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This study explores the integration of critical pedagogy with digital literacy, aiming to reconceptualize digital literacy beyond its traditional focus on technical proficiency and operational skills. Traditional digital literacy models emphasize the ability to navigate and use technology, primarily focusing on operational tasks such as managing digital devices, utilizing software, and performing specific functions. However, these models often overlook the broader implications of technology on society, including its role in shaping power dynamics and perpetuating social inequalities. By integrating critical pedagogy, this study argues that digital literacy should not only encompass technical skills but also encourage learners to critically assess the social, political, and cultural implications of digital tools. The critical digital literacy approach promotes social justice, equity, and empowerment by encouraging learners to question, reflect on, and challenge the ways in which technology influences their lives and society at large. Through critical engagement with digital tools, learners are empowered to challenge existing power structures, fostering social transformation. The study discusses how educators can integrate critical digital literacy into teaching practices, moving beyond traditional skill based models by encouraging critical thinking, ethical use of technology, and a deeper understanding of the role digital technologies play in shaping societal structures. Moreover, the study highlights the transformative potential of critical digital pedagogy, proposing it as a tool for educational equity and social change. By reconceptualizing digital literacy through a critical pedagogical lens, this study offers a framework that empowers students to become not just proficient digital users but active participants in societal transformation, contributing to a more inclusive and just digital future. The study also suggests areas for future research, including empirical investigations on the practical implementation of critical digital literacy frameworks across various educational settings and contexts.

Adapting to students’ learning styles is a key factor in enhancing the effectiveness of higher education, particularly in Informatics programs where learning preferences vary widely. This study aims to segment students based on their learning styles using the K-Means clustering algorithm, guided by the VARK model (Visual, Auditory, Read/Write, Kinesthetic). Data were collected from 130 Informatics students, including information on their learning preferences, and processed through normalization techniques. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, and subsequent cluster interpretation was conducted. The results identified three dominant clusters, each representing distinct learning behavior patterns. These clusters were analyzed to recommend tailored instructional strategies for each group. Specifically, Visual learners were found to benefit from graphic-heavy materials, Auditory learners preferred lectures and discussions, Read/Write learners thrived on written content and detailed notes, while Kinesthetic learners responded best to hands-on activities. The findings support the development of adaptive, data-driven teaching approaches that align with the actual learning tendencies of students in Informatics. Moreover, the study demonstrates that the K-Means method is effective in systematically identifying student learning profiles, which can be used to inform instructional improvements. This personalized approach to teaching could significantly enhance learning outcomes by providing students with the most effective educational experiences tailored to their individual learning styles