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PSLC Seminars

March 27, 2012 Improving Student Success: Easy-to-Use Techniques that Really Work John Dunlosky Kent State University

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Despite best efforts by educators in the United States, many students are being left behind by an educational system that some believe is in crisis. Improving educational outcomes will likely require efforts on many fronts. Thus, if simple techniques are available that teachers and students could use to improve student achievement, would you be surprised if teachers were not being told about these techniques and that many students were not using them? In the present talk, I will discuss some easy-to-use study techniques and offer general recommendations on their relative utility to improve student achievement. Next, I will discuss a retrieval-monitoring-feedback technique based on metacognitive monitoring and spaced retrieval practice that promises to boost students learning and comprehension of core concepts in introductory classes. The evidence relevant to the development and efficacy of this technique will be described in detail.

Dr. John Dunlosky is a Professor of Psychology at Kent State University. He has contributed empirical and theoretical work on memory and metacognition, and a major aim of his research program is to develop techniques to improve peoples self-regulated learning. A fellow of the Association for Psychological Science, he is a founder of the International Association for Metacognition and has served as an Associate Editor for the Journal of Experimental Psychology (JEP): Learning, Memory and Cognition.

December 13, 2010 Analytic representations: the design of tools to create and exploit them Gregory Dyke Carnegie Mellon University

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My dissertation focussed on computer support for "human analysis" (as opposed to the semi-automated or computer assisted analysis). As a result I created a tool called Tatiana ( http://code.google.com/p/tatiana ) which has interesting properties for researchers who collect and analyse various kinds of process data (videos, computer logs, transcripts, etc.). Tatiana is built on a framework for constructing and managing analytic representations using four quasi-orthogonal operation types: transformation, enrichment, visualisation and synchronisation.

This talk will describe Tatiana and the underlying framework:
- What Tatiana can currently do and what features are planned for the future.
- How Tatiana may be able to help solve research problems when analyzing videos, computer logs, transcripts, etc.
- Situations in which it might be beneficial to extend Tatiana, rather than constructing new software from scratch.

About Gregory Dyke: I am interested in the creation of tools to help humans analyse data of computer mediated collaboration (and learning). My PhD resulted in the creation of Tatiana (Trace Analysis Tool for Interaction ANAlysts), a flexible, extensible tool particularly well suited for the analysis of small group face to face and computer mediated interaction. My current work involves examining and assisting the discovery of how interaction unfolds over time.

December 10, 2010 Using DataShop Tools to Model Students Learning Statistics - A LearnLab DataShop Case Study Marsha Lovett Carnegie Mellon University

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Marsha Lovett describes using the LearnLab DataShop to improve the Carnegie Mellon Statistics course.

August 30, 2010 Physical Symbols for Powerful Reasoning Ken Koedinger Carnegie Mellon University

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One way that cultures advance the intelligence of their members is through the symbolic forms (e.g., language, numbers) they promulgate through formal and informal education. Algebra is a prime example of an external symbolic system that, once learned, greatly enhances human intelligence. This enhancement is reflected in better performance in more complex problem solving even though it may inhibit performance for simpler problem solving (Koedinger, Alibali, & Nathan, 2008). Learning to effectively use this external representational tool is not easy even discounting the time needed to acquire adequate background knowledge, it takes most students a school year or two to learn algebra. In other words, many changes in /internal/ cognition are required before effective use of this /external/ representation is possible. I will discuss our experiments on algebra learning with real and simulated students (e.g., Matsuda, Cohen, Sewall, Lacerda, & Koedinger, 2007) and emphasize the productive interplay between internal and external cognition. I will explore whether there is a human-algebra distributed system that has learning properties beyond the human system.

July 25, 2010 KDD Cup Workshop John Stamper Carnegie Mellon University

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2010 KDD Cup Workshop in Washington, DC.

July 21, 2010 Cognitive Science 2010 Plenary Talk Marsha Lovett Carnegie Mellon University

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Marsha will talk about knowledge component (KC) modeling in the context of the OLI-Statistics course, showing a new tool that helps instructors track their students' progress based on the models, and then describing some results from a series of studies showing accelerated learning in the OLI-Statistics course when instructors use this tool.

June 14, 2010 Thinking with your Hands Laurens Feenstra

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Multiple External Representations (MERs) have been used successfully in many instructional domains, including fractions. In most intelligent tutors however, these representations are used as static graphics, limiting student-tutor interaction to traditional inputs such as text boxes, drop-down menus, etcetera. This talk is about enhancing the use of the capabilities of Flash in example-tracing tutors, supporting direct student manipulation of graphics in a dynamic tutor environment. Allowing students to actively manipulate interactive MERs themselves, while retaining the feedback mechanisms that make cognitive tutoring in CTAT so successful is a potent mix to increase student learning.

I will demonstrate some of these possibilities by discussing a study with interactive fraction representations we conducted this spring with 312 4th and 5th-grade students in 13 classes. Students working with interactive fraction representations came away having learned more than students working with static representations and traditional inputs.

May 12, 2010 Engagement, Learning, and Assessment in Immersive Environments Chris Dede Harvard University

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Becoming a digital person in an immersive virtual world enables types of motivation, learning, and assessment potentially quite powerful for education. With Institute for Education Sciences funding, our research team is building immersive ecosystems (http://www.ecomuve.org) and virtual performance assessments (http://virtualassessment.org). This talk describes our design strategies for fostering engagement that does not undercut learning and long-term intrinsic motivation and for conducting unobtrusive assessments based on event-log analysis.

May 10, 2010 Adventures in Researching Self-Regulated Learning Philip H. Winne Simon Fraser University

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Amidst wide ranging quests to understand how learners learn, so principles can be offered for improving education, one topic of interest has been skills for learning. Under various guises and in multiple subject domains reading comprehension skills, strategies for composing, problem solving heuristics, and more it has been hypothesized that learners sometimes may direct their resources toward self-observing and attempting to self-improve learning skills by engaging in various metacognitive activities. This is known as self-regulated learning or SRL. As is the case in other areas of research, investigating SRL phenomena is challenging. I summarize these challenges and describe how I hope research on SRL will evolve in three respects: operationally defining constructs as data that actually reflect SRL processes, gathering sufficient data needed to advance learning science, and methods for analyzing data. I demonstrate a software tool, nStudy, that can make a dent in the first two of these topics, and offer conjectures for addressing the third. My goal in this talk is to arouse you to improve research on SRL rather than to present findings and quasi-conclusions.

Bio: Philip H. Winne is a professor of educational psychology and Canada Research Chair in Self-Regulated Learning and Learning Technologies atSimon Fraser University. Winne has made significant contributions to research on self-regulated learning. He is the principal investigator of theLearning Kit Project, which has developed educational software founded on principles of self-regulated learning. Before earning a PhD from Stanford University in 1976, Winne received undergraduate and masters degrees from Bucknell University. He has served as co-editor of the Educational Psychologist and associate editor of the British Journal of Educational Psychology. Winne has authored (or co-authored) over 70 peer-reviewed journal articles, over 30 book chapters, and 5 books including an introductory textbook on educational psychology that is widely used in Canada (Woolfolk, Winne, & Perry, 2006). Phil on the Web: http://www.educ.sfu.ca/research/winne/