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16 June 2021

How to Train Artificial Intelligence to Teach Math to Humans

The Experience of Plario.ru
16 June 2021

How to Train Artificial Intelligence to Teach Math to Humans

The Experience of Plario.ru
16 June 2021

How to Train Artificial Intelligence to Teach Math to Humans

The Experience of Plario.ru
My name is Dmitry Bubnov, and for the past 15 years, I've been leading ENBISYS. We help enterprises on their digital transformation journey, working in distributed development teams with our clients. In addition to our classic custom software development services, back in 2016, we established our own Machine Learning Lab. Since then, we've accumulated quite some competences working with big data. We have also successfully assisted a Western European startup in creating an adaptive learning system, which helped us in our strategic partnership with the Tomsk State University creating smart algorithms to improve the quality of learning.
My name is Dmitry Bubnov, and for the past 15 years, I've been leading ENBISYS. We help enterprises on their digital transformation journey, working in distributed development teams with our clients. In addition to our classic custom software development services, back in 2016, we established our own Machine Learning Lab. Since then, we've accumulated quite some competences working with big data. We have also successfully assisted a Western European startup in creating an adaptive learning system, which helped us in our strategic partnership with the Tomsk State University creating smart algorithms to improve the quality of learning.
My name is Dmitry Bubnov, and for the past 15 years, I've been leading ENBISYS. We help enterprises on their digital transformation journey, working in distributed development teams with our clients. In addition to our classic custom software development services, back in 2016, we established our own Machine Learning Lab. Since then, we've accumulated quite some competences working with big data. We have also successfully assisted a Western European startup in creating an adaptive learning system, which helped us in our strategic partnership with the Tomsk State University creating smart algorithms to improve the quality of learning.
In this blog post, I'd like to share our experience in developing an adaptive math learning platform Plario.ru for high-school students and college/ university freshmen. I will describe technological approaches to creating such systems, as well as further perspective and opportunities of their implementation.
In this blog post, I'd like to share our experience in developing an adaptive math learning platform Plario.ru for high-school students and college/ university freshmen. I will describe technological approaches to creating such systems, as well as further perspective and opportunities of their implementation.
In this blog post, I'd like to share our experience in developing an adaptive math learning platform Plario.ru for high-school students and college/ university freshmen. I will describe technological approaches to creating such systems, as well as further perspective and opportunities of their implementation.
Let's start by defining adaptive learning. Adaptive learning uses computer algorithms interacting with the students and providing them with individualized resources and exercises matching their unique demands. The system adapts the sequence of learning materials to the students' individual characteristics which are defined based on their previous answers to questions and exercises. Adaptive learning systems are aimed at transforming the learner from a passive recipient of information into an active collaborator in the learning process.
Let's start by defining adaptive learning. Adaptive learning uses computer algorithms interacting with the students and providing them with individualized resources and exercises matching their unique demands. The system adapts the sequence of learning materials to the students' individual characteristics which are defined based on their previous answers to questions and exercises. Adaptive learning systems are aimed at transforming the learner from a passive recipient of information into an active collaborator in the learning process.
Let's start by defining adaptive learning. Adaptive learning uses computer algorithms interacting with the students and providing them with individualized resources and exercises matching their unique demands. The system adapts the sequence of learning materials to the students' individual characteristics which are defined based on their previous answers to questions and exercises. Adaptive learning systems are aimed at transforming the learner from a passive recipient of information into an active collaborator in the learning process.
Which tasks are adaptive learning systems designed to solve? Why have they become such a powerful trend in modern education?
Which tasks are adaptive learning systems designed to solve? Why have they become such a powerful trend in modern education?
Which tasks are adaptive learning systems designed to solve? Why have they become such a powerful trend in modern education?
Adaptivity tasks
Adaptivity tasks
Adaptivity tasks
  • Pick the best next task based on the previous success (digital footprint)
  • Predict the probability of the right answer
  • Pick the best next task based on the previous success (digital footprint)
  • Predict the probability of the right answer
  • Pick the best next task based on the previous success (digital footprint)
  • Predict the probability of the right answer
The answer lies in the need of today's users to define the learning outcomes in a clear language and achieve them in the shortest periods of time, while tracking their own progress. One of the things that matter the most is being able to learn anytime from any place.
The answer lies in the need of today's users to define the learning outcomes in a clear language and achieve them in the shortest periods of time, while tracking their own progress. One of the things that matter the most is being able to learn anytime from any place.
The answer lies in the need of today's users to define the learning outcomes in a clear language and achieve them in the shortest periods of time, while tracking their own progress. One of the things that matter the most is being able to learn anytime from any place.
To be able to track the learning progress, it is not enough just to put coursebooks and lectures in a digital format. You must teach the system to help users master only the skills they need
To be able to track the learning progress, it is not enough just to put coursebooks and lectures in a digital format. You must teach the system to help users master only the skills they need
To be able to track the learning progress, it is not enough just to put coursebooks and lectures in a digital format. You must teach the system to help users master only the skills they need
Thus, modern education should be individualized (i.e. adaptive), granulated (presented in the microcontent format), socialized (communication inside and outside the platform) and continuous.
Thus, modern education should be individualized (i.e. adaptive), granulated (presented in the microcontent format), socialized (communication inside and outside the platform) and continuous.
Thus, modern education should be individualized (i.e. adaptive), granulated (presented in the microcontent format), socialized (communication inside and outside the platform) and continuous.
Algorithm task:
  • Recognize student profile
  • Exclude emotional factors
  • Choose the optimal trajectory

