Adaptive learning is an advanced technology providing personalization of educational process in e-learning environment. It is deemed currently important to develop and use e-learning courses that include mechanisms of adapting educational content to language peculiarities when teaching students originating from Russian indigenous minority peoples. The article represents an approach to adapting educational content when teaching mathematics in Russian-Tuvan bilingual conditions. The authors have chosen LMS Moodle as a software environment for implementing e-learning courses. It has the functional of educational content mechanism implementation via the accessibility settings of the e-learning course. The authors have developed an adaptive e-learning course (AELC) in discrete mathematics which supports automated course content navigation and is aimed at developing Russian language competence along with mathematical competence. Adaptation mechanisms have been put into action in the AELC by designing the rule base of educational content choosing and their binding to corresponding elements of the AELC, the access of students to which executes their activation by established conditions. Module “Sets and Relations” of the adaptive e-learning course “Discreet Mathematics” has been piloted and showed its effectiveness in training future mathematics and computer science teachers at the Faculty of Physics and Mathematics of Tuvan State University (training program 44.03.01 - Pedagogical Education). Over the longer term it is planned to optimize adaptation mechanisms of the AELC by means of including the conditions of student e-learning environment activity accounting into the rule base of educational content choosing.
Nowadays digitalization of education is characterized by the orientation of
educational programs on intensive use of innovative e-learning technologies providing
personalization of educational process in e-learning environment. Many researchers consider
adaptive learning to be an advanced technology providing personalization [
However, the range of individual characteristics is very wide including cognitive and personal features of students. For example, when teaching students originating from Russian indigenous minority peoples, it is necessary to use mechanisms of adapting educational content to language peculiarities. It is particularly true for junior years when teaching basic disciplines including mathematics. 2
Let us consider the Republic of Tuva, which is located in isolation from the rest of Russia and represents a specific cultural world [9]. By and large, the republic has a bigger percentage of population to speak the Tuvan language natively than in other national regions of Russia. It leads to the fact that many applicants do not have a sufficient command of Russian or only speak everyday Russian. It causes a specific educational process organization problem that resides in the necessity to develop Russian language competence alongside with subject competences because Russian is the instruction language at the university [10, 11].
The authors suggest using adaptive e-learning courses to organize mathematics training in Russian-Tuvan bilingual conditions. When creating the courses, it is important to take into account the level of Russian comprehension of students and make provision for combined development of both mathematical and Russian language competences. Herewith, one of the educational objectives is to acquire learning materials and get the course credit completely in Russian. The authors have chosen LMS Moodle as a software environment for implementing e-learning courses. It has the functional of educational content mechanism implementation via the accessibility settings of the e-learning course.
The authors have used the microteaching strategy when designing the adaptive elearning course in discrete mathematics. Educational content model represents a collection of micro-chunks for learning material represented in several ways with the difference in the extent of Russian use. The learning material variant designed for a student with a low command of Russian is formed simultaneously in Tuvan and Russian. It enables students to adapt to new learning conditions. The next variant of the material is designed for the students who have a medium command of Russian, i.e. those who can speak everyday Russian. This variant contains Russian formulations of statements and definitions and is accompanied by Tuvan translation, also including a mathematical term glossary. The number of fragments in Russian in each microchunk increases from version to version and finally reaches an entirely Russian variant.
When structuring the knowledge domain of the discreet mathematics course with the workload of 180 academic hours, 28 micro-chunks of educational content were formed in three different versions, and 62 terms were included into the subject field glossary.
Let us consider the representation of the educational content of the micro-chunk “Relations”. This is how the definition of relations is represented in the Russian version: ОТНОШЕНИЕ – это способ задания взаимосвязей между элементами. Strict definition: N-АРНОЕ ОТНОШЕНИЕ НА МНОЖЕСТВЕ А называется любое подмножество упорядоченных n элементов этого множества.
