Algorithms and Analytics: Connecting the Learner and the Learning
October 23, 2019
CERAS #101, Stanford University 8:30am to 4:45pm
Measuring, analyzing, and reporting data about learners, learning experiences and learning programs have all contributed to understanding and optimizing learning experiences.
A wide variety of new media, tools and practices in education continue to expand options for measuring attention and engagement in sensory experiences, as well as learners’ physical, cognitive and emotional responses. These frontiers are moving forward quickly.
To better accomplish learning – both personalized and at scale – many of these measurements, and the feedback they provide, are being instrumented and automated. Even with the consideration of integrative competencies such as critical thinking, decision making and grit in learning objectives, the algorithms driving many learning analytics are yet based primarily on objective functions that derive mainly from content mastery goals.
It’s time to carefully examine which indicators of learning are most meaningful. And which can be used ethically in algorithms for personalized learning and for learning at scale. And how data can be tagged for open exchange.
Join us at Stanford on October 23rd as experts and members in the mediaX community explore the frontiers of learning algorithms and analytics that connect learners with learning.
1. Measuring what matters in learning
2. Designing learning experiences and algorithms for conversation
3. Developing metatags for open exchange
Paid Registration Required (registration opening soon)
mediaX Members, please email Addy Dawes for a discounted registration.
Robert Moore is a research scientist at IBM Research-Almaden, where he examines the intersection of human conversation and technology. He has recently co-authored the book, Conversational UX Design: A Practitioner's Guide to the Natural Conversation Framework. In the past, Dr. Moore has worked as a scientist at Yahoo! Labs and the Xerox Palo Alto Research Center (PARC) and as a game designer at The Multiverse Network. He holds Ph.D., M.S. and B.A. degrees in sociology with concentrations in ethnomethodology, Conversation Analysis and ethnography.
Mark Musen is Professor of Biomedical Informatics at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Dr. Musen conducts research related to intelligent systems, reusable ontologies, metadata for publication of scientific data sets, and biomedical decision support. His group developed Protégé, the world’s most widely used technology for building and managing terminologies and ontologies. He is principal investigator of the National Center for Biomedical Ontology, one of the original National Centers for Biomedical Computing created by the U.S. National Institutes of Heath (NIH). He is principal investigator of the Center for Expanded Data Annotation and Retrieval (CEDAR). CEDAR is a center of excellence supported by the NIH Big Data to Knowledge Initiative, with the goal of developing new technology to ease the authoring and management of biomedical experimental metadata. Dr. Musen chaired the Health Informatics and Modeling Topic Advisory Group for the World Health Organization’s revision of the International Classification of Diseases (ICD-11) and he currently directs the WHO Collaborating Center for Classification, Terminology, and Standards at Stanford University.
Daniel Schwartz is dean of Stanford Graduate School of Education and an expert in human learning and educational technology. Schwartz oversees a laboratory whose computer-focused developments in science and math instruction permit original research into fundamental questions of learning. He has taught math in rural Kenya, English in south-central Los Angeles, and multiple subjects in Kaltag, Alaska. This diversity of experience informs his work. Among many honors, Schwartz was named Graduate School of Education Teacher of the Year for 2015. His latest book, The ABCs of How We Learn: 26 Scientifically Proven Approaches, How They Work and When to Use Them, distills learning theories into practical solutions for use at home or in the classroom. NPR noted the book among the "best reads" for 2016.
Roy Pea is the David Jacks Professor of Education and the Learning Sciences at Stanford University, Co-Founder and Faculty Director of the H-STAR Institute, Director of the PhD Program in Learning Sciences and Technology Design, and Professor, Computer Science (Courtesy). Since 1981, Dr. Pea has been exploring how information technologies can support and advance the scientific understanding and practices of learning and teaching, with particular focus on topics in science, mathematics, and technology education and their associated symbolic and communicative interchanges that are integral to learning. His current work is examining how informal and formal learning can be better understood and connected, and developing the DIVER paradigm for everyday networked video interactions for learning and communications. Other current research includes the influence of point of view on video-supported learning and collaboration; precollege mobile science inquiry and learning with sensors;and informal math learning in families.