interaction with instructors. Despite their adaptability and general outreach, MOOCs and other extensive virtual courses regularly face challenges with low completion rates. Research indicates that fewer than 13% of students finish these courses, with only 2-10% achieving the course objectives and receiving certificates [4]. The issue of student dropout has collected significant research interest, leading to various interpretations and proposed solutions. Some researchers attribute dropout to students' lack of motivation, commitment, and initial intent [5]. Conversely, others emphasize the necessity for high autonomy, which can promote feelings of isolation among participants [6]. Researchers have proposed numerous adaptive, personalized, and recommendation strategies to reduce dropout rates by considering learning pathways [7], prior knowledge [8], skills [9], learning styles [10], and language support [11]. Additionally, innovative pedagogical approaches such as enquiry-based learning have demonstrated potential in enhancing student engagement and persistence in online learning environments [12].  
Educators can implement retention and academic support strategies more effectively when they identify students at risk of dropping out early in an online course. Although researchers have introduced various methods for predicting student performance [13; 14; 15; 16; 17; 18; 19; 20; 21], these studies typically use data from courses that follow a strict sequence and schedule for lessons and assessments. Conversely, our research concentrates on what we refer to as \"non-linear\" virtual courses, also identified as \"learner-paced\", \"self-paced\", or \"self-study\" [22], where students can navigate through the materials at their speed and in any preferred order. Assignments may be completed at any time and in any sequence. Given that not all course assessments carry the same weight, these non-linear virtual courses present a unique challenge in the early prediction of student performance, as the percentage of assessments completed at any specific moment can vary significantly among students.  
Studying non-linear or self-paced courses presents challenges different from those in traditional instructor-paced environments. In these courses, students control the sequence and timing of their activities, leading to highly variable engagement patterns. While beneficial for accommodating diverse needs, this flexibility makes monitoring progress and predicting outcomes more difficult. As self-paced learning becomes more common, developing methods to understand and support student success in these settings is increasingly critical.  
Given this context, our contributions and findings are as follows:  
* An adaptive feature extraction method tailored for interaction data from non-linear virtual courses. We introduce the concept of a Feature Aggregation Time Point (FATP), where features are extracted based on the student's Cumulative Weight Assessment (CWA).
* Feature engineering from the interaction logs of 663 students in a non-linear Moodle course. We define engagement, behavior, and performance features.
* Multiple experiments on student performance prediction that show the predictive power of our features and the effectiveness of our feature extraction method.
* A clustering and correlation analysis of students' online learning patterns that provide insights into the relationship between students' interactions with the online course and students' performance.
* A feature importance analysis that reveals key interaction features that significantly impact the model's accuracy in predicting student performance, and that can improve the identification of interaction patterns through clustering analysis.