At the recent #LearningAID24 conference in Bochum, Germany, Hendrik Drachsler delivered a keynote that challenged conventional perspectives on Learning Analytics and AI in education. He opened the discussion by examining the often ambiguous boundary between Learning Analytics and AI in education, posing a thought-provoking question: are these two areas truly distinct, or do they converge into one shared domain?
Beyond theoretical discussions, Hendrik presented early empirical findings from the research program on Highly-Informative Learning Analytics (HILA), advocating for a more evidence-based approach to integrating Learning Analytics and AI into education. The goal, he argued, should be to ensure that these technologies effectively meet the informational needs of learners, providing meaningful and actionable insights.
He framed the 2nd conference day around the theme: INFORMED PRECISION – This concept captures the need for careful, evidence-based, and context-aware application of AI and Learning Analytics in education, emphasizing precision in design and implementation, which he believes is urgently needed amid the current hype surrounding AI in education.
What is Highly-Informative Learning Analytics (HILA)?
In his keynote, Hendrik Drachsler introduced his vision of Highly-Informative Learning Analytics, a research programme that seeks to ensure that Learning Analytics and AI applications address learners’ needs in a comprehensive and useful way. This approach has been developed in collaboration with interdisciplinary experts from psychometric, feedback theory, and learning design, and is currently being tested in experimental studies across higher education and school contexts.
The HILA-Manufacture: Data-Enriched Learning Activities
One of the program’s key innovations is the HILA-Manufacture, using an evidence-based design methodology called FoLA for creating Data-Enriched Learning Activities (DeLA). These DeLAs are flexible, allowing them to be adapted to a wide range of educational scenarios, while also serving as stable conditions for collecting data. This data, in turn, generates HILA feedback, providing valuable insights into which factors support or hinder effective learning across diverse contexts.
2024_Keynote_LearningAIDLooking Ahead: Evidence-Based Learning Analytics
The ultimate aim of the HILA program is to build an evidence-based knowledge foundation for the conditions under which AI applications in education can be most effective. To close his keynote, Professor Drachsler offered a deep analysis of the societal, technological, and pedagogical challenges that come with implementing Learning Analytics and AI. He emphasized the importance of using rigorous, empirical research to guide these implementations, ensuring that they contribute meaningfully to the educational landscape.
Professor Drachsler’s keynote not only highlighted the critical need for a stronger evidence base in Learning Analytics but also showcased the potential of AI to revolutionize education when applied thoughtfully. As the field continues to evolve, his work serves as a call to action for educators and researchers alike to embrace a more informed, data-driven approach to improving learning outcomes.
#LearningAID24 #LearningAnalytics #KünstlicheIntelligenz #Hochschulbildung #EdTech #HILA #KI