This is the reason why adaptive learning systems are created, where the main objective is to select the most appropriate content for each student with his/her digital footprint in mind.
Algorithm task:
  • Recognize student profile
  • Exclude emotional factors
  • Choose the optimal trajectory

This is the reason why adaptive learning systems are created, where the main objective is to select the most appropriate content for each student with his/her digital footprint in mind.
Algorithm task:
  • Recognize student profile
  • Exclude emotional factors
  • Choose the optimal trajectory

This is the reason why adaptive learning systems are created, where the main objective is to select the most appropriate content for each student with his/her digital footprint in mind.
Adaptive Learning System Functions
There are several types of adaptive learning systems:
Adaptive Learning System Functions
There are several types of adaptive learning systems:
Adaptive Learning System Functions
There are several types of adaptive learning systems:
Machine learning based
Artificial intelligence recognizes the pattern, and a digital twin is created as the basis for other algorithms.
Advanced algorithm
Such algorithms record clicks and intervals, compare them to other students and recalculate the trajectory.
Rules based
Such systems provide several trajectories and ensure rough personalization.
Decision trees
These algorithms include strictly fixed conditions "if-then": if a student failed question T1, then show theory and go back to skill X2.
Machine learning based
Artificial intelligence recognizes the pattern, and a digital twin is created as the basis for other algorithms.
Advanced algorithm
Such algorithms record clicks and intervals, compare them to other students and recalculate the trajectory.
Rules based
Such systems provide several trajectories and ensure rough personalization.
Decision trees
These algorithms include strictly fixed conditions "if-then": if a student failed question T1, then show theory and go back to skill X2.
Machine learning based
Artificial intelligence recognizes the pattern, and a digital twin is created as the basis for other algorithms.
Advanced algorithm
Such algorithms record clicks and intervals, compare them to other students and recalculate the trajectory.
Rules based
Such systems provide several trajectories and ensure rough personalization.
Decision trees
These algorithms include strictly fixed conditions "if-then": if a student failed question T1, then show theory and go back to skill X2.
Plario.ru example
Plario.ru example
Plario.ru example
In partnership with Tomsk State University, ENBISYS has resolved the challenge associated with "leveling out" basic math skill levels among students using adaptive learning technologies in Plario.ru simulator.

