Machine Learning Methods of Providing Informational Management Support for Students’ Professional Development

Abstract

Introduction.Professional development of students requires effective interaction with teachers, scientists, university administrators, students, representatives of professional community and labour market. The effectiveness of this interaction resulted from its information support, based on reliable information, promptly provided to all the members of learning process.The aim of this paper was to study the machine learning methods potential for the effective management of learning process by the example of implementing information support component designed to diagnose and predict the professional development of students based on automatic text analysis.Methodology and research methods. The theoretical basis of the research involved modelling of students’ professional development using the analysis of textual informative and professional relevance in written works of students. To identify the characteristics of professional development, a computer cluster analysis of texts was carried out using the K-means method of clustering. The Bayes method was used to construct a model for classifying students from the standpoint of identified features.Results and scientific novelty. A computer analysis of texts relating to different stages of learning for the evaluation of general and special vocabulary was performed. Regularities in the dynamics of students’ use of general scientific and professional terminology were revealed. Accordingly, the groups with certain trends of educational behaviour of students were formed. It was shown how this differentiation, based on the complex of previously selected dynamic indicators characterising the changes of professional vocabulary, expands the possibilities for diagnostics and forecasting of professional growth of students. The author notes that the efficiency of similar intellectual systems is determined not only by the continued database up-dating, i.e. the amount of data in turn influence the accuracy of model of students’ classification and, consequently, the forecast of students’ professional development. Equally important is the improvement of knowledge base, which contains the criteria of professional development and complies with the requirement of basic dictionaries relevance. In addition, supportive procedures should be carried out with participating of the representatives of professional community.Practical significance. The information support provided for the management of professional development of students can be used both for operational decision making and developing content and technologies for educational process. This means students can evaluate the dynamics of own performance in comparison with earlier works, classmates’ work, target indicators of the use of general scientific and professional terminology. This information management component allows teachers to monitor the content of texts and easily determine the authorship of content of learner’s general frequency vocabulary and the dynamics of its change. The representatives of labour market along with access to information on the current progress of a student can define his or her prospects as a future worker. Heads of educational programmes, university administrators receive objective information about the content of disciplines as their study is reflected in the students’ professional development.