Identifying Digital Literacy Profiles in Distance Education: A K-Prototypes Clustering Approach

Arman Haqqi Anna Zili; Made Diyah Putri Martinasari; Selly Anastassia Amellia Kharis

doi:10.37905/euler.v13i3.34568

Identifying Digital Literacy Profiles in Distance Education: A K-Prototypes Clustering Approach

Arman Haqqi Anna Zili, Made Diyah Putri Martinasari, Selly Anastassia Amellia Kharis

Abstract

Education quality is one of the main focuses of Indonesia’s Sustainable Development Goals (SDGs), particularly in the goal that emphasizes equitable access and lifelong learning. Universitas Terbuka (UT) is a higher education institution that implements an open and distance learning system. This setting creates a diverse student body in terms of age, occupation, and digital literacy levels. Segmenting students based on their digital literacy is both essential and challenging, as it involves combining demographic data with daily digital behavior. This study aims to identify the digital literacy profiles of UT students using cluster analysis with the K-Prototypes algorithm. Data were obtained from a survey of 10,396 students with 42 variables. The Elbow Method analysis revealed three distinct clusters, each reflecting unique engagement profiles. The first cluster, the Engaged Evening Digital User, is active during the evening and balances work with social activities. The second cluster, the Hyper Connected Communicator, relies heavily on messaging applications for social interaction. The third cluster, the Balanced Digital Citizen, shows a more even distribution of digital use across academic, entertainment, and communication activities. These clusters predominantly comprise Generation Z individuals, many of whom are actively engaged in the private sector. The profound implications of these findings lie in their capacity to forge highly targeted strategies for digital learning, communication, and student support, thereby enhancing educational outcomes. Furthermore, this research significantly advances methodological literature by demonstrating a powerful, integrated approach to clustering mixed-type attributes, offering a more nuanced understanding of learner profiles in distance education.

Keywords

Cluster Analysis; K-Prototypes; Machine Learning; Behavioural Segmentation

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DOI: https://doi.org/10.37905/euler.v13i3.34568