A Method for the Automatic Selection of Training Tasks in Learning Environment for IT Students

Abstract

Purpose of research. The research, the results of which are presented in this article, was carried out in order to activate and improve the efficiency of independent work of students in the information environment of learning by rational individual selection of training tasks. In the process of the research, a method for automatically selecting tasks for self-completion was developed and implemented in the educational process, based on predicting the difficulty and learning effect of the task for a specific student, taking into account the complexity of the task and the student’s readiness to perform this task. Methods and materials. The article provides a distinction between the concepts of complexity, difficulty, and the learning effect of training tasks. On this basis, the task of predicting the level of difficulty of the task for the student is set as a task of automatic classification of “student-task" pairs, which represent a set of characteristics of the student and the task that are available in the database of the e-learning system. The result of the classification is a forecast of the level of difficulty of the task for the student, on the basis of which a decision is made about the learning effect of this task.The classification problem is one of the well-developed machine learning tasks “with a lecturer". Decision trees were selected from several well-known trained classification models for implementation, since they, unlike neural networks, represent prediction rules in a visual form, while highlighting significant features. The learning phase of the model consists of building a decision tree based on a training sample containing data on precedents for students to complete tasks. As a result of the computational experiment, decision trees were built for several disciplines that practice automatic verification of students’ decisions, i.e. there is data for forming a training sample.Results. The article provides an example of a decision tree based on a training sample, which is formed on the basis of data from an electronic workshop on the discipline “Foreign language ". The quality of the predictive model was determined on the exam sample by the criteria of accuracy and generalizing ability (the degree of severity of the “retraining effect”). The obtained values of these indicators allow us to recognize the quality as acceptable. The first results ofpractical application of the proposed method of selecting tasks in the educational process are analyzed. The software developed in the process of the research can be considered as the basis of a recommendation system that can not replace live communication between the student and the lecturer, but is their smart assistant in the learning process. Conclusion. In general, the results of the research show that the capabilities of artificial intelligence technologies, in particular, machine learning, allow us to put into practice the principle of individualized learning, to adapt the learning process to the individual characteristics of each student in order to effectively develop their professional competencies. The proposed method is implemented and tested in the information environment of training students of IT areas of Vologda State University, however, this approach is quite universal, and it can be extended to other subject areas and forms of training.