The study we present in this paper explores the use of learning analytics to predict students' performance in Moodle, an online Learning Management System (LMS). The student performance, in our research context, refers to the measurable outcomes of a student's academic progress and achievement. Our research effort aims to help teachers spot and solve problems early on to increase student productivity and success rates. To achieve this main goal, our study first conducts a literature review to identify a broad range of attributes for predicting students' performance. Then, based on the identified attributes, we use an authentic learning situation, lasting a year, involving 160 students from CADT (Cambodia Academy of Digital Technology), to collect and analyze data from student engagement activities in Moodle. The collected data include attendance, interaction logs, submitted quizzes, undertaken tasks, assignments, time spent on courses, and the outcome score. The collected data is then used to train with different classifiers, thus allowing us to determine the Random Forest classifier as the most effective in predicting students' outcomes. We also propose a predictive algorithm that utilizes the coefficient values from the classifier to make predictions about students' performance. Finally, to assess the efficiency of our algorithm, we analyze the correlation between previously identified attributes and their impact on the prediction accuracy.