Ensure business insights from digital footprints provided by various educational providers within the scope of federal projects.

The system complex facilitates the reception of digital footprints to providers. A significant effort has been made to ensure transparency in drawing conclusions and providing feedback on errors in the formation of digital footprints.
1. Big data normalization and labeling system.
2. Processing and pre-preparation system for the interface.
3. Multi-role system with access to performance indicators.
One of the key developments is the ability to manage data display in the interface and grant access rights directly from the database programming.

Course and Education provider ranking in federal projects

A comprehensive report has been prepared, examining the project through various methodologies. A dedicated methodology has been developed for the project, where the course's performance is gauged by a combination of evaluations from numerous student reflections, including responses to questions like "what have you learned." Additionally, other feedback parameters from students are considered. The resulting metric provides valuable insights into the knowledge density (terminological richness) imparted during the course and the level of students' comprehension based on their responses.

Enhancing the Capabilities of the Semantic Network in the Educational Platform

The semantic network is a collection of entity instances (e.g., students, courses, course providers, etc.) connected through multiple objects (hence the term "neuro-"). The neuro-semantic network has enabled the following functionalities:1. Course classification based on subject areas and educational levels for the educational program event.2. An algorithm to check for similar programs and prevent double government funding.3. A semantic navigation algorithm for exploring ontological connections.These solutions have allowed the business to secure significant government contracts for conducting federal educational programs.

Developing an adaptive recommender system for integration into diverse educational scenarios

"Next educational step" recommendation system - tailored to various product scenarios. Versatile recommendation system "Next education step" designed to seamlessly adapt to different educational scenarios, such as:
1. Student Project Intensive.
2. Project Accelerator for "Islands and Archipelago 2035."
3. Event Recommendations for Educational Activities.
4. Course Recommendations within the Federal Project "Digital Professions."
Along with other potential usage cases in various product scenarios. Our system employs multiple algorithms, including informational search, character advisor, experience advisor, and role advisor. Furthermore, our comprehensive solution incorporates a subsystem for fine-tuning the recommendation algorithm based on user feedback.

Develop an Algorithm for Recommending Non-obvious but Beneficial Educational Steps

We analyzed the educational paths of users on the educational platform (37k link transitions, 13k users, 850 online-courses). Objects were categorized into thematic clusters, and we analyzed the cohesion between these clusters (represented by colors). Notably, we observed instances of users deviating from the primary theme; for example, a user who enrolled in the course "Machine Learning in Finance" might also express interest in "Practical Applications of Blockchain," despite the absence of a direct semantic link. Additionally, during the study, we identified user groups labeled as "entrepreneurs" and "developers" based on their navigational patterns.

Implement a Extension for LMS (Learning Management System) that enables course personalization

EdX LMS Extension enabling course personalization for Instructors.The implemented extension for EdX LMS empowers program instructors to facilitate various course progressions, offering adaptive strategies:1. "Marination" - Ensuring a solid understanding of the theoretical and practical essentials.2. "Acceleration" - Accelerating course completion.3. "Explorer" - Encouraging exploratory behavior.The extension also confirms the following functionalities:4. Pre-adaptation of the course prior to the semester's commencement.5. Course assembly from fragments for master's programs.Additionally, the instructor's dashboard has been realized, streamlining administrative tasks.

Ensure recommendation algorithm accuracy surpasses the accuracy accounting for training dataset errors.

An operator dashboard has been implemented, enabling rapid data entry with suggestions for the best match from the hierarchical rubricator's categories.
The rubricator is based on the OKPD2 (Classification of Economic Activities) directory, which consists of 22,000 elements across 8 hierarchy levels.
The algorithm efficiently addresses challenges related to uneven granularity, duplication, homonymy, discrepancies in codes, seriality, batch processing, high-level categorization, and operator markup errors.

What do people who have taken online courses have in common? Are there recurring psychometric indicators measured by diagnostic instruments that increase the likelihood of a learner completing the course successfully?

Using machine learning methods, patterns of several characteristics have been identified, which can be labeled as follows:
1. Self-improvement inclination
2. Conformity
3. Discipline
Additionally, specific characteristics that increase or decrease the likelihood of course completion have been separately identified.