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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">RUDN Journal of Informatization in Education</journal-id><journal-title-group><journal-title xml:lang="en">RUDN Journal of Informatization in Education</journal-title><trans-title-group xml:lang="ru"><trans-title>Вестник Российского университета дружбы народов. Серия: Информатизация образования</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2312-8631</issn><issn publication-format="electronic">2312-864X</issn><publisher><publisher-name xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">42729</article-id><article-id pub-id-type="doi">10.22363/2312-8631-2024-21-4-448-464</article-id><article-id pub-id-type="edn">SJSRYC</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>TEACHING COMPUTER SCIENCE</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>ОБУЧЕНИЕ ИНФОРМАТИКЕ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Designing the trajectories of variant teaching of the basics of artificial intelligence in the school course of computer science taking into account the possibilities of project-research and extracurricular activities</article-title><trans-title-group xml:lang="ru"><trans-title>Проектирование траекторий вариативного обучения основам искусственного интеллекта в школьном курсе информатики с учетом возможностей проектно-исследовательской и внеурочной деятельности</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8151-8108</contrib-id><contrib-id contrib-id-type="spin">7462-2637</contrib-id><name-alternatives><name xml:lang="en"><surname>Karakozov</surname><given-names>Sergey D.</given-names></name><name xml:lang="ru"><surname>Каракозов</surname><given-names>Сергей Дмитриевич</given-names></name></name-alternatives><bio xml:lang="en"><p>Doctor of Pedagogical Sciences, Professor, Director of the Institute of Mathematics and Informatics</p></bio><bio xml:lang="ru"><p>доктор педагогических наук, профессор, директор Института математики и информатики</p></bio><email>sd.karakozov@mpgu.su</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-0797-5532</contrib-id><contrib-id contrib-id-type="spin">5599-8846</contrib-id><name-alternatives><name xml:lang="en"><surname>Samylkina</surname><given-names>Nadezhda N.</given-names></name><name xml:lang="ru"><surname>Самылкина</surname><given-names>Надежда Николаевна</given-names></name></name-alternatives><bio xml:lang="en"><p>Doctor of Pedagogical Sciences, Associate Professor, Professor at the Department of Theory and Methodology of Informatics Education, Institute of Mathematics and Informatics</p></bio><bio xml:lang="ru"><p>доктор педагогических наук, доцент, профессор кафедры теории и методики обучения информатике, Институт математики и информатики</p></bio><email>nsamylkina@yandex.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Moscow Pedagogical State University</institution></aff><aff><institution xml:lang="ru">Московский педагогический государственный университет</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-12-31" publication-format="electronic"><day>31</day><month>12</month><year>2024</year></pub-date><volume>21</volume><issue>4</issue><issue-title xml:lang="en">VOL 21, NO4 (2024)</issue-title><issue-title xml:lang="ru">ТОМ 21, №4 (2024)</issue-title><fpage>448</fpage><lpage>464</lpage><history><date date-type="received" iso-8601-date="2025-02-03"><day>03</day><month>02</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Karakozov S.D., Samylkina N.N.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Каракозов С.Д., Самылкина Н.Н.</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Karakozov S.D., Samylkina N.N.</copyright-holder><copyright-holder xml:lang="ru">Каракозов С.Д., Самылкина Н.Н.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/informatization-education/article/view/42729">https://journals.rudn.ru/informatization-education/article/view/42729</self-uri><abstract xml:lang="en"><p>Problem statement . The mandatory study of the basics of artificial intelligence and data analysis in general education course of informatics is a significant innovation that requires adjusting methodological system of teaching informatics at school. The article presents the results of research on the problem of designing trajectories of variant teaching of the basics of artificial intelligence and data analysis in the course of computer science of basic general and secondary general education in accordance with the requirements of the updated FSES of general education on the basis of current