# Analyzing Student Behavioral Patterns in MOOCs Using Hidden Markov Models in Distance Education
## 1 Introduction
A model has been employed, which is defined by the unified theory of acceptance and use of technology (UTAUT), to determine the acceptance of MOOCs in higher education in Saudi Arabia. Computer self-efficacy, social influence, and performance expectancy are important. MOOCs play a crucial role in the labor market. They enable jobsekers to acquire state-of-the-art knowledge without the barriers of location and with flexibility. In [1], the authors address questions like whether mechanisms are available to increase the effectiveness and persistence of e-learning and how students perceive formal e-learning experiences. The findings highlight the significance of interactions between learners and teachers as well as the role of open dialogues facilitated through cloud meetings. These are available through cloud meetings. The study found that these dialogues and interactions through cloud meetings enhance learning effectiveness by simplifying the learning process and making it more accessible. In [5], there is a study about the relationship between the mediating effect of student-intrinsic motivation and academic commitment. It also analyzes the connection between the support provided to students during online learning and their academic performance. The familiarity of modern students with digital technology and online learning has contributed to the growing adoption of this approach in higher education institutions, driven by rapid technological advancements. Online educational platforms have become essential tools in supporting this process of teaching and learning. The findings in [5] emphasize the need for effective support in online learning. They also underline the critical role of intrinsic motivation and academic engagement as the key to academic success. Data for this study were collected through questionnaires, further demonstrating the importance of fostering motivation and engagement to enhance student outcomes in e-learning environments. In this paper, we attempt to interpret the clickstream data from a large dataset such as those generated by a MOOC. We represent student behavior as a sequence of latent states. The observed states are the grades, and we consider them as the learning outcomes. To uncover hidden knowledge within the MOOC dataset, we employ a two-layer Hidden Markov Model (HMM). Student behavior is influenced by numerous factors; therefore, we chose not to adopt the methods outlined in [6]. Instead, we pursued the proposed representation using the two-layer HMM. Our observations indicate that the behavioral patterns revealed by this methodology are significant, as demonstrated by quantitative indices obtained from training the 2-layer HMM. The methodology Is unsupervised, allowing It to be Iy applied to other datasets without requiring extensive effort at the sequence level. Consequently, this approach empowers tutors and instructors to efficiently interpret the behaviors revealed by the two-layer patterns exhibited by students in MOOCs. HMMs are used to represent the dependencies among variables. Traditionally, they were applied to problems with inherent temporality, such as speech recognition and gesture recognition. Presently, HMMs are used to approach several other problems, and in our study, we employ them to analyze student behavior. Specifically, we aim to bridge the research gap of resource effectiveness in MOOCs and the interaction among students as a behavior model. The transition between resources is defined as a behavior model. We address this issue by analyzing the student participation in the resources of the course and by considering the students and the resources in which they participate as a hidden sequence. The impact of this interaction and participation is measured by the grades achieved on the final exams, which serve as the visible sequence. In addition, we aim to identify the most probable sequence of transition among hidden states.