Automating Learning Analytics Dashboards: A Data-Driven Approach to Tracking Digital Literacy Progression
Keywords:
Learning Analytics, Digital Literacy, ETL Pipeline, K-Means Clustering, DashboardAbstract
As cyber-learning environments become increasingly complex, the manual assessment of digital literacy progression presents a significant bottleneck for educators and researchers. This paper proposed a high-performance, automated Learning Analytics Dashboard (LAD) designed to track and visualise digital literacy development in real-time. Utilising a data-driven architecture, the system integrated a multi-layered ETL (Extract, Transform, Load) pipeline to ingest raw interaction logs from game-based pedagogy platforms and AI-supported learning tools. The methodology employed a hybrid approach, combining Rule-Based Classification with unsupervised Machine Learning (K-Means Clustering) to categorise user behaviours into proficiency tiers based on the European Digital Competence Framework (DigComp). The technical implementation focused on low-latency data processing and the synthesis of teknolinguistics metadata to provide actionable insights. Preliminary results indicated that the automated dashboard significantly reduced the latency between data generation and pedagogical intervention, achieving a 92% accuracy rate in identifying struggling learners compared to traditional manual assessments. This study contributes to the field of IT in education by providing a scalable framework for longitudinal tracking of digital competencies, ensuring that interdisciplinary scholarship in language and technology is supported by robust, empirical data visualisations.
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