2 research outputs found
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±Π°Π·ΠΎΠ²ΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π²ΡΠ±ΠΎΡΠΊΠΈ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΡΠΎΠ²Π½Ρ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ
Purpose of the research. The research of influence of the system of professional education on parameters of development of digital economy in Russian Federation regions can be conducted in different directions: identification of the professional education system status as the institute, providing the digital economy of the region with human resources; to identify the needs of the separate industries of economy for the specialists, having the corresponding competences for the work in the field of the digital economy. The purpose of this research is justification of the mathematical model, allowing creating evidential basic statistical sampling for the evaluation of the levels of mastering digital competences by lecturers of educational institutions of professional education.Materials and methods. In this work the estimation methods, based on soft computing is offered. This approach allows correlating a quality indicator of mastering digital competences and quantitative category, to create basic statistical sampling for the analysis of personnel potential in the field of professional education and assessment of digital competences development in the explored area. The competence-based approach is used for the assessment of readiness of lecturers of the professional education system to carry out the professional activity, aimed at providing development of digital economy of the region. The received values of levels of mastering different digital competences are aggregated on each indicator of a linguistic variable in summary values, which can be used as basic statistical sampling.Results. On the basis of this model statistical analysis of the human resources of the region in the aspect of formation of knowledge and abilities in the field of information and computer technologies can be carried out. This model can be used for information processing about testing of different groups: pedagogical employees, public and municipal officers. The results will allow to diagnose an initial status of levels of mastering digital competences of the employees of the regional industry or the studied organization and to carry out monitoring of development of human resources of the region within the Digital Economy project. Statistically the data obtained on the basis of the offered model are well interpreted with the use of standard graphic means (for example, diagrams and histograms).Conclusion. The developed mathematical model is tested on the basis of real data and accepted as the basic one for evaluating the level of mastering digital competences of lecturers by the Ministry of Education and Youth Policy of the Ryazan region. The offered model has characteristic of universality and can be applied to receive basic statistical samplings of the level of mastering digital competences of areas of the real sector of economy. Further researches are planned to be conducted in the sphere of automation of process of the statistical data analysis on digitalization of the population of the region, first of all in the sphere of professional education. On the basis of the mathematical model the algorithm of analytical processing of statistical data on monitoring of digital competences is developed.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Π Π€ ΠΌΠΎΠ³ΡΡ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡΡΡ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΡ
: Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΈΠ½ΡΡΠΈΡΡΡΠ°, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠ΅Π³ΠΎ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠ΅ΡΡΡΡΠ°ΠΌΠΈ ΡΠΈΡΡΠΎΠ²ΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ ΡΠ΅Π³ΠΈΠΎΠ½Π°; Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΠΎΡΡΠ°ΡΠ»Π΅ΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π² ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°Ρ
, ΠΎΠ±Π»Π°Π΄Π°ΡΡΠΈΡ
ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠΌΠΈ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΡΠΌΠΈ Π΄Π»Ρ ΡΠ°Π±ΠΎΡΡ Π² ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅. Π¦Π΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅ΠΉ Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ Π±Π°Π·ΠΎΠ²ΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ Π²ΡΠ±ΠΎΡΠΊΡ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΡΠΎΠ²Π½Π΅ΠΉ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»ΡΠΌΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° ΠΌΡΠ³ΠΊΠΈΡ
Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡΡ
. ΠΠ°Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠΎΠΎΡΠ½Π΅ΡΡΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΈ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ Π±Π°Π·ΠΎΠ²ΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ Π²ΡΠ±ΠΎΡΠΊΡ Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΠ°Π΄ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° Π² ΡΡΠ΅ΡΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΏΠΎ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ. ΠΠ»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΡ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ Π½Π° ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°, ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠ½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΡΡΠΎΠ²Π½Π΅ΠΉ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ Π°Π³ΡΠ΅Π³ΠΈΡΡΡΡΡΡ ΠΏΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠΌΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π² ΡΠ²ΠΎΠ΄Π½ΡΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π±Π°Π·ΠΎΠ²ΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π²ΡΠ±ΠΎΡΠΊΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄Π°Π½Π½ΡΡ