The definition is given in Tuvan for the students who do not have a sufficient command of Russian: ХАРЫЛЗАА – дээрге кезектернин бот-боттарынын аразында онаарынын аргазы.
Strict definition: А БӨЛУГДЕ N-АРНЫГ ХАРЫЛЗАА дээрге ол-ла бөлугнун подмножествонун чурумчудуткан n- кезектери.
For the students with the low command of Russian, the material contains Russian formulations of statements and definitions but it is accompanied by the RussianTuvan glossary of mathematical terms, a fragment of which is shown in Table 1.
When preparing educational content of the e-learning course the methodical technique “Language communication technique variability” has been used. It is aimed at the simultaneous use of two languages – Russian and Tuvan to create better learning conditions in the realm of intelligibility and perception of the material learnt.
The AELC provides for automated navigation between the material versions depending not only on the command of Russian but also on the level of acquiring learning material. Students are offered a version of learning material to study. Then the acquisition of new material is checked automatically and, depending on the results obtained, these stages follow: - if there is a problem acquiring language-related learning material, the course automatically offers the learning material version with more material in Tuvan corresponding to the current Russian level of the student; - if the problem lies in the lack of comprehension of the course material, the student gets assistance in the form of automated heuristic prompts and explaining examples embodied as a training test.
Adaptation mechanisms have been put into action in the AELC by designing the rule base of educational content choosing and their binding to corresponding elements of the AELC, the access of students to which executes their activation by established conditions. For example, the rule of automatic presentation of other version of the educational content to the student who has not acquired learning material and shown medium command of Russian is set as follows: IF [Parameter «Command of Russian» : = «Средний» AND
Parameter «Term i acquisition level» : = «Insufficient» (from 0 % to 50%)] ELSE [Parameter «Term i intelligibility » := TRUE AND Parameter «Version» := «Version B» AND
Parameter «Detalization» := « Maximum»].
At the moment the authors are designing the elements of the adaptive e-learning course that include training tests providing step-by-step solution of multistage problems and peer review individual project assignments.
The proposed approach was tested in the educational process of students of Tuva State University. Before the study, the control and experimental groups of 19 people were formed.
The construction of the educational process in the control group was carried out traditionally, and in the experimental group using the approach proposed in the work using AELC. The positive dynamics of the formation of mathematical and russianlanguage competence in the control and experimental groups when teaching discrete mathematics, Fig. 1.
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
Control group
Experimental group Mathematical competence
Russian-speaking competence
Fig. 1. Dynamics of educational results
Assessment of the level of formation of competencies of each student was carried out on a 100-point scale. The histogram shows the average values of the formation of each competence in the control and experimental groups, converted into percentage components. 3
Module “Sets and Relations” of the adaptive e-learning course “Discreet Mathematics” has been piloted and showed its effectiveness in training future mathematics and computer science teachers at the Faculty of Physics and Mathematics of Tuvan State University (training program 44.03.01 – Pedagogical Education).
The use of adaptive e-learning courses enabling adaptation of educational content when teaching mathematics in Russian-Tuvan bilingual conditions has made it possible to develop Russian language competence along with mathematical competence. Over the longer term it is planned to optimize adaptation mechanisms of the AELC by means of including the conditions of student e-learning environment activity accounting into the rule base of educational content choosing. 9. Lamajaa, Ch.K. Tuvinovedenie: Novye gorizonty. M.: Librokom. (2013) 10. Taryma, A. K., Shershneva, V. A., Weinstein, Yu. V. Formation of information and communication competence of the future teacher of the Republic of Tuva in the conditions of bilingualism. Perspectives of science and education, 4(40), 77-90 (2019) 11. Noskov M.V.,Safonov K.V., Tanzy M.V., Shershneva V.A. The formation of mathematical competency of students with account of the national-regional features of the republic of tuva. Vestnik KGPU im. V.P.Astafieva, 3(33), 53-57 (2015) https://www.elibrary.ru/item.asp?id=24306075