Christopher Brooks is an applied Computer Scientist who builds and studies the effects of educational technologies in higher education and informal learning environments. Dr. Brooks has a particular domain focus on data science education and methodological interests in predictive modeling, learning analytics, and collaborative learning. He has published widely in the areas of educational technologies and human computer interaction, and has been awarded several best papers (LAK, AIED, CHI, CSCW) with collaborators. At the University of Michigan School of Information he directs the activities of the educational technology collective (etc), a group of postdoctoral scholars, graduate students, undergraduate students, and other collaborators.
Dr. Brooks is passionate about technical skills instruction, and teaches data science courses in both the graduate and undergraduate levels, most notably in the new Master of Applied Data Science program. In addition to this traditional form of education, Brooks teaches applied data science and computer science courses on the Coursera platform, and has impacted over 30,000 learners through these efforts. In 2017 Dr. Brooks was recognized with the UMSI teaching award, and in 2018 he was part of a team awarded the Coursera Innovation award.
Areas of interest
I work with students and other researchers to better understand a number of phenomena, including:
1. Educational Discourse, both in traditional on-campus cohorts and Massive Open Online Courses (MOOCs). My interest in educational discourse stems from early involvement in the I-Help peer tutoring system, and now focuses on how the nature of discourse changes in different online environments. This work is funded in part by the NSF.
2. Lecture Capture Impact, and how lecture capture tools are used by students to achieve academic success. Much of my previous research in this area focused on clustering based on student usage of the Opencast Matterhorn open-source lecture capture system (which I am a committer on) in second year STEM courses. I am particularly interested in large lecture capture usage datasets and how the use of annotation tools impact student outcomes.
3. Predicting the Academic Success of Students, through semi-automated learning of models based on student interactions with e-learning systems instead of the use of domain, pedagogy, or content specific modelling approaches. I am particularly interested in contrasting this with more traditional methods of learning analytics which are demographic in nature.
4. Massive Open Online Courses, and understanding how alumni and students in traditional higher education use these systems to augment their education. This work is supported in part by the USE Lab and the University of Michigan office of Digital Education and Innovation, and I maintain an open repository of tools available for researchers who are doing analysis on the Coursera MOOC platform. I am actively looking for students who want to contribute to this work.