There are several reasons we chose math as the subject. First, it's quite easy to decompose math into logical granulated pieces of content. Second, there is a tangible problem: students enrolling in higher educational institutions find themselves at very different levels of math mastery. Their level is often not enough to master more advanced math. Thus, the efficiency of group classes drops, because the lecturer might be struggling to adapt the structure and the pace of the learning materials to the actual mastery level of each student. Besides, many high school students need additional individual work to prepare for the Unified State Examination.
In partnership with Tomsk State University, ENBISYS has resolved the challenge associated with "leveling out" basic math skill levels among students using adaptive learning technologies in Plario.ru simulator.

There are several reasons we chose math as the subject. First, it's quite easy to decompose math into logical granulated pieces of content. Second, there is a tangible problem: students enrolling in higher educational institutions find themselves at very different levels of math mastery. Their level is often not enough to master more advanced math. Thus, the efficiency of group classes drops, because the lecturer might be struggling to adapt the structure and the pace of the learning materials to the actual mastery level of each student. Besides, many high school students need additional individual work to prepare for the Unified State Examination.
In partnership with Tomsk State University, ENBISYS has resolved the challenge associated with "leveling out" basic math skill levels among students using adaptive learning technologies in Plario.ru simulator.

There are several reasons we chose math as the subject. First, it's quite easy to decompose math into logical granulated pieces of content. Second, there is a tangible problem: students enrolling in higher educational institutions find themselves at very different levels of math mastery. Their level is often not enough to master more advanced math. Thus, the efficiency of group classes drops, because the lecturer might be struggling to adapt the structure and the pace of the learning materials to the actual mastery level of each student. Besides, many high school students need additional individual work to prepare for the Unified State Examination.
Plario.ru includes 6 learning modules: algebraic expressions conversion, trigonometry, logarithms, rational equations and inequations, and functions
Plario.ru includes 6 learning modules: algebraic expressions conversion, trigonometry, logarithms, rational equations and inequations, and functions
Plario.ru includes 6 learning modules: algebraic expressions conversion, trigonometry, logarithms, rational equations and inequations, and functions
Each module is presented as a set of interconnected skills (from 30 to 60), and each of them corresponds to the certain ability to use a given set of theoretical knowledge to solve practical problems. Each skill is associated with multiple learning materials (theory and practical tasks). Working with those learning materials is the essential part of the learning process.

The state (model) of the student is represented as the sum of pairs (skill, mastery). Mastery is the extent to which the skill is absorbed and confidence that the student has learned this skill. Mastery can take on a value within the range (0, 1]. The skill is considered to be mastered when the confidence is higher than a certain threshold value, e.g. 0.9.
Each module is presented as a set of interconnected skills (from 30 to 60), and each of them corresponds to the certain ability to use a given set of theoretical knowledge to solve practical problems. Each skill is associated with multiple learning materials (theory and practical tasks). Working with those learning materials is the essential part of the learning process.

The state (model) of the student is represented as the sum of pairs (skill, mastery). Mastery is the extent to which the skill is absorbed and confidence that the student has learned this skill. Mastery can take on a value within the range (0, 1]. The skill is considered to be mastered when the confidence is higher than a certain threshold value, e.g. 0.9.
Each module is presented as a set of interconnected skills (from 30 to 60), and each of them corresponds to the certain ability to use a given set of theoretical knowledge to solve practical problems. Each skill is associated with multiple learning materials (theory and practical tasks). Working with those learning materials is the essential part of the learning process.

The state (model) of the student is represented as the sum of pairs (skill, mastery). Mastery is the extent to which the skill is absorbed and confidence that the student has learned this skill. Mastery can take on a value within the range (0, 1]. The skill is considered to be mastered when the confidence is higher than a certain threshold value, e.g. 0.9.
The system was developed based on the following requirements:
The system was developed based on the following requirements:
The system was developed based on the following requirements:
1. When using the system, the student must achieve the target level, i.e. master all skills with the confidence not lower than a certain threshold value, e.g. 0.9. This means that the student will have this skill at the required level with 90% probability.

2. The initial preparedness level helps define which skills the student needs to train and which skill he/she should start with. The system must diagnose the initial knowledge level with detailed analysis of each skill.

