Crystal Island – Outbreak


Principal Investigators: James Lester (PI, Computer Science), John Nietfeld (co-PI, Educational Psychology), Hiller Spires (co-PI, Curriculum & Instruction)

Primary Participants: Kristy Boyer (Computer Science), Julius Goth (Computer Science), Joe Grafsgaard (Computer Science), EunYoung Ha (Computer Science), Kristin Hoffman (Psychology), Seung Lee (Computer Science), Sunyoung Lee (Computer Science), Eleni Lobene (Psychology), Scott McQuiggan (Computer Science), Bradford Mott (Computer Science), Jennifer Robison (Computer Science), Jonathan Rowe (Computer Science), Lucy Shores (Computer Science), Kim Turner (Psychology)

Sponsors: National Science Foundation – Research and Evaluation on Education in Science and Engineering Program (2007-2009) & Human-Centered Computing Program (2008-2011)

Objectives: Recent years have seen a growing recognition of the importance and challenges of creating learning environments that promote motivating, inquiry-based science learning. Pedagogical agents are embodied software agents that have emerged as a promising vehicle for promoting effective learning. They provide customized problem-solving experiences and advice that are precisely tailored to individual learners in specific contexts. By co-habiting a rich inquiry-based learning environment with learners, pedagogical agents can meticulously observe learners’ problem solving activities, offer situated advice, and actively support learners’ iterating through cycles of questioning, hypothesis generation, data collection, and hypothesis testing. However, inquiry-based learning also presents a significant challenge: the very “openness” of the learning environment introduces multiple sources of complexity into tutorial planning. To address the complexities associated with scaffolding inquiry-based learning, this project explores Bayesian pedagogical agents that leverage recent advances in Bayesian and decision-theoretic computational models of reasoning to promote self-regulated learning experiences that are both effective and engaging.

The project has two complementary technology and learning thrusts:

1. It will develop a full suite of Bayesian pedagogical agent technologies for inquiry-based science learning environments. To promote effective and engaging learning processes and outcomes, the research team is creating Bayesian pedagogical agents that leverage probabilistic computational models that systematically reason about the multitude of factors that bear on decision making to infer learners’ beliefs, goals, and plans, including strategy use, from their problem-solving actions. By introducing pedagogical agents into the visually engaging environments that typify high-end game platforms and embedding them in dynamically generated science narratives, we are addressing the complementary goals of achievement and engagement.

2. It will provide a comprehensive account of the cognitive processes and results of interacting with Bayesian pedagogical agents in inquiry-based science learning by conducting extensive empirical studies. To understand the cognitive mechanisms by which self-regulated inquiry-based science learning occurs with middle school students interacting with Bayesian pedagogical agents, the research team is taking a multi-method approach to investigating the use and effectiveness of Bayesian pedagogical agents. In both controlled laboratory and classroom-based field settings, these studies are investigating the central issues of self-regulation with respect to both achievement (science content knowledge, transfer, and effective strategy use, including strategy selection and strategy shifting) and engagement (self-efficacy, situational interest, and mastery orientation with an emphasis on persistence) to determine precisely which technologies and conditions contribute most effectively to learning processes and outcomes.

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