The field of learning analytics is an interdisciplinary field that brings together researchers from education, learning sciences, computational sciences and statistics, and all discipline ­specific forms of educational inquiry. Michigan has a rich history being a leader in this field through activities such as cross-disciplinary laboratories (e.g. the Learning Education and Design (LED) Lab), the Student Learning and Analytics at Michigan (SLAM) seminar series arranged by CRLT, and the Michigan Data Science Initiative. Despite this, there are gaps in the support of UM learning analytics researchers with respect to the building of technical skills, sharing knowledge of educational datasets, and facilitating collaborative investigations.

To bridge this gap we created the Academic Innovation at Michigan Analytics workshop series (AIM Analytics) as a catalyst for serving these needs for graduate students, postdoctoral fellows, and faculty in the area. With a particular emphasis on technical competencies and skills, we interact deeply with researchers from the departments of Computer Science, Statistics, and the School of Information, as well as the Michigan Data Science Initiative more broadly, in the building of experiences intended to enrich the abilities of students and faculty across disciplines. We also engage with students in disciplines where the inferential statistical study of learning is a central method, such as Economics, Education, and Psychology. Bringing these groups together provides opportunities to engage in both multi­ and interdisciplinary skill building, and the connections formed across these disciplines have potential strengthen collaborations and joint grant applications from UM in the area learning analytics.