Engagement in Inquiry Learning

As
screenshot1
science education shifts towards focusing on the processes rather than products of science, it is increasingly clear that scientific inquiry should play a major role in science learning. Our understanding of inquiry has improved greatly over the last several decades, and this informs instructional design. However, how does productive inquiry “look” like? Ask teachers whether they know if their students are making progress, and answers will be all over the place. On a nutshell, while we understand the principles behind productive inquiry, we still do not know how to identify one.
This project focuses on identifying productive inquiry in virtual labs. Using the PhET simulations, I study which online behaviours correlate with positive shifts in knowledge, skills, and attitudes. The outcomes of this project, which is joint to teams in UBC, University of Colorado Boulder, and Stanford University, are three-fold. First, we seek to better understand scientific inquiry in the context of learning with virtual labs. Second, we design unobtrusive assessments that can evaluate students’ inquiry processes. Last, identifying productive inquiry allows us to offer adaptive support in virtual labs.

This work is support by the Social Sciences and Humanities Research Council (SSHRC) and the Gordon and Betty Moore Foundation (GBMF).

Learning and Teaching with Massive Open Online Courses (MOOCs)

Higher
MOOC
education is being transformed by MOOCs in ways that are yet to be known. While the discussion around MOOCs focuses on business-models and coolness, it seems that learning was left outside the conversation. In my work I put the emphasis teaching and learning with MOOCs.
My work seeks to understand how learners learn with MOOCs, and how MOOCs can support better learning. What are productive patterns of engagement with MOOCs? What are design guidelines that support better learning in these environments? How can meaningful interactions be added to an online-only course?
A second focus of mine is the effect of MOOCs on on-campus learning. How can MOOCs be used in on-site and blended courses? What elements of classroom teaching can be added to MOOC and augment learning? I believe that the answers to these questions will help us create better and more accessible higher education.

Metacognition and Self-Regulated Learning

Interactive
CogTut
learning environments do a pretty good job at teaching domain knowledge. However, to facilitate meaningful learning, they should do much more than that - they should support students in becoming better life-long learners. This line of work focuses on supporting students in acquiring the skills and dispositions of effective self- and co-regulation.
While working with interactive learning environments, students generate streams of fine-grain data. This data can be used to infer their strategies, attitudes, and motivations. I design and evaluate computational cognitive models of self-regulation and metacognition. These models trigger adaptive support for metacognition and inform theories of learning and instructional design. For example, I have implemented adaptive support for students’ help seeking in a problem-solving environment. Students who worked with the supported environment improved their help-seeking behaviours in a manner that transferred to new, unsupported, activities in a new topic.

This work is supported by the National Science Foundation and the Pittsburgh Science of Learning Center.

Productive Failure and Invention Activities

All
best fit, weighted uncertainty
too often learning is mistaken for the ability to solve practice problems correctly. However, learning requires the ability of students to transfer their knowledge to new situations and to expand and adapt to different situations. Studies in Productive Failure show that inviting students to invent solutions to novel problems prior to instruction shows long-term benefits in knowledge and motivation, even though students fail to invent correct methods.
One focus of my work on Productive Failure is to understand under what conditions “failure” becomes “productive”. What activities, settings, and norms help students learn from their errors? What task elements help students gain more from their suboptimal attempts? What are the outcomes of Productive Failure with regard to students’ knowledge, attitudes, and motivation?
My second focus is understanding the cognitive mechanisms of Productive Failure. By analyzing students’ activity data I identify behaviours during failure that lead to better learning. This helps me design better environments and understand the cognitive processes behind Productive Failure.

This work is supported by the Pittsburgh Science of Learning Center and the Carl Wieman Science Education Initiative.