This research discusses the use of Augmented Reality, Virtual Reality and Mixed Reality technology applications in the learning process of relevant content to the Computer Science area. This systematic review aims to identify applications that use technologies to represent virtual environments and support the teaching and learning of Computer Science subjects. A protocol was elaborated and executed, resulting in the final selection of 14 papers from four databases, published from 2010 to 2018. The examined papers presented information that categorized technology applications in terms of tools used. Contents addressed to the identification of applied instructional strategies and techniques, and the recognition of effects on the learning process. As a result, we found virtual environments that show potential to teaching basic content in courses related to Computer Science. In addition, the application of virtual environments in this educational scenario has provided positive effects on the learning process, such as increased interactivity, easier content absorption, increased motivation and interest in the subjects, providing greater understanding and improving efficiency in content transmission.
We investigate the possibility to apply a known machine learning algorithm of Q-learning in the domain of a Virtual Learning Environment (VLE). It is important in this problem domain to have algorithms that learn their optimal values in a rather short time expressed in terms of the iteration number. The problem domain is a VLE in which an agent plays a role of the teacher. With time it moves to different states and makes decisions which regarding action to choose for moving from current state to the next state. Some actions taken are more efficient than others. The transition process through the set of states ends in a final (goal) state, one which provides the agent with the largest benefit possible. The best course of action is to reach the goal state with the maximum return available. This paper introduces a way of definition of a rewards matrix, which allows the maximum tolerance for the changes of a discounted reward value to be achieved. It also proposes way of an application of the Q-learning that allows a teaching policy to exist, which maps the situation in the learning environment.