3. To achieve the target state, each student needs a different number of learning materials of various complexity levels. The system must provide the amount of various level learning materials that should be sufficient to master each skill. To exclude the chance of the student memorizing the right answer, the content must be redundant.

4. The current student state (mastery of each skill) must be available at any time.

5. The system must provide the student with an individual sequence of learning materials corresponding with his/her current state.

6. All theoretical materials must be available to the student regardless of his/her current state.
1. When using the system, the student must achieve the target level, i.e. master all skills with the confidence not lower than a certain threshold value, e.g. 0.9. This means that the student will have this skill at the required level with 90% probability.

2. The initial preparedness level helps define which skills the student needs to train and which skill he/she should start with. The system must diagnose the initial knowledge level with detailed analysis of each skill.

3. To achieve the target state, each student needs a different number of learning materials of various complexity levels. The system must provide the amount of various level learning materials that should be sufficient to master each skill. To exclude the chance of the student memorizing the right answer, the content must be redundant.

4. The current student state (mastery of each skill) must be available at any time.

5. The system must provide the student with an individual sequence of learning materials corresponding with his/her current state.

6. All theoretical materials must be available to the student regardless of his/her current state.
1. When using the system, the student must achieve the target level, i.e. master all skills with the confidence not lower than a certain threshold value, e.g. 0.9. This means that the student will have this skill at the required level with 90% probability.

2. The initial preparedness level helps define which skills the student needs to train and which skill he/she should start with. The system must diagnose the initial knowledge level with detailed analysis of each skill.

3. To achieve the target state, each student needs a different number of learning materials of various complexity levels. The system must provide the amount of various level learning materials that should be sufficient to master each skill. To exclude the chance of the student memorizing the right answer, the content must be redundant.

4. The current student state (mastery of each skill) must be available at any time.

5. The system must provide the student with an individual sequence of learning materials corresponding with his/her current state.

6. All theoretical materials must be available to the student regardless of his/her current state.
Therefore, Plario.ru consists of the following elements: adaptive algorithm, subject domain model in the form of ontologies (skill graphs) and a diagnostic algorithm
Therefore, Plario.ru consists of the following elements: adaptive algorithm, subject domain model in the form of ontologies (skill graphs) and a diagnostic algorithm
Therefore, Plario.ru consists of the following elements: adaptive algorithm, subject domain model in the form of ontologies (skill graphs) and a diagnostic algorithm
Due to the subject domain specifics, we decided to use the extended version of ВКТ algorithm. The differences from the standard version are:
  • set individual values to "transit", "slip" and "guess" probabilities for each pair "learning material - skill";
  • correlate learning materials with more than one skill;
  • create "prerequisite" type dependencies between skills.
Extended BKT in Plario.ru helps to dynamically form an individual learning trajectory, with the trajectory changing after each exercise is completed.
Due to the subject domain specifics, we decided to use the extended version of ВКТ algorithm. The differences from the standard version are:
  • set individual values to "transit", "slip" and "guess" probabilities for each pair "learning material - skill";
  • correlate learning materials with more than one skill;
  • create "prerequisite" type dependencies between skills.
Extended BKT in Plario.ru helps to dynamically form an individual learning trajectory, with the trajectory changing after each exercise is completed.
Due to the subject domain specifics, we decided to use the extended version of ВКТ algorithm. The differences from the standard version are:
  • set individual values to "transit", "slip" and "guess" probabilities for each pair "learning material - skill";
  • correlate learning materials with more than one skill;
  • create "prerequisite" type dependencies between skills.
Extended BKT in Plario.ru helps to dynamically form an individual learning trajectory, with the trajectory changing after each exercise is completed.
This algorithm allows us to evaluate the current state of the student (which skills are mastered and which skills are to be mastered) after each interaction with the system, i.e. giving the answer to the problem.
This algorithm allows us to evaluate the current state of the student (which skills are mastered and which skills are to be mastered) after each interaction with the system, i.e. giving the answer to the problem.
This algorithm allows us to evaluate the current state of the student (which skills are mastered and which skills are to be mastered) after each interaction with the system, i.e. giving the answer to the problem.
At the first stage of the project, we had no actual data on the learning parameters and outcomes to build the recommendation part of the system (adaptive logic), so we decided to use the materials ranging algorithm that was reviewed and enhanced to meet the system requirements. We are going to transfer to machine learning technologies later, after we accumulate enough data.

