Three questions have stirred controversies, fueled perpetual debates, and polarized educators, parents and students for as long as educational institutions have existed: What constitutes good teaching? What are effective learning practices? And, perhaps most importantly, what does “student achievement” mean? Attempts to answer these questions have evolved from philosophical musings to data-based modern educational research. Today, with Big Data research in learning and education taking flight, we are ready to unravel the secret of successful learning and teaching by leveraging massive and complex learning data and incorporating a large number of factors that differentially influence individual students’ learning success.
The MIDASLearning Analytics Research Hub is a new addition to U-M’s nationally renowned research in learning analytics, propelled by the Learning Analytics Task Force launched in 2012. The two initial core research projects at the Hub are funded by MIDAS’ Challenge Initiative. One project, ” LEAP: analytics for LEarners As People,” led by Rada Mihalcea in the College of Engineering, will create a new generation of learning analytics tools to understand the link between learning success and personal attributes such as values, beliefs, interests, behaviors, background, and emotional state. The other project, ” Holistic Modeling of Education (HOME),” led by Stephanie Teasley in the School of Information, will build a holistic learning model based on existing data and knowledge on the relationship of learner behaviors and outcomes, the interconnected semantics that make up knowledge, and evidence-based representations of learning. In addition to these two projects, the MIDAS Learning Analytics Research Hub is incorporating other research projects as the opportunities arise.
Starting from the initial set of research projects, the continuing efforts of the Hub will focus on:
- Disseminating tools and methods, as soon as they become available, to empower campus-wide learning analytics research.
- Building a collaborative network of Big Data learning analytics researchers, and helping UM stay at the forefront of learning analytics research in the nation.
- Forming industry partnerships and transform research findings into the next generation of learning systems.
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