methodological approaches, taking into account the possibilities of project-research and extracurricular activities. Methodology. Theoretical methods of research were used: analysis of scientific publications on the subject of artificial intelligence and data analysis, analysis and comparison of materials of foreign educational standards of different levels of education, review of domestic practices of implementation of the results of pedagogical research on the methodology of teaching computer science on the basis of integrative methodological approach. Results. On the basis of the proposed components of the methodology of teaching artificial intelligence basics and data analysis, the possibilities of designing different learning trajectories in accordance with personal requests of participants of educational relations, as well as for the rational use of resources of the information educational environment of the organization in the implementation of basic educational programs of general education are shown. Conclusion. Designing trajectories of variant teaching of the basics of artificial intelligence in the school course of computer science, taking into account the possibilities of project-research and extracurricular activities, allows to optimize needs of students and resources of educational organizations.</p></abstract><trans-abstract xml:lang="ru"><p>Постановка проблемы. Обязательное изучение основ искусственного интеллекта и анализа данных в общеобразовательном курсе информатики является существенным нововведением, требующим корректировки методической системы обучения информатике в школе. В статье представлены результаты исследования по проблеме проектирования траекторий вариативного обучения основам искусственного интеллекта и анализа данных в курсе информатики основного общего и среднего общего образования в соответствии с требованиями обновленного ФГОС общего образования на основе актуальных методологических подходов с учетом возможностей проектно-исследовательской и внеурочной деятельности. Методология. Использовались теоретические методы исследования: анализ научных публикаций по тематике искусственного интеллекта и анализа данных, анализ и сравнение материалов зарубежных образовательных стандартов различных уровней образования, обзор отечественных практик внедрения результатов педагогических исследований по методике обучения информатике с опорой на интегративный методологический подход. Результаты. На основе предложенных компонентов методики обуче ния основам искусственного интеллекта и анализа данных показаны возможности проектирования различных траекторий обучения в соответствии с персональными запросами участников образовательных отношений, а также для рационального использования ресурсов информационной образовательной среды организации при реализации основных образовательных программ общего образования. Заключение . Проектирование траекторий вариативного обучения основам искусственного интеллекта в школьном курсе информатики с учетом возможностей проектно-исследовательской и внеурочной деятельности позволяет оптимизировать потребности обучающихся и ресурсы образовательной организации.</p></trans-abstract><kwd-group xml:lang="en"><kwd>personal learning trajectories</kwd><kwd>artificial intelligence</kwd><kwd>data analysis</kwd><kwd>methodology of variant training in the basics of artificial intelligence</kwd><kwd>integrative approach</kwd><kwd>updated FSES of general education</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>персональные траектории обучения</kwd><kwd>искусственный интеллект</kwd><kwd>анализ данных</kwd><kwd>методика вариативного обучения основам искусственного интеллекта</kwd><kwd>интегративный подход</kwd><kwd>обновленный ФГОС общего образования</kwd></kwd-group><funding-group><funding-statement xml:lang="en">The research was carried out within the framework of the state assignment of Ministry of Education of Russian Federation on the theme No. 124052100092-0 “Variant teaching of the basics of artificial intelligence in general education through an intergrative approach”.</funding-statement><funding-statement xml:lang="ru">Исследование выполнено в рамках государственного задания Министерства просвещения Российской Федерации по теме № 124052100092-0 «Вариативное обучение основам искусственного интеллекта в общем образовании на основе интегративного подхода».</funding-statement></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">Samylkina NN. Organization of advanced training in informatics on the basis of integrative approach: monograph. Moscow: Moscow Pedagogical State University; 2020. (In Russ.)