev_source_title={Adv. Intell. Sys. Comput.},
+thanks = {rfbr-20-07-00764}
+}
+@ARTICLE{Litovkin2021188,
+author={Litovkin, D. and Dontsov, D. and Anikin, A. and Sychev, O.},
+title={Suitability of Object-Role Modeling Diagrams as an Intermediate Model for Ontology Engineering: Testing the Rules for Mapping},
+journal={Advances in Intelligent Systems and Computing},
+year={2021},
+volume={1310},
+pages={188-194},
+doi={10.1007/978-3-030-65596-9_24},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-65596-9_24},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={Creating and understanding ontologies using OWL2 language is a hard, time-consuming task for both domain experts and consumers of knowledge (for example, teachers and students). Using Object-Role Modeling diagrams as an intermediate model facilitates this process. To achieve this, the method of mapping ORM2 diagrams to OWL2 ontologies and vice versa is necessary. Such methods were proposed in different works, but their suitability and possible errors are in doubt. In this paper, we propose a method of evaluating how well existing rules of mapping follow ORM semantics. Several ontologies were created using mapping rules and tested. During testing, a significant difference between ORM2 and OWL2 basic properties and assumptions were discovered. This difference require updating the mapping rules. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
+correspondence_address1={Anikin, A.; Volgograd State Technical UniversityRussian Federation; эл. почта: anton@anikin.name},
+editor={Samsonovich A.V., Gudwin R.R., Simoes A.d.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={21945357},
+isbn={9783030655952},
+language={English},
+abbrev_source_title={Adv. Intell. Sys. Comput.},
+thanks = {rfbr-20-07-00764}
+}
+@ARTICLE{Uglev202193,
+author={Uglev, V. and Sychev, O.},
+title={Creating and Visualising Cognitive Maps of Knowledge Diagnosis During the Processing of Learning Digital Footprint},
+journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
+year={2021},
+volume={12677 LNCS},
+pages={93-98},
+doi={10.1007/978-3-030-80421-3_11},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-86970-0_47},
+affiliation={Siberian Federal University, Zheleznogorsk, Russian Federation; Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={The paper describes the problem of creating a single mapping of the learning situation during the processing of digital footprint and decision making in intelligent tutoring systems. We propose creating a Cognitive Map of Knowledge Diagnosis as a way of summarising data in the digital footprint. The elements of this cognitive map and the details of their visualisation are described. The results of the use of cognitive maps are demonstrated on the example of personalising the content of an online learning course and providing detailed feedback. © 2021, Springer Nature Switzerland AG.},
+correspondence_address1={Uglev, V.; Siberian Federal UniversityRussian Federation; эл. почта: uglev-v@yandex.ru},
+editor={Cristea A.I., Troussas C.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={03029743},
+isbn={9783030804206},
+language={English},
+abbrev_source_title={Lect. Notes Comput. Sci.},
+thanks = {rfbr-20-07-00764}
+}
+@ARTICLE{Novozhenina2021681,
+author={Novozhenina, E. and Sychev, O. and Toporkova, O. and Evtushenko, O.},
+title={Teaching English Word Order with CorrectWriting Software},
+journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
+year={2021},
+volume={12951 LNCS},
+pages={681-692},
+doi={10.1007/978-3-030-86970-0_47},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-86970-0_47},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={The article looks into the problem of using interactive computer technologies in technology-enhanced teaching English at a technical university. An electronic course “English Word Order” using CorrectWriting software for Moodle LMS was developed and used to teach students of various levels of education. The system of formative and summative assessments regarding the word order of English sentences was studied. The students’ responses to CorrectWriting questions were analyzed using the Longest Common Subsequence algorithm, allowing determining misplaced, missing and extraneous words. During formative assessments, the students were offered automatic hints on how to fix their mistakes without having to wait for feedback from the teacher. The learning gains and the student survey prove the efficiency of the developed online course which helps to improve students’ grammar skills during the classes and unsupervised work. The study also notes a positive role of electronic teaching materials in optimizing the technological and organizational support of the educational process. Further work on enhancing the developed course and software is outlined. © 2021, Springer Nature Switzerland AG.},
+correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: o_sychev@vstu.ru},
