Noble and Wilson Named as Learning Analytics Fellows
Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their instruction, for purposes of understanding and optimizing teaching and learning. The central goal of the Learning Analytics Task Force is to make it much easier for faculty, staff, and students to assess and improve teaching and learning at Michigan.
To explore the challenges of learning analytics, the task force provides support for Exploring Learning Analytics projects. Prof. Noble and Ms. Wilson were selected for their research, which focuses on using quantitative measures of student work habits to predict final course outcomes. They are using CTools log data to build a predictive model of academic performance, as well as discover what online behavior patterns are most indicative of eventual academic success. They are also collecting data on when and how often students in early CS classes compile their code, which will be used to predict academic performance and identify strategic work habits that lead to effective programming.
Prof. Noble’s research interests center on mobile, pervasive, and ubiquitous systems, and projects he has undertaken have included securing mobile devices against physical possession attacks; providing for fairness in collaborative, peer-to-peer storage systems; measuring and modeling network availability and host mobility; and improving access to the information economy in the developing world. In addition to operating systems, distributed systems, and mobile computing, he also has interests in incentive-centered design, the usability of mobile systems, and automotive telematics. Prof. Noble was selected to serve as Associate Dean for Undergraduate Education at the College of Engineering in 2013 and has recently played a leadership role on the Campus IT Council.