The adaptivity of the learning process is ensured by a flexible algorithm recommending student activities that provides:
  • accelerated elimination of gaps;
  • learning process continuity;
  • required prerequisites (mastery level sufficient for moving forward).
At the first stage of the project, we had no actual data on the learning parameters and outcomes to build the recommendation part of the system (adaptive logic), so we decided to use the materials ranging algorithm that was reviewed and enhanced to meet the system requirements. We are going to transfer to machine learning technologies later, after we accumulate enough data.

The adaptivity of the learning process is ensured by a flexible algorithm recommending student activities that provides:
  • accelerated elimination of gaps;
  • learning process continuity;
  • required prerequisites (mastery level sufficient for moving forward).
At the first stage of the project, we had no actual data on the learning parameters and outcomes to build the recommendation part of the system (adaptive logic), so we decided to use the materials ranging algorithm that was reviewed and enhanced to meet the system requirements. We are going to transfer to machine learning technologies later, after we accumulate enough data.

The adaptivity of the learning process is ensured by a flexible algorithm recommending student activities that provides:
  • accelerated elimination of gaps;
  • learning process continuity;
  • required prerequisites (mastery level sufficient for moving forward).
Competency framework (skill graph)
Competency framework (skill graph)
Competency framework (skill graph)
The competency framework in each Plario.ru module was developed by the group of experts of Tomsk State University. It is presented as an oriented acyclic graph, where:
  • the nodes represent specific skills (e.g. factor out common factor, group summands);
  • arcs represent dependencies between skills, where the initial node corresponds with the prerequisite skill and the end node to the dependent skill.
  • dependencies have "power" describing the degree of necessity of mastering the prerequisite in order to start learning the skill (where 1 is maximum dependency that does not allow starting to learn the skill unless the prerequisite skill is mastered).
The state of each student at each moment (student model) can be presented as the sum of skills each having a defined individual mastery value (confidence that this student has mastered this skill). By superposing the student model on the skill graph, we get the individual skill graph of this student.
The competency framework in each Plario.ru module was developed by the group of experts of Tomsk State University. It is presented as an oriented acyclic graph, where:
  • the nodes represent specific skills (e.g. factor out common factor, group summands);
  • arcs represent dependencies between skills, where the initial node corresponds with the prerequisite skill and the end node to the dependent skill.
  • dependencies have "power" describing the degree of necessity of mastering the prerequisite in order to start learning the skill (where 1 is maximum dependency that does not allow starting to learn the skill unless the prerequisite skill is mastered).
The state of each student at each moment (student model) can be presented as the sum of skills each having a defined individual mastery value (confidence that this student has mastered this skill). By superposing the student model on the skill graph, we get the individual skill graph of this student.
The competency framework in each Plario.ru module was developed by the group of experts of Tomsk State University. It is presented as an oriented acyclic graph, where:
  • the nodes represent specific skills (e.g. factor out common factor, group summands);
  • arcs represent dependencies between skills, where the initial node corresponds with the prerequisite skill and the end node to the dependent skill.
  • dependencies have "power" describing the degree of necessity of mastering the prerequisite in order to start learning the skill (where 1 is maximum dependency that does not allow starting to learn the skill unless the prerequisite skill is mastered).
The state of each student at each moment (student model) can be presented as the sum of skills each having a defined individual mastery value (confidence that this student has mastered this skill). By superposing the student model on the skill graph, we get the individual skill graph of this student.
Put simply, the learning process has the student moving through the individual graph from mastered skills to those which have not been mastered yet, following the dependencies between them.
Put simply, the learning process has the student moving through the individual graph from mastered skills to those which have not been mastered yet, following the dependencies between them.
Put simply, the learning process has the student moving through the individual graph from mastered skills to those which have not been mastered yet, following the dependencies between them.
Fragment of skill graph from Simplification of Algebraic Expressions module
Fragment of skill graph from Simplification of Algebraic Expressions module
Fragment of skill graph from Simplification of Algebraic Expressions module
The educational content in Plario.ru was prepared by Tomsk State University experts based on the proprietary methodology. It is based on the following principles:
  • high degree of granularity - priority on "micro doses" of content which are aimed at clarifying / training a small but complete knowledge segment (applying a certain rule or formula) the mastering of which does not require a lot of time;
  • independence of content units from one another allowing to combine them in any order;
  • priority of the practical part over the theoretical one.