</mixed-citation><mixed-citation xml:lang="ru">Самылкина Н.Н. Организация углубленного обучения информатике на основе интегративного подхода: монография. М.: МПГУ, 2020. 346 с.</mixed-citation></citation-alternatives></ref><ref id="B2"><label>2.</label><citation-alternatives><mixed-citation xml:lang="en">Grigoriev SG, Kalinin IA, Samylkina NN. The task system for the first All-Russian Olympiad in artificial intelligence for schoolchildren. Informatics and Education. 2022;37(3):12–20. (In Russ.) https://doi.org/10.32517/0234-0453-2022-37-3-12-20</mixed-citation><mixed-citation xml:lang="ru">Григорьев С.Г., Калинин И.А., Самылкина Н.Н. Система заданий для первой всероссийской олимпиады школьников по искусственному интеллекту. Информатика и образование. 2022. Т. 37. № 3. С. 12-20. https://doi.org/10.32517/02340453-2022-37-3-12-20</mixed-citation></citation-alternatives></ref><ref id="B3"><label>3.</label><citation-alternatives><mixed-citation xml:lang="en">Roy D, Dutta M. A systematic review and research perspective on recommender systems. Journal of Big Data. 2022;9. https://doi.org/10.1186/s40537-022-00592-5</mixed-citation><mixed-citation xml:lang="ru">Roy D., Dutta M. A systematic review and research perspective on recommender systems // Journal of Big Data. 2022. No. 9. https://doi.org/10.1186/s40537-022-00592-5</mixed-citation></citation-alternatives></ref><ref id="B4"><label>4.</label><citation-alternatives><mixed-citation xml:lang="en">He Q, Li X, Cai B. Graph neural network recommendation algorithm based on improved dual tower model. Scientific Reports. 2024;14. https://doi.org/10.1038/s41598-024-54376-3</mixed-citation><mixed-citation xml:lang="ru">He Q., Li X., Cai B. Graph neural network recommendation algorithm based on improved dual tower model // Scientific Reports. 2024. No. 14. https://doi.org/10.1038/s41598-024-54376-3</mixed-citation></citation-alternatives></ref><ref id="B5"><label>5.</label><citation-alternatives><mixed-citation xml:lang="en">Broussard M. Artificial intelligence: limits of the possible. Trans. from English by Arye E. Moscow: Alpina non-fiction; 2020. (In Russ.)</mixed-citation><mixed-citation xml:lang="ru">Брусард М. Искусственный интеллект: пределы возможного / пер. с англ. Е. Арье. М.: Альпина нон-фикшн, 2020. 362 с.</mixed-citation></citation-alternatives></ref><ref id="B6"><label>6.</label><citation-alternatives><mixed-citation xml:lang="en">Kornev MS. History of Big Data: dictionaries, scientific and business periodicals. Bulletin of Russian State University for Humanities. Series: History. Philology. Cultural Studies. Oriental Studies. 2018;1(34):81–85. (In Russ.) https://doi.org/10.28995/20736355-2018-1-81-85</mixed-citation><mixed-citation xml:lang="ru">Корнев М.С. История понятия «большие данные» (Big Data): словари, научная и деловая периодика // Вестник РГГУ. Серия: История. Филология. Культурология. Востоковедение. 2018. № 1 (34). С. 81-85. https://doi.org/10.28995/20736355-2018-1-81-85</mixed-citation></citation-alternatives></ref><ref id="B7"><label>7.</label><citation-alternatives><mixed-citation xml:lang="en">Salij VV, Kuharenko LV, Ishchenko OV. Digital transformation of the economy and implementation of big data storage in company infrastructure. Bulletin of the Academy of Knowledge. 2021;3(44):208–214. (In Russ.) https://doi.org/10.24412/2304-61392021-11240</mixed-citation><mixed-citation xml:lang="ru">Салий В.В., Кухаренко Л.В., Ищенко О.В. Цифровая трансформация экономики и внедрение хранилищ данных на основе больших данных в инфраструктуру компании // Вестник Академии знаний. 2021. № 3 (44). С. 208-214. https://doi.org/10.24412/2304-6139-2021-11240</mixed-citation></citation-alternatives></ref><ref id="B8"><label>8.</label><citation-alternatives><mixed-citation xml:lang="en">Menshchikov AA, Perfilyev VE, Fedosenko MYu, Fabziev IR. The main problems of using Big Data in modern information systems. Stolypin’s Bulletin. 2022;1:316–329. (In Russ.)</mixed-citation><mixed-citation xml:lang="ru">Менщиков А.А., Перфильев В.Э., Федосенко М.Ю., Фабзиев И.Р. Основные проблемы использования больших данных в современных информационных системах // Столыпинский вестник. 2022. № 1. С. 316-329.</mixed-citation></citation-alternatives></ref><ref id="B9"><label>9.</label><citation-alternatives><mixed-citation xml:lang="en">Egorov VB. Some issues of software-defined datacenters. Systems and Means of Informatics. 2020;30(2):103–112. (In Russ.) https://doi.org/10.14357/08696527200210</mixed-citation><mixed-citation xml:lang="ru">Егоров В.