The article presents the result of work on the creation and implementation of the annual course “Algorithmics for Preschool Children”, which, under the guidance of Academician V. B. Betelin for six years led the Department of Educational Informatics of the SRISA RAS together with the Department of Education of the Administration of the city of Surgut (Western Siberia). Since September 2018, the course has been held in all preparatory groups of all kindergartens in Surgut — more than 6,000 children annually. The educational and methodological kit for the course, including the digital educational environment PiktoMir, is freely distributed and can be downloaded from the SRISA RAS website for use for any purpose, including commercial. The course “Algorithmics for preschoolers” discussed in the article is the first part of the long-term course “The basics of programming for preschoolers and junior schoolchildren” being developed. The course uses a textless programming technique. The child composes a program from pictograms with robot commands, singleletter names of subroutines and pictograms of control structures. At the initial stage of training, a screenless technology for compiling a program from material objects is used. The proposed technology favorably differs from analogues in the world in that when working with PiktoMir, a program in the material world can be composed of material objects freely movable by a child: cards, cubes with printed or hand-drawn command pictograms. Information about each command of the program is extracted exclusively from the image perceived by the child, and not from any machine-readable graphic codes or electrical codes hardwired into the material carrier of the pictogram. The child “photographs” pictograms with his tablet, on which they are recognized using neural networks by a special PiktoMir module.
No abstract
Процесс цифровизации образования, активно проводимый в нашей стране и по всему миру, позволил более широко применить в учебном процессе современные приемы преподавания, перенося часть педагогической нагрузки с очного формата на дистанционный. Проектируемые и используемые цифровые образовательные платформы уже сейчас включают в себя не только оцифрованный лекционный видеоматериал и электронные формы учебников, но и элементы автоматизации проверки выполненных учащимися заданий. Расширение области применения автоматической проверки решенных учащимися задач и выполненных упражнений является объективной необходимостью, в противном случае при дистанционных формах образовательного процесса резко возрастает нагрузка на педагога, который должен выделять значительное время на проверку увеличившегося самостоятельной работы школьников и студентов. Кроме того, при дистанционном преподавании снижается эффект личного присутствия педагога, когда учитель и ученики разделены экранами компьютеров. Существенной помощью может стать использование интеллектуальных помощников преподавателя и автоматизированных систем проверки, построенных методами машинного обучения и технологии нейронных сетей. В настоящей статье рассмотрены подходы к решению поставленных задач по автоматической проверке графических заданий и выявлению заимствований в текстовом виде. Показаны возможные варианты реализации этих функций с использованием технологий искусственного интеллекта. The digitalization of education in Russia and worldwide enables a more extensive introduction of advanced teaching methods through a partial switch from offline to online teaching. The existing and coming e-learning platforms feature not only digital lecture videos and e-textbooks, but some automated assessment/grading tools. There is a need to expand the coverage of such tools to avoid the extreme burden of online teaching as the educator has to allocate significant time for assessing the increased amount of high school/university student assignments. Also, distant learning diminishes the effect of the educator personal presence since the teacher and the student are separated by their computer screens. Smart educator assistants and automated assessment tools based on machine learning and neural networks can significantly alleviate the problem. This study offers some strategies for automated assessment of graphic assignments and checks for plagiarism. Possible AI-based implementations of such features are presented.
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