## 1 Introduction  
Over the past few years, institutions of higher learning have increasingly adopted information and communication technology (ICT)-based learning approaches, resulting in the generation of vast amounts of educational data. These data, sourced from Learning Management Systems (LMS), Student Information Systems (SIS), video-assisted courses, and other digital learning platforms, offer valuable insights into student behavior and academic performance patterns [1]. However, this influx of data brings new challenges, including data complexity, diversity, and high dimensionality. To make sense of this vast array of information, Educational Data Mining (EDM) has gained recognition as a significant tool, enabling researchers and educators to analyze data and predict student academic success [2]. Educational data mining (EDM) is an increasingly expanding domain that applies data-driven approaches to analyze and improve various aspects of education. With the increasing availability of data from digital learning platforms, student management systems, and online learning behaviors, EDM provides opportunities to uncover patterns and insights that can inform personalized learning, early intervention strategies, and policy decisions [3]. By harnessing advanced approaches including machine learning and data representation, EDM has proven effective in addressing key educational challenges, including dropout prediction, performance forecasting, and engagement analysis. The task of predicting student academic success has become a critical research area, as accurate predictions can guide interventions and support strategies, particularly for students at risk of poor performance or dropout [4]. Traditional predictive models have primarily relied on structured data, such as test scores, attendance records, and demographic information. While these features provide valuable insights, they often oversimplify the multifaceted nature of student performance by overlooking behavioral data, which can reveal deeper patterns of engagement, effort, and learning dynamics. Behavioral data, such as online learning interactions, video engagement, and assignment submission patterns, are inherently unstructured and multidimensional, making them challenging to analyze using conventional methods. As a result, valuable information embedded in these data remains untapped in many predictive frameworks. This limitation underscores the need for innovative approaches that can effectively extract and model behavioral data to enhance the precision and robustness of academic success predictions. Data-driven methods have greatly transformed this field, as machine learning and deep learning techniques now enable the analysis of various data types, including engagement metrics, grades, and participation patterns [5]. However, a persistent challenge in predicting student success is the complexity and high dimensionality of behavioral data collected from online learning platforms. Traditional predictive methods typically rely on one-dimensional numerical or text-based data, which can overlook latent patterns within the data that may significantly influence academic outcomes [6]. In recent research, transforming text-based and numerical behavioral data into alternative formats has shown promise for boosting the effectiveness of predictive architectures. One particularly effective approach involves converting behavioral data into image-based representations, which allows for the application of advanced image-processing techniques from the field of deep learning [7]. By representing behavioral data visually, these methods aim to capture temporal dependencies, correlations, and intricate patterns that might be difficult to identify using traditional linear modeling techniques [8]. Deep learning architectures, particularly Convolutional Neural Networks (CNNs), are highly appropriate for this work. CNNs are highly effective at extracting spatial patterns from image-based data, making them ideal for analyzing the transformed behavioral representations.