Virtual Reality (VR) has been found useful to improve engagement and retention level of students, for some topics, compared to traditional learning tools such as books, and videos. However, a student could still get distracted and disengaged due to a variety of factors including stress, mind-wandering, unwanted noise, external alerts, and internal thoughts in mind. Thus, distractions could be classified as either external (due to the environment) or internal (due to internal thoughts). Student eye gaze data could be useful for detecting externally distracted students. Gaze data-based visualizations have been proposed in the past to help a teacher monitor distracted students. However, it is not practical for a teacher to monitor a large number of student indicators while teaching. To help filter students based on distraction level, we propose an automated system based on machine learning to classify students based on their distraction level. We divided our research problem into two sub-problems. Firstly, we created a labeled eye gaze dataset from an educational VR environment, and we propose an automatic system to gauge a student's distraction level from gaze data. Then we apply and compare several classifiers for this purpose. Additionally, a personalized machine learning model was found to improve the classification accuracy significantly. Secondly, we consider adding EEG data along with eye tracking data so that the combination of EEG and eye gaze data can help to detect internal and external distractions. Therefore, we collected EEG and eye gaze data of participants using educational VR environment, and then trained various machine learning and deep learning models and we found that the overall accuracy of the machine learning model for detecting internal and external distraction was over 90%. Based on these findings, in the future, we will evaluate real-time distraction detection system and guide students through audio voice or controller alerts through the VR system automatically when the system detects distractions. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]