This Collaborative Enhancement Project promotes the innovative use of learner analytics to improve student engagement. The evolving landscape of higher education demands innovative ways to enhance student engagement beyond attendance and in-class participation. Traditional methods fall short in large-group, online, or blended learning. Learning analytics - measuring and analysing learner data - can offer personalised interventions, early warnings, and improved outcomes.
Project lead: New Uzbekistan University
About this project
Higher education is rapidly evolving, requiring new approaches to student engagement and learning experience.
Traditional methods- attendance, participation, and assessment - remain common but are inadequate in large-group or online teaching. Learning analytics, defined as the measurement, collection, analysis, and reporting of learner data, offers more effective solutions. By tracking engagement with resources, identifying areas for improvement, and providing early warnings, it enables tailored interventions that enhance outcomes. Despite strong evidence of its benefits, adoption remains limited. This project seeks to address these challenges by promoting best practice in the use of learning analytics to improve engagement and optimise learning environments.
The ever-changing landscape of higher education requires innovative approaches to student engagement and enhancing the student learning experience. Traditionally, student engagement has been assessed through attendance records, participation in in-class activities, and homework submissions. These methods are still commonly used at universities where adoption of education technologies is limited. Efforts to enhance the student experience tend to focus mainly on in-class and on-campus activities.
Traditional methods are not effective in large group teaching or in courses where engagement with learning resources happens mostly online and attendance is not compulsory. Large group teaching requires a more innovative approach that allows instructors to use ‘big data’ on student engagement, learning, and the learning environment. This kind of educational analytics can be mined to create individualised interventions.
Learning analytics involves the measurement, collection, analysis, and reporting of data about learners and their context, to understand and improve learning and learning environments. It includes early warning systems to flag potential issues, helping students achieve their academic goals by identifying areas for improvement, tracking engagement with course materials, and predicting potential obstacles. Lack of engagement and untracked materials can lead to disinterest. Learning analytics estimates aim to improve the effectiveness of educational interventions and student engagement, leading to better learning outcomes. Despite its benefits, adoption of learning analytics remains slow.
This project aims to address these issues and promote best practice in using learning analytics.
Lead institution:
Gizem Uzuner, New Uzbekistan University
Partner institutions:
Ravshonbek Otojanov, Queen Mary University of London
Sanat Chuponov, Mamum University
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