Exploring students' learning processes by logging and analyzing their interaction behavior in a Virtual Reality learning environment

Educational researchers have done remarkable work in analyzing the impact of VR on education and measuring the learners' experience, engagement, motivation, etc of using VR in education. Most of the studies conducted reveal that VR in education has a positive impact as the learners immersively experience the VRLE and interact with the virtual objects in the first-person perspective. Certain experiments conducted with VR also claim that using VR in education increases the presence but decreases learning due to an overload of extraneous cognitive load. Due to the contradictory claims made by different authors on the use of VR in education, it has become important to understand the learning processes happening while using VR. The existing studies conducted to understand the learning processes in VR have considered the cognitive factors, and affective factors leading to the learning outcomes. Those studies have used the data collected from pre-tests, post-tests, self-reported questionnaires, interviews, surveys, physiological devices, and human observers. However, no study has considered the data related to the behavior of the learners due to interacting with a Virtual Reality learning environment (VRLE) to understand the learning processes. This is due to the non-existence of an efficient data collection mechanism that is able to log all the interaction behavior of the learners. Hence, we developed a data collection mechanism that is able to log automatically all the interaction behavior of the learners in real time along with timestamps. We also conducted a study with 14 participants by deploying the developed data collection mechanism in a VRLE. The purpose of this doctoral thesis is to understand the learning processes happening in VRLE from the lens of the interaction behavior of the learners. The analysis done on the data collected can also be further used to predict the learners' performance based on their interaction behavior