Using Data to Improve Learning Outcomes
Equip educators, academic leaders, instructional designers, and education professionals with the knowledge and practical skills to collect, analyse, interpret, and use educational data to improve learner achievement, instructional effectiveness, institutional performance, and evidence-based decision-making. Participants will gain hands-on experience using Learning Management Systems (LMS), learning dashboards, AI-powered analytics tools, and educational data visualisation techniques to support continuous improvement in teaching and learning.
What is Learning Analytics? Evolution of data-driven education Why educational data matters Types of educational data Learning analytics ecosystem Global trends and best practices Homework: Participants prepare a reflection describing how data is currently used within their institution and identify opportunities for improvement.
Understanding quantitative and qualitative data Data collection methods Data quality and reliability Educational indicators Data interpretation fundamentals Avoiding common analytical mistakes Homework: Analyse a sample student dataset and identify five meaningful educational insights.
Understanding LMS dashboards Student activity reports Attendance monitoring Assignment tracking Learning progress indicators Course completion analytics Homework: Explore an LMS dashboard and prepare a summary of learner engagement patterns.
Learning outcomes assessment Formative and summative analytics Student engagement metrics Early warning indicators Monitoring learner progression Academic intervention planning Homework: Develop an intervention plan for at-risk learners using sample analytics reports.
Principles of data visualisation Educational dashboards Charts and graphs Interactive reports Visual storytelling Communicating evidence effectively Homework: Create an educational performance dashboard using Microsoft Excel or Power BI.
Evidence-based instructional improvement Personalised learning Differentiated instruction Curriculum improvement Continuous quality improvement Reflective teaching practices Homework: Redesign one lesson using learning analytics to improve student engagement.
AI-powered educational analytics Predictive learning models Adaptive learning systems Intelligent tutoring systems Generative AI for educational insights Human-AI collaboration Homework: Evaluate one AI-powered learning analytics platform and identify its educational applications.
Predicting student performance Dropout prediction Risk identification Student retention strategies Academic success indicators Ethical use of predictive models Homework: Develop a predictive intervention framework for improving student retention.
School performance indicators Teacher performance analytics Programme evaluation Institutional benchmarking Accreditation indicators Quality assurance dashboards Homework: Design a performance scorecard for a school or educational institution.
Educational data privacy Data protection principles Ethical use of learner data Responsible AI Cybersecurity considerations Institutional governance policies Homework: Develop a draft Learning Analytics Policy for an educational institution.
Programme review Integrating learning analytics into institutional practice Capstone project planning Peer review Project mentoring Presentation preparation Homework: Complete and submit the Learning Analytics Project Report.
Presentation of Learning Analytics Project Demonstration of dashboards Evidence-based recommendations Peer review Expert feedback Programme reflection Certification ceremony Progression to advanced specialisations
