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Game Changers: Education and IT The collaboration among this diverse group of experts led us to a method of instruction that is repeated throughout an OLI course for each concept. Instruction on every concept starts with one or more student-centered, observable learning objectives. We then present expository content in the form of text, images, simulations, short (3–5 minutes) videos, and worked examples where appropriate (Figure 1). Interspersed with the exposition are interactive tasks that support students to engage in authentic practice with the concepts and skills they are learning. The tasks are presented in a supported environment with hints available to the students if they are struggling. They receive feedback that reinforces correct responses and targets common student misconceptions (Figure 2). Finally, we offer students a chance to do a quick self-assessment and reflect on what they have learned, so that they can decide whether they should move on or whether they need additional practice (Figure 3). A key attribute of the OLI environment is that while students are working through the course, we are collecting analytics data and using those data to drive multiple feedback loops (Figure 4): • Feedback to students: We provide the student with timely and targeted support throughout the learning process. This support is in the form of corrections, suggestions, and cues that are tailored to the individual’s current performance and that encourage revision and refinement. • Feedback to instructors: The richness of the data we are collecting about student use and learning provides an unprecedented opportunity to give instructors a clear picture of the student’s current knowledge state. As a result, instructors are able to spend less classroom time lecturing and more time interacting with students in ways that take advantage of the instructor’s unique expertise and interests targeted to student needs. • Feedback to course designers: Analysis of these interaction-level data allows us to observe how students are using the material in the course and assess the impact of their use patterns on learning outcomes. We are then able to take advantage of that analysis to iteratively refine and improve the course for the next group of students. • Feedback to learning science researchers: Finally, there is a feedback loop for learning science researchers who use information gathered by the OLI environment to create and refine theories of human learning. In addition to building on what we know about learning, our courses serve as a platform in which new knowledge about human learning can be developed. 204
The Open Learning Initiative Figure 1. Learning Objective, Expository Content (Engineering Statics) 205
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Contents Foreword Molly Corbett Bro
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1 The Knowledge Economy: Challenges
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Case STUDY 8 OpenCourseWare Mary Lo
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Index Weiser, Mark, 134 Welcome to
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