Prediction of Learning Success in an Integrated Educational Environment Using Online Analytics Tool
Abstract
The need to formulate qualifications and prepare students for the digital future is changing the teaching strategies and approaches to university education in the direction of digital design of the learning process. The expandable space of accessible data allows the use of new educational data mining (EDM) methods in order to explore unique data types, understand student actions activity, predict academic results, improve process performance, make management decisions and adapt the learning environment.
The objective of this study is to create a personalized educational environment for individual accompaniment support of students on the basis of a model of cognitive potential.
The task of supporting the learning process is to obtain information on the dynamics of cognitive growth (“growth” of the knowledge level) of each student based on the data obtained during the learning process. The task of differentiating students, predicting the success of training to improve the adaptation and customization of the learning process is considered.
An approach to predicting the success of learning based on a cognitive model is important for understanding the productivity of learning materials by students in an informationrich environment. The task of differentiating students, predicting the success of learning to improve adaptation and tuning the learning process is considered. Organization of feedback in the structure of the learning process based on student differentiation allows you to manage and customize learning scenarios to improve the adaptation of the individual process. An integrated web environment combines traditional learning tools with innovative digital online tools.
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