# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 1 Introduction  
Online education has reduced some barriers, including geography and time, allowing learners to access meaningful, high-quality educational content no matter where they are in the world. These changes are significantly altering the educational landscape. Forexample, current efforts to expand alternative pedagogy through adaptive learning, better virtual classrooms, and performance development based on data are increasing student engagement and support. At the same time, Massive Open Online Courses (MOOCs) have become a key part of modern e-learning [1], which has altered the educational landscape into one of broader access and democratization of learning experiences [2]. MOOCs offer learners a structured learning experience available free of charge, or at a low cost, to those interested in the learning process, be it students, professionals, or lifelong learners. The Open Classrooms platform explicitly employs mentor-led programs, interactive assignments, real-world projects, and recommendation systems to add value to a personalized learning experience [3; 4]. While MOOCs provide various advantages, they also present several obstacles, which include overall completion rates, a lack of personalized support during learning, varied learner motivations, and no opportunity for interaction with instructors [5]. Learner behavior is fragmented and data-rich in MOOCs and can include several different activities such as watching video lectures, engaging in discussions, and completing assignments [6]. Research suggests that goal-setting and peer engagement (or interaction) are among the strongest predictors of improved completion rates student performance in the course [7]. Additionally, group-level behaviors may either facilitate or inhibit course selection and learning outcomes [8; 9]. Secure online learning is also a necessity, involving policies and practices that establish strong encryption, multi-factor authentication, and strict privacy compliance to protect learner data and establish trust within digital learning contexts [2; 10]. The large amount and variety of MOOCs create challenges for the implementation of a personalized learner analytics process. Predictive models and clustering methods can put learners into categories with respect to their behavior, but their usefulness is limited by the amount of heterogeneity in students and the noisy, unstructured data produced by redundant interactions [4]. Privacy issues often make large-scale data collection difficult in practice, and the data collected must comply with strict ethical and data protection policies. To compound these challenges, many analytics approaches struggle with both scalability and interpretability when dealing with complex learning data. To address these shortcomings, this research integrates process-mining methods with learning analytics to help reveal the sometimes hidden, implicit patterns within MOOC learner data. The study will critically evaluate the current analytic techniques in this context and propose the TSA-Transformer, a new deep learning model architecture that incorporates Temporal Sparse Attention (TSA) and Transformer networks into a single model. This model can filter noise in the data and model contextualized and sequential data from learners' interactions while extracting the underlying implications of learner engagement and dropout risk. Ultimately, a more personalized online learning system can be established for learners. Through TSA-Transformer, it is possible to have personalized interventions that are intended for individual learners, resulting in improved educational outcomes in an MOOC context. This research presents a new TSA-GRU model that is used to understand learner movement in MOOCs. While TSA-GRU is based on known mechanisms such as sparse attention and GRU, it is novel in how the model builds TSA across the sequence dynamically and selectively to focus on the learning moments that are most critical to achieving learning objectives.