A Smart Trajectory Model for Teacher Training

Merzon, Elena E.; Galimullina, Elvira Z.; Ljubimova, Elena Mikhaelovna

doi:10.4018/978-1-5225-6136-1.ch010

Cited by 5 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, "level" understanding of domains is considered as potentially useful for indicating utility paths, i.e. [15,18,19].…”

Section: Resultsmentioning

confidence: 99%

Identification Features of External and Internal Variables in the Mathematical Model of Educational Trajectory

Tatiana

2020

IJEAT

View full text Add to dashboard Cite

The article describes the main stages of identification and formation of variables which are included in the developed mathematical model of the student’s educational trajectory. Analyzed are such groups of external variables as geographical location, age and gender, learning style and parameters of user interaction with the user interface, academic performance, level of possession of complex skills. The numerical values of the variables in different conditions are determined. The types of sessions in which information is collected are highlighted. Data arrays from such information sources as the GlobalLab online platform and the electronic diary Diary.ru were analyzed. Using the studied variables, a mathematical model of the student is presented, which takes into account many properties. The aim of the project is to create a technological model for the application of machine learning methods to predict the optimal educational trajectory of the student. Achieving this goal and using the scientific and technical results of the Project will provide a number of useful technical, technological and technical and economic effects.

show abstract

“…In particular, "level" understanding of domains is considered as potentially useful for indicating utility paths, i.e. [15,18,19].…”

Section: Resultsmentioning

confidence: 99%

Identification Features of External and Internal Variables in the Mathematical Model of Educational Trajectory

Tatiana

2020

IJEAT

View full text Add to dashboard Cite

show abstract

“…Infra-structure descriptions in section 12 of PNI Report for 1 st & 2 nd Phase) [1], Due to the upgrade of the database of users activity in e-learning platforms, the implementation, setup, and administration of the framework has progressed. The overall range of services providing the task to modify the behavioral dataset of users in e-learning structures in the 2 nd phase has been increased to 160 [15,20].…”

Section: Ensuring Cloud Technology Implementation Setup and Management System That Enhances User Activity Housing In E-learning Platformsmentioning

confidence: 99%

User Behavioral Data Enhancement and Vectorization in E-Learning Models

Krupa¹

2021

cibg

View full text Add to dashboard Cite

The data acquiring mechanism on the criteria of users-interaction to the users-interface has been developed in the initial stage of this applied science research. The input system gets multiple inhomogeneous texts about the interface actions of a specific user, whereas the vector has been demonstrated by the results that describe the user in a consolidated manner. Moreover, the vector collection for various users has been employed as an input of clustering algorithm (i.e. K-means), the outcomes of behaviors of users are among the one of these (k-clusters) that differentiates the users by their behavioral types. Almost, 67.8% of the Interactional data of the interface of the user is accessible to GlobalLab platform users. However, for the electronic diary (i.e.Diary.ru) there is no corresponding data. Although not every user of the GlobalLab system takes the initiative to create the projects, ideas, questionnaire task, and educational resources, the proportion of learners have been 9.7 thousand whose value with all 4 variables varied according to a neutral one.

show abstract

“…The total number of services providing work to update the user behavior data set in e-learning systems in Phase 2 has been expanded to 160 [15,20].…”

Section: Ensuring Deployment Configuration and System Administratiomentioning

confidence: 99%

Vectorization and Optimization of User Behavior Data in E Learning Systems

Tatiana

2020

IJEAT

View full text Add to dashboard Cite

At the first stage, an applied scientific research developed a procedure for collecting data on the parameters of user interaction with the user interface. This input procedure receives many heterogeneous messages about the actions of a particular user in the interface, while the output represents a vector that describes the user in aggregated form. The set of vectors for different users, in turn, was then used as input for the k-means clustering algorithm, the result of which is the user's attitude to one of the k clusters that distinguish the user by the type of behavior. User interface interaction data is available to 67.8% of GlobalLab platform users. There is no such data for the Diary.ru electronic diary. Considering that not all users of the GlobalLab platform took measures to create a project, ideas, work with questionnaires and educational materials, the total number of students for whom the value of all 4 variables differs from the neutral one was 9.7 thousand.

show abstract

A Smart Trajectory Model for Teacher Training

Cited by 5 publications

References 3 publications

Identification Features of External and Internal Variables in the Mathematical Model of Educational Trajectory

Identification Features of External and Internal Variables in the Mathematical Model of Educational Trajectory

User Behavioral Data Enhancement and Vectorization in E-Learning Models

Vectorization and Optimization of User Behavior Data in E Learning Systems

Contact Info

Product

Resources

About