In blended learning environments (BLE), the approach to education is a flipped classroom, where content is provided through the Internet in advance before the commencement of the class [4]. The flipped classroom enables students to come to the class prepared, and the educator uses activities in the class that can clear the doubts of the students, as well as helping them in their assessments and the concepts learned from the content provided to them in the form of discussions or activities. The content that is usually provided by the educator for the flipped classroom includes reading along with questions and answers, created video lectures, demonstration videos, an online class discussion room, lecture slides, tutorials and reading from textbooks or reference books [5].  
The adoption of flipped classrooms by higher education institutions (HEI) has rapidly increased in recent years [6]. The crucial factor in flipped classrooms is the electronic support that the HEI uses to disseminate knowledge among the learners. Encouraging learners to learn at their own pace, including through video lectures, frees up classroom time for more applied learning or active learning. Educators take support from various educational technologies to replicate the virtual classroom.  
At Middle East College (MEC), different systems are in place to store student information. The student information system (SIS) stores the related academic data; the learning management system (LMS)--i.e., Moodle--is used as an e-learning tool to disseminate knowledge at the same time as online behavior is stored in the logs for each student; and the video streaming server (VSS) is used alongside Moodle to share video lectures to the students [7]. As mobile culture is on the rise, an in-house built mobile application--eDify--has been developed to share video lectures to students since spring 2019, enhancing the teaching and learning process. With the use of eDify, the collection of students' video interactions was made easy, providing valuable information about the learners. The data which these systems hold about the learners can be useful for enhancing the teaching and learning process. Learning analytics (LA) help HEIs to gain useful insights about their learners interacting with the system [8; 9; 10; 11]. When videos are used to provide useful information about the learners, it is called video learning analytics (VLA) [12; 10; 13; 14].  
The study aimed to use systems such as SIS, LMS and eDify to explore students' overall performance at the end of the semester. Education data mining is used to elucidate this issue as this uses predictive analysis on the data [8; 15; 16]. The majority of the research suggests that the classification model can be used to predict success in HEIs using data related to student profiles and data logs from Moodle [17; 18; 19]. Similarly, research has been carried out to predict student performance using videos, but little work has been done on using both systems to predict the students' academic performance. The study attempts to predict student performance in HEIs using video learning analytics and data mining techniques to help faculty, learners and the management to make better decisions.  
The contribution of this paper is threefold: (1) when the classification model is formed using interaction data from different systems used in the learning setting, we determine which algorithm and preprocessing techniques are best for predicting successful students; (2) we determine the impacts that feature selection techniques have on classification performance; and (3) we determine which features have greater importance in the prediction of student performance.  
This paper is organized as follows: Section 2 presents a literature review and an overview of related works in this field. Section 3 presents the methodology used in the study. Section 4 presents the results. Section 5 presents the discussion on the study. Finally, Section 6 draws a conclusion and proposes future research.