Educational content in Plario.ru consists of:
  • theoretical materials presented mainly as reference data or problem solution examples with explanations;
  • practical problems playing a dual role in the system: they have both educational and controlling function.

When educational materials are created in the system, they are described in accordance with the extended BKT algorithm used in Plario.ru. At the diagnostic stage it is important to evaluate the mastery level in a minimal time with maximum degree of certainty. Therefore, diagnostic problems have slightly different requirements:
  • the number and the complexity of problems must be selected so that an average student could provide answers to the test within 2 academic hours;
  • problems must cover all skills of the modulel;
  • if possible, each skill must be covered by more than one problem, to avoid the impact of accidental errors or guessing;
  • incorrect answers are a source of additional information about the existing gaps in the knowledge and it should be possible to use this information in the system.
The educational content in Plario.ru was prepared by Tomsk State University experts based on the proprietary methodology. It is based on the following principles:
  • high degree of granularity - priority on "micro doses" of content which are aimed at clarifying / training a small but complete knowledge segment (applying a certain rule or formula) the mastering of which does not require a lot of time;
  • independence of content units from one another allowing to combine them in any order;
  • priority of the practical part over the theoretical one.

Educational content in Plario.ru consists of:
  • theoretical materials presented mainly as reference data or problem solution examples with explanations;
  • practical problems playing a dual role in the system: they have both educational and controlling function.

When educational materials are created in the system, they are described in accordance with the extended BKT algorithm used in Plario.ru. At the diagnostic stage it is important to evaluate the mastery level in a minimal time with maximum degree of certainty. Therefore, diagnostic problems have slightly different requirements:
  • the number and the complexity of problems must be selected so that an average student could provide answers to the test within 2 academic hours;
  • problems must cover all skills of the modulel;
  • if possible, each skill must be covered by more than one problem, to avoid the impact of accidental errors or guessing;
  • incorrect answers are a source of additional information about the existing gaps in the knowledge and it should be possible to use this information in the system.
The educational content in Plario.ru was prepared by Tomsk State University experts based on the proprietary methodology. It is based on the following principles:
  • high degree of granularity - priority on "micro doses" of content which are aimed at clarifying / training a small but complete knowledge segment (applying a certain rule or formula) the mastering of which does not require a lot of time;
  • independence of content units from one another allowing to combine them in any order;
  • priority of the practical part over the theoretical one.

Educational content in Plario.ru consists of:
  • theoretical materials presented mainly as reference data or problem solution examples with explanations;
  • practical problems playing a dual role in the system: they have both educational and controlling function.

When educational materials are created in the system, they are described in accordance with the extended BKT algorithm used in Plario.ru. At the diagnostic stage it is important to evaluate the mastery level in a minimal time with maximum degree of certainty. Therefore, diagnostic problems have slightly different requirements:
  • the number and the complexity of problems must be selected so that an average student could provide answers to the test within 2 academic hours;
  • problems must cover all skills of the modulel;
  • if possible, each skill must be covered by more than one problem, to avoid the impact of accidental errors or guessing;
  • incorrect answers are a source of additional information about the existing gaps in the knowledge and it should be possible to use this information in the system.
Further plans and opportunities
Further plans and opportunities
Further plans and opportunities
Plario.ru platform was initially designed to function without an instructor. The instructor could oversee the students' progress but not interfere with the process directly.

