Self-assessment activities as factor for driving the learning performance

Abstract

Machine learning proposes innovative methods for students' learning analysis and new ways for modeling the learning process and its realization. Learning analytics takes advantage of this fact and processes data according to accepted or emerging algorithms that leads to creation of analytical and predictive models. Learning performance is connected to a set of behavioral activities in educational environment concerning improvement of knowledge and skills. It is a very important criterion for students' progress and for the formation of the final students' outcomes. For achieving better learning performance, the activities should lead to the learning optimization in context of time duration, educational tasks organization, content presentation and management. Activities that support learning are oriented to self-dependent and self-regulated learning as well as socially-oriented and group-driven learning. The aim of the paper is to present an exploration focusing on the influence of self-dependent activities in the form of self-assessment on learning performance. An experiment is conducted with students who have had the possibility to direct and organize their self-assessment activities in the learning management system. Self-assessment activities are not graded and they are not included in the formation of the final course mark. The students' behavior is traced during one semester and machine learning algorithms are utilized to analyze the quality and quantity of the taken self-assessment activities. On this base analytical and predictive models regarding learning performance and the achieved academic results are created. The patterns and anomalies are outlined and they are used to point out the directions for learning performance and final outcomes improvement.