CMU’s LearnLab Experts Present Education Research Accomplishments at NSF Meeting-CMU News - Carnegie Mellon University

Tuesday, February 16, 2016

CMU’s LearnLab Experts Present Education Research Accomplishments at NSF Meeting

By Shilo Rea / 412-268-6094 / shilo@cmu.edu

The National Science Foundation (NSF) recently held a three-day conference to celebrate the achievements of its six Science of Learning Centers. Key members from each center, including Carnegie Mellon University and the University of Pittsburgh’s LearnLab, presented their educational research accomplishments to underscore the importance of establishing a sustainable science of learning community to produce breakthroughs that impact education.

LearnLab, which leverages cognitive theory and computational modeling to identify the instructional conditions that lead to robust student learning, was represented by CMU’s Vincent Aleven, David Klahr, Ken Koedinger and Carolyn Rose, as well as Pitt’s Tim Nokes and Lauren Resnick.

NSF Logo

“The NSF created the Science of Learning Centers program in order to bring top researchers from many diverse fields together and provide them with the resources to deepen our understanding of learning,” said Fay Cook, NSF assistant director for social, behavioral and economic sciences. “Over the past 10 years, these centers have addressed important questions and gaps in our knowledge of the process of learning — questions that are complex in scope and scale, and that required infrastructure and human capital beyond what small individual research groups could provide. This event was an opportunity for the scientific community to learn from this transformative program.”

Koedinger, professor of human-computer interaction and psychology and director of LearnLab, outlined how the joint CMU-Pitt center has facilitated more than 360 live, cross-domain classroom experiments. By demonstrating successful instructional interventions and using fine-grain process data, the experiments revealed insights into the causal mechanisms of implicit and explicit learning processes and the social and motivational conditions that enable them.

“Integrating insights across the experiments allowed us to advance an education-relevant learning theory that culminated in the Knowledge-Learning-Instruction (KLI) framework, which demonstrates how different knowledge goals require different optimal configurations of instructional techniques because they require different primary learning processes, like memory, induction or sense-making,” Koedinger said.

Koedinger and Aleven, associate professor of human-computer interaction, gave a talk on “Improving Learning and Learning Science Through Technology,” in which they expanded on the KLI framework and how it provides empirical and theoretical reasons for why principles of instruction do not straightforwardly generalize across variations in student aptitude and domain content.

“For example, KLI demonstrates how conflicting recommendations from the learning science literature for more ‘testing’ versus more ‘worked examples’ can be resolved in terms of an analysis of how these instructional methods function to facilitate different learning mechanisms needed to achieve different knowledge acquisition goals,” Koedinger shared.

Other LearnLab presentations included “Timing is Everything…Sometimes” by CMU’s Klahr, the Walter van Dyke Bingham Professor of Cognitive Development and Education Sciences, and Pitt’s Nokes, assistant professor of psychology. “Technology-Infused Professional Development: Towards Socializing Intelligence in Urban Classrooms” was presented by CMU’s Rose, associate professor of language technologies and human-computer interaction, and Pitt’s Resnick, university professor of psychology and cognitive science.

“LearnLab researchers have led the way in developing data-driven learner models that allow us to go beyond reliance on intuitions as students and teachers, on how to optimize learning experiences, said Soo-Siang Lim, program director for the NSF Science of Learning Program. “LearnLab’s foundational work to develop a conceptual framework to systematically investigate the instructional design, combined with Big Data from LearnLab’s DataShop, are important elements of these achievements. DataShop is the world’s largest open repository of educational technology, and LearnLab researchers are pioneering the new and increasing important field of education data mining.”

LearnLab is a partner of CMU’s Simon Initiative, which harnesses a cross-disciplinary, learning-engineering ecosystem that has developed over several decades at CMU with the goal of measurably improving student learning outcomes.

Read “Ten Takeaways From Ten Years of the Science of Learning Centers.”

Find out more about LearnLab.

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LearnLab Summer School