This workshop explores how learning analytics can reconcile deficit- and asset-based approaches. Deficit-based models focus on identifying and remedying learner shortcomings in order to move them towards a specific learning standard. However, this approach may neglect learners' existing strengths. An asset-based approach may support this, where learners' identities, values, and existing knowledge are considered as assets to their learning. In this workshop, we advocate for a combination of both. We ground our discussion in the data feminism framework, which examines power structures in data design and interpretation. We will delve into three core data feminism principles: examine power, challenge power, and rethink binaries and hierarchies, to construct narratives affirming students' diverse identities.
Our work is informed by the data feminism framework, which takes an intersectional approach to
defining the power structures involved in designing, collecting, and interpreting data [
Examine Power. Power refers to structural privileges or oppressions different groups experience
[
Challenge Power. Asset- and deficit-based data narratives can challenge unequal power structures
by communicating understandings of data that are grounded in the lived experiences of learners. In
learning analytics, this means we can use data to showcase the ways learner identities are
marginalized in the learning space, and their social reality. For example, the work of Stewart et al.
[
Rethink Binaries and Hierarchies. Binaries and hierarchies are necessary to collecting and
analyzing data. This is because data will always simplify the complexity of each learners’ lived
experience and the learning environment. However, as we construct asset-based narratives of learners,
we must consider the ways in which binaries and hierarchies inadequately characterize a learners’
knowledge and experiences. Further, we can reconsider how binaries and hierarchies uphold systems
of oppression, for example by aligning with dominant views of how learners are expected to behave in
a learning environment. For example, categorizing “good” engagement as speaking out loud in class
ignores the ways learners might be processing information non-verbally [
Dr. Angela Stewart is an Assistant Professor in the University of Pittsburgh School of Computing and Information and Research Scientist at the Learning Research and Development Center. Angela conducts research at the intersection of the learning sciences, artificial intelligence, and human-computer interaction. She uses multimodal data to understand students' social and cognitive states, particularly in collaborative STEM learning. She also creates equitable educational spaces by designing technologies that support the agency of students and teachers. Angela applies a culturally-responsive lens to her research, with a particular focus in emboldening Black girls' design of transformative technologies. Angela was named a 2021 - 2022 Emerging Scholar by the International Society of the Learning Sciences, and recently received an NSF Racial Equity in STEM Award for creating an intersectional AI learning ecosystem for Black girls.
Dr. Caitlin Mills joined the Educational Psychology Department at the University of Minnesota as an Assistant Professor in 2022, after spending four years in the Psychology Department at the University of New Hampshire. She received her Ph.D. from the University of Notre Dame in Cognitive Psychology (2016), then spent two years as a postdoctoral fellow at the University of British Columbia (2016-2018) focusing on Cognitive Neuroscience. She is interested in the intersection of cognitive psychology, computer science, and education. Most of her work has focused on mind wandering and engagement- including their relationship to affect, creativity, and learning.
Dr. Stephen Hutt is an Assistant Professor of Computer Science at the University of Denver. His research activities focus at the intersection of Computer Science, Cognitive Science and Learning Science, considering how Artificial Intelligence can be used to support learning and learning technologies. He takes a human-centered computing approach to his work, placing the needs of users as the focal point for research. He is also interested in how AI and learning technology may serve as a force for equity within education and ways in which AI advances may help (or hinder) that goal.
Dr. Alyssa Wise, PhD is Professor of Technology and Education and Director of LIVE, Vanderbilt’s Learning Innovation Incubator. Her research focuses on supporting data-informed decision-making in-situ, with attention to actionability, equity and impact, including mixed-method investigations of how learning practices are being reshaped by new sources of data and the growing availability of new artificial intelligence capabilities.
Fanjie Li is a Ph.D. student at Vanderbilt’s Peabody College and a doctoral researcher with the LIVE Initiative. With a focus on the socio-technical design of learning analytics (eco)systems, her research draws on human-centered design to explore approaches to LA innovation that are not only informed by technical possibilities but also attuned to the human contexts the tool intends to serve and the humanistic aspects of learning.
Dr. Ryan Baker is a Professor in the Graduate School of Education at the University of Pennsylvania. Dr. Baker researches how students use and learn from educational games, intelligent tutors, and other educational software. Drawing on the fields of learning analytics and learning engineering, he develops methods for mining the data that come out of the interactions between students and educational software. He then uses this information to improve our understanding of how students respond to educational software, and how these responses influence their learning. Prior to joining Penn GSE, Dr. Baker was an associate professor in the Department of Human Development at Teachers College, Columbia University. He has been teaching the “Big Data and Education” MOOC for over a decade, with a total enrollment of more than 100,000 students. He has served as founding president of the International Educational Data Mining Society, where he currently serves on the board of directors. He has been co-author on over a dozen award-winning papers and received the Educational Research Award from the Council of Scientific Society Presidents. 3. References