Investigation of students' profiles and learning approaches in the e-learning environment used in the process of distance education: A learning analytical study

This study aims to examine the learning approaches of students and analyze student profiles using data from students using an e-learning environment. The data entered by students in the developed e-learning environment and the data detected by the system regarding the time spent by students in the online learning environment were stored in relevant databases. Within the created platform, the "Study Approach Scale" was administered to students online, and their learning approaches were automatically determined by the system. The relationship between the identified learning approaches and the data recorded in the database was examined using educational data mining methods. The sample of the study consisted of 726 high school students registered in the system. The students used the online learning environment prepared to access course materials, take exams, and complete the survey. In this environment, the students' viewing time of course content and their duration of stay in the e-learning environment were stored in the relevant databases in line with the research objectives. The data were initially collected from the instructor panel of the developed e-learning environment, and data preprocessing was performed to prepare the data for analysis according to educational data mining techniques. Data mining methods, including classification analysis, were utilized in line with the objectives of this study. Based on the findings, it was determined that the time spent by students in the system and in the class affected their achievement, while student profiles and learning approaches did not have a significant impact on achievement. The decision tree resulting from the classification analysis contained four nodes: Time spent in class, grade level, time spent in the system, and learning approach. After creating the decision tree, hidden rules were established within the data.