We are currently completing the system with new subjects and we are adding some functions that would assist the instructor during the learning process.

Plario system will be enhanced and transformed into a digital training package allowing to:

issue individual homework and control performance depending on the target state set by the instructor and the student's progress;
provide interfaces for various subjects (video, AR/VR content, checking the logic of a provided solution to the problem, etc);
offer an optimal course / subject plan to the instructor depending on the students' specialty;
reveal gifted and weaker students and help the instructor to adjust the target learning indicators.
Plario.ru platform was initially designed to function without an instructor. The instructor could oversee the students' progress but not interfere with the process directly.

We are currently completing the system with new subjects and we are adding some functions that would assist the instructor during the learning process.

Plario system will be enhanced and transformed into a digital training package allowing to:

issue individual homework and control performance depending on the target state set by the instructor and the student's progress;
provide interfaces for various subjects (video, AR/VR content, checking the logic of a provided solution to the problem, etc);
offer an optimal course / subject plan to the instructor depending on the students' specialty;
reveal gifted and weaker students and help the instructor to adjust the target learning indicators.
Plario.ru platform was initially designed to function without an instructor. The instructor could oversee the students' progress but not interfere with the process directly.

We are currently completing the system with new subjects and we are adding some functions that would assist the instructor during the learning process.

Plario system will be enhanced and transformed into a digital training package allowing to:

issue individual homework and control performance depending on the target state set by the instructor and the student's progress;
provide interfaces for various subjects (video, AR/VR content, checking the logic of a provided solution to the problem, etc);
offer an optimal course / subject plan to the instructor depending on the students' specialty;
reveal gifted and weaker students and help the instructor to adjust the target learning indicators.
In the new reality and with the development of technologies, adaptive learning systems must provide students with individual tracks for building new skills. They should assist the instructor in making the blended learning process as personalized as possible
In the new reality and with the development of technologies, adaptive learning systems must provide students with individual tracks for building new skills. They should assist the instructor in making the blended learning process as personalized as possible
In the new reality and with the development of technologies, adaptive learning systems must provide students with individual tracks for building new skills. They should assist the instructor in making the blended learning process as personalized as possible
Plario.ru is a solid example of an adaptive system solving a real problem of students and instructors. The concept of the platform matches the priority task of today's educational establishments associated with digitalization of the educational environment.

By using smart algorithms, ontologies and the expertise of the leading mathematicians of Tomsk State University, Plario transforms the process of "leveling out" the students' knowledge. Additionally, it decreases the workload of instructors removing their routine operations, and also reduces the load on the classrooms. In 2019-2021, Plario was integrated in 5 Russian higher educational establishments and 6 schools with the total number of users over 5000.
Plario.ru is a solid example of an adaptive system solving a real problem of students and instructors. The concept of the platform matches the priority task of today's educational establishments associated with digitalization of the educational environment.

By using smart algorithms, ontologies and the expertise of the leading mathematicians of Tomsk State University, Plario transforms the process of "leveling out" the students' knowledge. Additionally, it decreases the workload of instructors removing their routine operations, and also reduces the load on the classrooms. In 2019-2021, Plario was integrated in 5 Russian higher educational establishments and 6 schools with the total number of users over 5000.
Plario.ru is a solid example of an adaptive system solving a real problem of students and instructors. The concept of the platform matches the priority task of today's educational establishments associated with digitalization of the educational environment.

By using smart algorithms, ontologies and the expertise of the leading mathematicians of Tomsk State University, Plario transforms the process of "leveling out" the students' knowledge. Additionally, it decreases the workload of instructors removing their routine operations, and also reduces the load on the classrooms. In 2019-2021, Plario was integrated in 5 Russian higher educational establishments and 6 schools with the total number of users over 5000.
Do you have a new custom software development project in mind? Contact us and we can start talking about how we can collaborate on your next development project.
Do you have a new custom software development project in mind? Contact us and we can start talking about how we can collaborate on your next development project.
Do you have a new custom software development project in mind? Contact us and we can start talking about how we can collaborate on your next development project.