Informatics in Education

Research trends on computational thinking (CT) and its learning strategies are showing an increase. The strategies are varying, for example is using games to provide enjoyment, engagement, and experience. To improve the high level of immersion and presence of game objects, learning strategies through games can be improved by virtual reality (VR) technology and its application. However, a systematic review that specifically discusses game based in VR (GBiVR) settings is lacking. This paper reports previous studies systematically about the strategies used to learn CT through games and VR applications. 15 papers were selected through Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. As the result, this study proposes a conceptual framework for designing a strategy to learn CT through GBiVR settings. The framework consists of critical aspects of variables that can be considered in the learning environment like game elements, VR features, and CT skills. All the aspects are discussed below.

The primary purpose of this study is to investigate CT skills development process in learning environments. It is also aimed to determine the conceptual understanding and measurement approaches in the studies. To achieve these aims, a systematic research review methodology was implemented as the research design. Empirical studies on computational thinking indexed in the Web of Science and ERIC databases were selected without constraint on the publication dates. The studies found were examined and a pre-analysis was conducted by the researchers. Following the pre-analysis, 29 articles were selected to be included in the study. Content analysis was applied in order to determine and evaluate the common codes and themes related to the findings. In conclusion, instead of relying on attractiveness, functionality, market share of educational tools (robotic sets, software packets etc.), availability of qualified learning activities focused on problem solving is the main point practitioners should consider.

In this paper we present our experiences of teaching an annually organized virtual reality (VR) capstone course. We review three iterations of the course, during which a total of 45 students completed the course and 16 VR applications were implemented. Our comparative analysis describes the students' evaluation of the course, the applications created by them, and their development experiences. The results suggest that our gradual improvements on the course and the utilized software paid off, as the latest of the compared course iterations produced the best feedback and the highest quality VR applications. Our learning assessment analysis reveals that our course is effective in teaching VR application development and having students meet their personal learning goals. We also bring forward our RUIS toolkit that was used in the course with success, and present evidence on how better software toolkits can affect the development experience and allow students to create more impressive applications. Finally we share the lessons learned during five years of teaching the course, introducing several practical considerations for VR course organizers regarding pedagogics, software, and hardware.

The article presents the results of an experiment in which Excel applications that depict rotatable and sizable orthographic projection of simple 3D figures with face overlapping were developed with thirty gymnasium (high school) students of age 17-19 as an introduction to 3D computer graphics. A questionnaire survey was conducted to find out whether the students acquired the principles of the projection, found the lessons interesting and contributing to their technological knowledge, and found the topic motivating enough to continue with more complicated models. The results are discussed.

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.