Chapter 7: Research and Development 7. Research and Development Policy relies upon research-based knowledge; likewise, improving practice depends on feedback loops that analyze empirical evidence. Consequently, the 2010 NETP specified a series of “grand challenges” which were “R&D problems that might be funded and coordinated at a national level.” One 2010 NETP grand challenge was to create personalized learning systems that continuously improve as they are used: “Design and validate an integrated system that provides real-time access to learning experiences tuned to the levels of difficulty and assistance that optimize learning for all learners and that incorporates selfimproving features that enable it to become increasingly effective through interaction with learners.” Since 2010, much R&D has addressed this challenge. Conferences about learning analytics, educational data mining, and learning at scale have blossomed. Developers have created platforms that use algorithms and the analysis of big data to tune learning experiences. The challenge has not been fully achieved, and further work on this challenge is still relevant today. 7.1. Insight: Research Can Strengthen the Role of Context in AI Despite the relevance of 2010’s grand challenges, it has become apparent that the R&D community is now looking to expand their attention. The 2010 challenges were stated as technical problems. Today’s researchers want to more deeply investigate context, and today’s tech companies want to develop platforms that are responsive to the learners’ characteristics and situations more broadly—not just in terms of narrow cognitive attributes. We see a push to transform R&D to address context sensitivity. 72 | P a g e
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