DUET Seminar Series
DUET is a DUE Education Talk series sponsored by the Office of the Dean for Undergraduate Education (DUE) and organized by the Teaching and Learning Laboratory in 2012-13. The monthly series emphasizes current research on learning, cognitive psychology, educational technology, educational assessment, among other areas. All members of the MIT and edX communities who are interested in learning more about education are welcome to attend. DUET’s goal is to provide access to educational research that can support individual and institutional efforts to enhance both residential and online student learning.
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Past Speakers (2013)
Mitchel Resnick, LEGO Papert Professor of Learning Research and head of the Lifelong Kindergarten group at the MIT Media Lab, explores how new technologies can engage people in creative learning experiences. Resnick's research group developed the "programmable brick" technology that inspired the LEGO Mindstorms robotics kit. He co-founded the Computer Clubhouse project, a worldwide network of after-school centers where youth from low-income communities learn to express themselves creatively with new technologies. Resnick's group also developed Scratch, an online community where children program and share interactive stories, games, and animations
Director of the Pittsburgh Science of Learning Center
Using big data to discover tacit knowledge and improve learning
Despite our strong sense of conscious awareness of what we know and learn, most of what we know and learn is outside our conscious awareness. One estimate is that the knowledge we are aware of represents only about 30% of what we know. As a consequence, the typical approach of designing educational materials and technologies based on intuition and introspection is limited, even error-prone. Instead, educational design is better based on accurate cognitive models of student knowledge and learning. And accurate cognitive models are best built by combining constraints from computational modeling and from statistical models of student performance data. Online educational technology use is a great source of such data. I will illustrate these points with projects that employ 1) educational data mining and computational modeling to discover better cognitive models and 2) “close the loop” experiments to demonstrate that redesigning personalized online instruction based on these models improves student learning.
Professor Koedinger's background includes a BS in Mathematics, a MS in Computer Science, a PhD in Cognitive Psychology, and experience teaching in an urban high school. This multi-disciplinary preparation has been critical to his research goal of creating educational technologies that dramatically increase student achievement. Toward this goal, he creates "cognitive models", computer simulations of student thinking and learning, that are used to guide the design of educational materials, practices and technologies. These cognitive models provide the basis for an approach to educational technology called "Cognitive Tutors" in which he creates rich problem solving environments for students to work in and provide just-in-time learning assistance much like a good human tutor does.
Koedinger is a co-founder and board member of Carnegie Learning, Inc. and the CMU director of the Pittsburgh Science of Learning Center (PSLC). The PSLC is a $25 million National Science Foundation center that will provide researchers with the "LearnLab", an international resource for creating, running, and analyzing realistic and rigorous experiments on human and machine learning.
Differentiating Four Levels Of Engagement In Learning: The ICAP Hypothesis
Professor Chi will describe the ICAP hypothesis, embedded in a framework that differentiates overt engagement activities. The framework suggests that any learning activity can be classified into one of four distinct categories based on students’ overt behaviors or mode of engagement: Interactive or dialoguing, Constructive or generating, Active or selecting, and Passive or receiving. Based on this framework, the ICAP hypothesis predicts that as students become more cognitively engaged with the learning material, from passive to active to constructive to interactive, their learning will increase. Empirical support for the ICAP hypothesis will be provided by numerous studies in the literature, by studies undertaken in our lab, as well as in classrooms whose teachers we have trained. Limitations of the hypothesis, caveats to its generalizability, and benefits of the hypothesis for research and instructional design will also be discussed. Our immediate effort is the development of an online module to train teachers how to design and improve classroom activities that move students from one level of engagement to a higher level of engagement. If time permits, I will also describe briefly our findings about learning from observing videos of tutorial dialogues.
Improving Learning and Reducing Costs: New Models for Online Learning
Colleges and universities are offering thousands of fully online courses, ostensibly altering centuries-old methods of teaching and learning. Few of these courses, however, make significant improvements in either the cost or quality dimensions of student learning. Instead, they frequently replicate face--to-face pedagogies and organizational frameworks rather than taking advantage of IT's capabilities to design new learning environments. Using examples drawn from NCAT's work with more than 200 colleges and universities, this presentation will discuss new models for online learning that improve the quality of student learning and reduce instructional costs.
Past Speakers (2012)
Variability Matters: Confessions of an Average Learning Scientist
Modern learning science theory and research is starting to transform the way we think about learning. Rejecting the myth of the “average” learner, this approach emphasizes the reality of variability, the systematic influence of context, and the need to understand individual learners. This presentation will discuss what modern learning science tells us about the origins of learning variability, and what this means for the way we design flexible, effective, and scalable learning environments in the age of EdX.
Todd Rose is a research scientist with CAST (non-profit R&D) and a faculty member at the Harvard Graduate School of Education, where he teaches Educational Neuroscience. His work is organized around six themes: human variability; course design and pedagogy in higher education; adaptive learning analytics; interdisciplinary thinking; the synergistic relationship between neuroscience, technology, and design in education; and the application of dynamic systems models to the study of behavior, learning, and development.
Charles Fadel, Founder & Chairman, Center for Curriculum Redesign
What should students learn in the 21st century?
The last major changes to curriculum took place in the late 1800’s as a response to the sudden growth in societal and human capital needs. Technology is now deeply affecting employability via automation and offshoring, so education standards need to be deeply redesigned for the four dimensions of (relevant) Knowledge, Skills, Character, and Metacognition. Adapting to 21st century needs means revisiting each dimension and the interplay between them.
Charles Fadel is founder of the Center for Curriculum Redesign, and author of best-seller “21st century skills”. After two decades in ICT (semiconductors, systems), he is now a visiting lecturer at Harvard and MIT. He holds a BSEE, an MBA, and has been awarded five patents.