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Learning analytics provide institutions with opportunities to support student progression and to enable personalised, rich learning. With the increased availability of large datasets, powerful analytics engines, and skilfully designed visualisations of analytics results, institutions may be able to use the experience of the past to create supportive, insightful models of primary (and perhaps real-time) learning processes. While the opportunities and drawbacks of “Big Data” in the media might have been a bit over exaggerated, current research indicate several interesting but complex challenges. How can we filter the “good” from “bad”, or even ugly analytics:
• What evidence is there that analytics actually helps learners to reach their potential?
• How does the Open University UK use analytics to provide support for students and teachers?
• How can we make learning more personalised, adaptive and meaningful, and what are the implications for Moodle?
Via Glyndwr E-learning, Miloš Bajčetić
