Watch a recent seminar by Dr. Teasley, this research team’s Principal Investigator.
With the advancement of data science, we are facing unprecedented opportunities and challenges in understanding the effects of the vast number of learning tools, strategies and electronic learning systems on learning behavior and academic performance. The challenges come from the availability of the enormous amount and types of data on learning behavior, interactions between learners and learning systems, and grades, test scores, competency measures as learning outcomes The opportunities come from our increasing sophistication in processing and interpreting the vast types and amount of data. The research team aims to bring revolutionary changes in our understanding of education and learning through the holistic modelling of learners, impact the development and deployment of effective learning tools and methods, and greatly enhance UM’s research capability in learning analytics.
The holistic models examine the relationship of learner behavior, learner interaction with learning systems, and outcomes, the interconnected semantics as knowledge representation, and data on learner activities, artifacts and achievements. The models are generated by integrating multiple types of learning data and using a variety of data science approaches including inferential statistics, machine learned predictive models, and probabilistic semantic graphs. The learning data used in these holistic models come from different disciplinary traditions, including psychological constructs such as affect and cognition, linguistic features expressed through text, learner interactions with computational systems and artifacts as well as peers and instructional experts. The research team’s extensive experience on processing educational data and research on the effect of learning approaches on academic performance, together with the existing momentum at Michigan in learning analytics, will help fundamentally shift the focus of learning analytics at Michigan away from iterative technique-driven research towards multi- and interdisciplinary connected research.