+editor={Gervasi O., Murgante B., Misra S., Garau C., Blecic I., Taniar D., Apduhan B.O., Rocha A.M., Tarantino E., Torre C.M.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={03029743},
+isbn={9783030869694},
+language={English},
+abbrev_source_title={Lect. Notes Comput. Sci.},
+thanks = {rfbr-20-07-00764}
+}
+@ARTICLE{Uglev2021443,
+author={Uglev, V. and Sychev, O.},
+title={Concentrating Competency Profile Data into Cognitive Map of Knowledge Diagnosis},
+journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
+year={2021},
+volume={12909 LNAI},
+pages={443-446},
+doi={10.1007/978-3-030-86062-2_46},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_46},
+affiliation={Siberian Federal University, Zheleznogorsk, Russian Federation; Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={The paper describes the process of aggregating primary learning data in the form of learning digital footprints for managing educational process using cognitive visualization techniques. The over-arching process of transition from the learning data to competency diagrams and concentrate them into Cognitive Maps of Cnowledge Diagnosis that. The competency profile, represented as a radar-chart diagram, is compressed into a cognitive map, allowing interpreting this information during decision making by faculty and administrative staff. This allows generalizing results for groups of students. The results of using competency profiles and respective cognitive maps to analyze the results of summative assessments, final, and cross-curricular exams are provided as an illustration of the proposed approach. © 2021, Springer Nature Switzerland AG.},
+correspondence_address1={Uglev, V.; Siberian Federal UniversityRussian Federation; эл. почта: vauglev@sfu-kras.ru},
+editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={03029743},
+isbn={9783030860615},
+language={English},
+abbrev_source_title={Lect. Notes Comput. Sci.},
+thanks = {rfbr-20-07-00764}
+}
+@ARTICLE{Denisov2021408,
+author={Denisov, M. and Anikin, A. and Sychev, O.},
+title={Dynamic Flowcharts for Enhancing Learners’ Understanding of the Control Flow During Programming Learning},
+journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
+year={2021},
+volume={12909 LNAI},
+pages={408-411},
+doi={10.1007/978-3-030-86062-2_42},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_42},
+affiliation={Volgograd State Technical University, Lenin Ave, 28, Volgograd, 400005, Russian Federation},
+abstract={In introductory programming learning, flowcharts are often used to help students comprehend patterns of behavior of control flow statements like alternatives, loops, and switches. The use of flowcharts in assignments along with lectures can increase their learning effect. In some areas, the potential of flowcharts remains largely underestimated. Flowcharts are rare in intelligent learning systems for learning programming, although class, object, and sequence diagrams are quite popular. This poster describes the implementation of a non-editable flowchart widget in our intelligent programming tutor on control flow structures (hereinafter referred to as the exerciser). In normal use, the student chooses the next correct action in the program text (algorithm), i.e., makes one transition through the flowchart. Our exerciser allows making a wrong choice and gives a text hint explaining the reason for incorrectness. The wrong or inexistent transition is also displayed on the flowchart to help localize the error. © 2021, Springer Nature Switzerland AG.},
+correspondence_address1={Anikin, A.; Volgograd State Technical University, Lenin Ave, 28, Russian Federation; эл. почта: anton.anikin@vstu.ru},
+editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={03029743},
+isbn={9783030860615},
+language={English},
+abbrev_source_title={Lect. Notes Comput. Sci.},
+thanks = {rfbr-20-07-00764}
+}
+@ARTICLE{Sychev2021404,
+author={Sychev, O.},
+title={Visualizing Program State as a Clustered Graph for Learning Programming},
+journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
+year={2021},
+volume={12909 LNAI},
+pages={404-407},
+doi={10.1007/978-3-030-86062-2_41},
+url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_41},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={The poster presents a method of visualizing the program state as a diagram of data elements that can both include nested elements and reference other elements. It differs from the known methods because it allows nested objects and arrays; the method also acknowledges high-level data structures like containers and iterators. Layout methods for graphs of this complexity are discussed, and examples of the resulting graphs are provided. The approach was used in computer science courses; the survey showed that it was popular among middle- and low-performing students while high-performing students preferred a professional tool, giving more compact images. Educational uses of the generated diagrams are discussed. © 2021, Springer Nature Switzerland AG.},
+correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: o_sychev@vstu.ru},
+editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
+publisher={Springer Science and Business Media Deutschland GmbH},
+issn={03029743},
+isbn={9783030860615},
+language={English},
+abbrev_source_title={Lect. Notes Comput. Sci.},
+thanks = {rfbr-20-07-00764}
+}
+@CONFERENCE{Sychev2021_6,
+author={Sychev, O.A. and Prokudin, A.A. and Evtushenko, O.E. and Toporkova, O.V.},
+title={The impact of formative quizzes using CorrectWriting question type on learning word order in an ESL course},
+journal={Journal of Physics: Conference Series},
+year={2021},
+volume={1801},
+number={1},
+doi={10.1088/1742-6596/1801/1/012011},
+art_number={012011},
+url={https://iopscience.iop.org/article/10.1088/1742-6596/1801/1/012011},
+affiliation={Volgograd State Technical University, Lenina Avenue 28, Volgograd, 400005, Russian Federation},
+abstract={Studying word order in English sentences is a problem for ESL students whose native languages allow flexible word orders. Developing skills in formulating English sentences often requires trial-and-error process that takes time and requires supervision. Quiz software that is able to analyse students' answers, and give meaningful feedback on mistakes in word order and hints on fixing them allows training without teacher's supervision that greatly enhances the amount of attempts a student can do. In this study, we used CorrectWriting question type for Moodle LMS to create formative and summative quizzes for Russian students learning English as a second language. The experiments show that the students who made more than one attempt of formative quizzes had significantly better learning gains than the students who didn't use formative quizzes or attempted them only once. It also showed vulnerabilities in question design that need to be fixed to utilise software features better in learning process. © Published under licence by IOP Publishing Ltd.},
+correspondence_address1={Sychev, O.A.; Volgograd State Technical University, Lenina Avenue 28, Russian Federation; эл. почта: oasychev@gmail.com},
+publisher={IOP Publishing Ltd},
+issn={17426588},
+language={English},
+abbrev_source_title={J. Phys. Conf. Ser.},
+thanks = {rfbr-20-07-00764}
+}
+@CONFERENCE{Sychev2021623,
+author={Sychev, O.},
+title={CorrectWriting: Open-Ended Question with Hints for Teaching Programming-Language Syntax},
+journal={Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE},
+year={2021},
+pages={623-624},
+doi={10.1145/3456565.3460030},
+url={https://dl.acm.org/doi/10.1145/3456565.3460030},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={Teaching students to construct correct statements in programming languages is an important part of introductory programming courses. While modern development environments highlight syntax errors, they do not stimulate thinking in grammatical terms to help the student understand and memorize syntax rules. To facilitate learning syntax, we developed CorrectWriting, a question-type plug-in for the popular LMS Moodle. It finds mistakes in token order and composition and detects typos, including missing and extraneous separators. Mistake messages use teacher-supplied token descriptions to show the grammatical role of each wrong token. Hints are provided about students' mistakes. CorrectWriting questions are actively used by the students of Volgograd State Technical University to prepare for classwork. © 2021 Owner/Author.},
+correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: oasychev@gmail.com},
+publisher={Association for Computing Machinery},
+issn={1942647X},
+isbn={9781450383974},
+language={English},
+abbrev_source_title={Annu. Conf. Innov. Technol. Comput. Sci. Educ. ITiCSE},
+thanks = {rfbr-20-07-00764}
+}
+@CONFERENCE{Sychev2021621,
+author={Sychev, O. and Denisov, M. and Terekhov, G.},
+title={How it Works: Algorithms-A Tool for Developing an Understanding of Control Structures},
+journal={Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE},
+year={2021},
+pages={621-622},
+doi={10.1145/3456565.3460032},
+url={https://dl.acm.org/doi/10.1145/3456565.3460032},
+affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
+abstract={Developing an understanding of control structures is one of the important tasks in introductory programming courses. To facilitate active learning with immediate feedback, we developed a constraint-based tutor, "How it Works: Algorithms,"that asks students to build an execution trace of the given algorithm and provides explanatory feedback about the mistakes the student made. The reasons for the student's faults are determined by an inference engine, using a set of rules describing the subject domain. Teachers can create exercises using a simple visual block-based interface and sending them to students as permanent links. © 2021 Owner/Author.},
+publisher={Association for Computing Machinery},
+issn={1942647X},
+isbn={9781450383974},
+language={English},
+abbrev_source_title={Annu. Conf. Innov. Technol. Comput. Sci. Educ. ITiCSE},
+thanks = {rfbr-20-07-00764}
+}
 @CONFERENCE{Sychev2021728,
 author={Sychev, O.},