ΠΎΠ± ΡΡΠΎΠ²Π½Π΅ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΡΡΡΡΠΎΠ² ΡΠ΅Π³ΠΈΠΎΠ½Π° Π² Π°ΡΠΏΠ΅ΠΊΡΠ΅ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΠΎΡΡΠΈ Π·Π½Π°Π½ΠΈΠΉ ΠΈ ΡΠΌΠ΅Π½ΠΈΠΉ Π² ΡΡΠ΅ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎ-ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. ΠΠ°Π½Π½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ°Π·Π½ΡΡ
Π³ΡΡΠΏΠΏ: ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ², Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΡ
ΠΈ ΠΌΡΠ½ΠΈΡΠΈΠΏΠ°Π»ΡΠ½ΡΡ
ΡΠ»ΡΠΆΠ°ΡΠΈΡ
. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΡΡΠΎΠ²Π½Π΅ΠΉ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΎΡΡΠ°ΡΠ»ΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π° ΠΈΠ»ΠΈ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΡΡΡΡΠΎΠ² ΡΠ΅Π³ΠΈΠΎΠ½Π° Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΏΡΠΎΠ΅ΠΊΡΠ° Β«Π¦ΠΈΡΡΠΎΠ²Π°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°Β». Π‘ΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π΄Π°Π½Π½ΡΠ΅, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Ρ
ΠΎΡΠΎΡΠΎ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΡΡΡΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΡ
Π³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ΅Π΄ΡΡΠ² (Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, Π³ΡΠ°ΡΠΈΠΊΠΎΠ² ΠΈ Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌ).ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π°ΠΏΡΠΎΠ±ΠΈΡΠΎΠ²Π°Π½Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅Π°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΏΡΠΈΠ½ΡΡΠ° Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π±Π°Π·ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΡΠΎΠ²Π½Ρ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΌΠΈΠ½ΠΈΡΡΠ΅ΡΡΡΠ²ΠΎΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π ΡΠ·Π°Π½ΡΠΊΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΡΠ²ΠΎΠΉΡΡΠ²ΠΎΠΌ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Π° Π΄Π»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ Π±Π°Π·ΠΎΠ²ΡΡ
ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΡΠ±ΠΎΡΠΎΠΊ ΡΡΠΎΠ²Π½Ρ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ Π² ΠΎΠ±Π»Π°ΡΡΡΡ
ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΠΎΡΠ° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ. ΠΠ°Π»ΡΠ½Π΅ΠΉΡΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠ»Π°Π½ΠΈΡΡΠ΅ΡΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ Π² ΡΡΠ΅ΡΠ΅ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ ΡΠ΅Π³ΠΈΠΎΠ½Π°, ΠΏΡΠ΅ΠΆΠ΄Π΅ Π²ΡΠ΅Π³ΠΎ Π² ΡΡΠ΅ΡΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°Π΅ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Ρ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ
Mathematical model of the formation of the basic statistical sample for evaluating the level of the digital competence of lecturers
Purpose of the research. The research of influence of the system of professional education on parameters of development of digital economy in Russian Federation regions can be conducted in different directions: identification of the professional education system status as the institute, providing the digital economy of the region with human resources; to identify the needs of the separate industries of economy for the specialists, having the corresponding competences for the work in the field of the digital economy. The purpose of this research is justification of the mathematical model, allowing creating evidential basic statistical sampling for the evaluation of the levels of mastering digital competences by lecturers of educational institutions of professional education.Materials and methods. In this work the estimation methods, based on soft computing is offered. This approach allows correlating a quality indicator of mastering digital competences and quantitative category, to create basic statistical sampling for the analysis of personnel potential in the field of professional education and assessment of digital competences development in the explored area. The competence-based approach is used for the assessment of readiness of lecturers of the professional education system to carry out the professional activity, aimed at providing development of digital economy of the region. The received values of levels of mastering different digital competences are aggregated on each indicator of a linguistic variable in summary values, which can be used as basic statistical sampling.Results. On the basis of this model statistical analysis of the human resources of the region in the aspect of formation of knowledge and abilities in the field of information and computer technologies can be carried out. This model can be used for information processing about testing of different groups: pedagogical employees, public and municipal officers. The results will allow to diagnose an initial status of levels of mastering digital competences of the employees of the regional industry or the studied organization and to carry out monitoring of development of human resources of the region within the Digital Economy project. Statistically the data obtained on the basis of the offered model are well interpreted with the use of standard graphic means (for example, diagrams and histograms).Conclusion. The developed mathematical model is tested on the basis of real data and accepted as the basic one for evaluating the level of mastering digital competences of lecturers by the Ministry of Education and Youth Policy of the Ryazan region. The offered model has characteristic of universality and can be applied to receive basic statistical samplings of the level of mastering digital competences of areas of the real sector of economy. Further researches are planned to be conducted in the sphere of automation of process of the statistical data analysis on digitalization of the population of the region, first of all in the sphere of professional education. On the basis of the mathematical model the algorithm of analytical processing of statistical data on monitoring of digital competences is developed