Б. Некоторые вопросы программного определения центров обработки данных // Системы и средства информатики. 2020. Т. 30. Вып. 2. С. 103-112 https://doi.org/10.14357/08696527200210</mixed-citation></citation-alternatives></ref><ref id="B10"><label>10.</label><citation-alternatives><mixed-citation xml:lang="en">Samylkina NN, Salakhova AA. Teaching the basics of artificial intelligence and data analysis in the course of computer science at the level of secondary general education: monograph. Moscow: Moscow Pedagogical State University; 2022. (In Russ.) https://doi.org/10.31862/9785426310643</mixed-citation><mixed-citation xml:lang="ru">Самылкина Н.Н., Салахова А.А. Обучение основам искусственного интеллекта и анализа данных в курсе информатики на уровне среднего общего образования: монография. М.: МПГУ, 2022. 228 с. https://doi.org/10.31862/9785426310643</mixed-citation></citation-alternatives></ref><ref id="B11"><label>11.</label><citation-alternatives><mixed-citation xml:lang="en">Levchenko IV, Sadykova AR, Merenkova PA. A model of variant teaching for basic school students in the field of artificial intelligence. Informatics and Education. 2024;39(2):16–24. (In Russ.) https://doi.org/10.32517/0234-0453-2024-39-2-16-24</mixed-citation><mixed-citation xml:lang="ru">Левченко И.В., Садыкова А.Р., Меренкова П.А. Модель вариативного обучения учащихся основной школы в области искусственного интеллекта // Информатика и образование. 2024. Т. 39. № 2. С. 16-24. https://doi.org/10.32517/02340453-2024-39-2-16-24</mixed-citation></citation-alternatives></ref><ref id="B12"><label>12.</label><citation-alternatives><mixed-citation xml:lang="en">Lee SJ, Kwon K. A systematic review of AI education in K-12 classrooms from 2018 to 2023. Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence. 2024;6. https://doi.org/10.1016/j.caeai.2024.100211</mixed-citation><mixed-citation xml:lang="ru">Lee S.J., Kwon K. A systematic review of AI education in K-12 classrooms from 2018 to 2023. Topics, strategies, and learning outcomes // Computers and Education: Artificial Intelligence. 2024. Vol. 6. https://doi.org/10.1016/j.caeai.2024.100211</mixed-citation></citation-alternatives></ref><ref id="B13"><label>13.</label><citation-alternatives><mixed-citation xml:lang="en">Hazzan O, Ragonis N, Lapidot T. Data science and computer science education. In: Guide to teaching computer science: An activity-based approach. 3rd ed. Springer; 2020. p. 95–117. https://doi.org/10.1007/978-3-030-39360-1_6</mixed-citation><mixed-citation xml:lang="ru">Hazzan O., Ragonis N., Lapidot T. Data science and computer science education // Guide to teaching computer science: An activity-based approach. 3rd ed. Springer, 2020. P. 95-117. https://doi.org/10.1007/978-3-030-39360-1_6</mixed-citation></citation-alternatives></ref><ref id="B14"><label>14.</label><citation-alternatives><mixed-citation xml:lang="en">Foundations of data science for students in grades K-12: Proceedings of a workshop. Washington: National Academies Press; 2023. https://doi.org/10.17226/26852</mixed-citation><mixed-citation xml:lang="ru">Foundations of data science for students in grades K-12: Proceedings of a workshop. Washington: National Academies Press, 2023. 152 p. https://doi.org/10.17226/26852</mixed-citation></citation-alternatives></ref><ref id="B15"><label>15.</label><citation-alternatives><mixed-citation xml:lang="en">Israel-Fishelson R, Moon PF, Tabak R, Weintrop D. Preparing students to meet their data: an evaluation of K-12 data science tools. Behaviour &amp; Information Technology. 2023;1–20. https://doi.org/10.1080/0144929X.2023.2295956</mixed-citation><mixed-citation xml:lang="ru">Israel-Fishelson R., Moon P.F., Tabak R., Weintrop D. Preparing students to meet their data: an evaluation of K-12 data science tools // Behaviour &amp; Information Technology. 2023. P. 1-20. https://doi.org/10.1080/0144929X.2023.2295956</mixed-citation></citation-alternatives></ref><ref id="B16"><label>16.</label><citation-alternatives><mixed-citation xml:lang="en">Tkach TV. Machine learning and Big Data processing in a modern school. Informatics in School. 2020;7(160):25–29. (In Russ.) https://doi.org/10.32517/2221-19932020-19-7-25-29</mixed-citation><mixed-citation xml:lang="ru">Ткач Т.В. Машинное обучение и обработка больших данных в условиях современной школы // Информатика в школе. 2020. № 7 (160). С. 25-29. https://doi.org/10.32517/2221-1993-2020-19-7-25-29</mixed-citation></citation-alternatives></ref></ref-list></back></article>
