Exploring Factors and Indicators for Measuring Students' Performance in Moodle Learning Environment

One of the most important pillars of smart cities is the smart learning environment. This environment should be well prepared and managed to improve the instruction process for instructors from one side and the learning process for students from the other side. This paper presents the student's Engagement, Behavior and Personality (EBP) predictive model. This model uses Moodle log data to investigate the influence and the effect of the students' EBP factors on their performance. For this purpose, this paper uses the data log files of the "Search Strategies on the Internet" online course in Fall 2019 at Sultan Qaboos University (SQU) extracted from Moodle database. The intention of conducting this kind of experiments is of three-facets: 1. to assist in gaining a holistic understanding of online learning environments by focusing on student EBP and performance within the course activities, 2. to explore whether the student's EBP can be considered as indicators for predicting student's performance in online courses, and 3. to support instructors with insights to develop better learning strategies and tailor instructions for personal learning of individual students. Moreover, this paper takes a step forward in identifying effective methods to measure student's EBP during the learning process. This may contribute to proposing a framework for the smart learning behavior environment that would guide the instructors to observe students' performance in a more creative way. All the 38 students who participated in this experiment had compatible statistics and results as the relationship between their Engagement, Behavior, Personality was symmetric with their Performance. This relationship was presented using a group of condition rules (If-then). The extracted rules gave us a straightforward and visual picture of the relationship between the factors mentioned in this paper.