One of the acquisitions of Digital First project, is that learning analytics offers a powerful opportunity to rethink not only how students learn ICT, but also how teachers teach it. At the centre of this transformation lies a shift towards a more responsive, evidence-based, and human-centred pedagogical practice, that the project has tried to pursue and put into action with its educational tools.
Our project recently hosted an engaging webinar titled ‘Learning Analytics in Action: Turning Learning Data into Better Education.’ While we encourage you to check out that online discussion, we also want to highlight how data analysis in education can support teachers beyond just ICT lessons.
One of the most significant contributions of learning analytics is the possibility for teachers to use learning data to identify patterns in student performance. Instead of relying exclusively on end-point results or subjective impressions, educators can observe how learning unfolds over time. This includes recognising recurring difficulties, understanding how different students approach the same task, and identifying where conceptual understanding begins to break down. Such insights allow teachers to detect misconceptions early, often before they become deeply rooted, enabling timely and targeted interventions that can significantly improve learning outcomes.
Equally important is the capacity of learning analytics to support real-time adaptation of instructional strategies. When teachers are equipped with continuous feedback derived from learner behaviour, teaching becomes a dynamic process rather than a fixed sequence of planned activities. If data indicates that a significant proportion of students are struggling with a specific concept, the teacher can immediately adjust explanations, provide alternative examples, or redesign learning activities. Conversely, when data shows strong understanding, teaching can move more quickly towards deeper or more complex challenges. In this way, learning analytics transforms classroom practice into a more agile and responsive system.
This evolution marks a profound shift towards evidence-based education. Teaching decisions are no longer guided solely by professional intuition or accumulated experience—although these remain essential—but are increasingly supported by concrete, real-time insights into how students actually learn. The combination of pedagogical expertise and data-informed feedback strengthens the quality of decision-making and enhances the precision of educational interventions.
Within the Digital First perspective, this development is particularly relevant for ICT and informatics education, where learning processes are often complex, non-linear, and highly dependent on problem-solving strategies. Understanding how students think through digital tasks becomes as important as whether they arrive at the correct solution. Learning analytics makes these cognitive processes more visible, offering teachers a window into student reasoning and engagement.
At the same time, this approach significantly reinforces the evolving role of the teacher. Rather than functioning primarily as a transmitter of knowledge, the teacher becomes a facilitator of learning—someone who guides, interprets, and supports students in their learning journey. The teacher’s role shifts towards orchestrating learning experiences, using data as a compass to navigate diverse student needs and learning pathways.
This facilitative role is not diminished by technology; on the contrary, it is strengthened by it. Learning analytics frees teachers from purely reactive teaching and enables them to focus on higher-order pedagogical functions: supporting critical thinking, fostering collaboration, and nurturing students’ ability to learn autonomously. In this sense, data becomes not a control mechanism, but a tool for empowerment—both for teachers and learners.
Ultimately, within the Digital First vision, learning analytics supports a deeper transformation of ICT education, and coherent with the shift from structural to functional education. It helps build a learning environment where teaching is continuously refined, students are better understood, and education becomes more adaptive, inclusive, motivating, retaining, and thus meaningful.
References and bibliography
Siemens, G. (2013). Learning Analytics: The Emergence of a New Discipline. In The Handbook of Knowledge and Learning Science
Baker, R. S. J. D., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics: A Review of the State of the Art. Journal of Educational Technology & Society, 17(4)
Drachsler, H., & Greller, W. (2016). Accessibility and Transparency of Learning Analytics Data: The Role of the Teacher. Educational Technology & Society, 19(2)
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilizing Learning Analytics for Study Success: A Systematic Review. Educational Technology & Society, 23(2).
Ahn, J., & Yoon, H. (2020). Exploring the Impact of Learning Analytics on Teaching Practices: A Meta-Analysis. Educational Technology & Society, 23(4)
García, I., & Llorente, M. (2021). Data-Driven Decision Making in Education: The Role of Learning Analytics in Improving Teaching and Learning. Journal of Computer Assisted Learning, 37(4)
Baker, R. S. J. D., & Inventado, P. S. (2022). The Role of Learning Analytics in Achieving Educational Equity: A Review of the Literature. Journal of Learning Analytics, 9(1)
González, A., & Santos, I. (2022). Learning Analytics for Personalized Learning: Current Trends and Future Directions. Educational Technology Research and Development, 70(3)