This paper elaborates on the need for adaptive educational systems, describes an evidence-based, four-process framework for analyzing adaptive educational systems, and reviews several adaptive technologies and types of adaptive learning environments. In addition, interviews with experts in the field inform our discussion on what to adapt, how to adapt, and the future of adaptive educational systems.
This paper defines Bayesian network models and examines their applications to IRT‐based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models are reviewed, as they affect applications to diagnostic assessment. The paper discusses how Bayesian network models are set up with expert information, improved and calibrated from data, and deployed as evidence‐based inference engines. Aimed at a general educational measurement audience, the paper illustrates the flexibility and capabilities of Bayesian networks through a series of concrete examples, and without extensive technical detail. Examples are provided of proficiency spaces with direct dependencies among proficiency nodes, and of customized evidence models for complex tasks. This paper is intended to motivate educational measurement practitioners to learn more about Bayesian networks from the research literature, to acquire readily available Bayesian network software, to perform studies with real and simulated data sets, and to look for opportunities in educational settings that may benefit from diagnostic assessment fueled by Bayesian network modeling.
The purpose of our project is to explore the measurement of cognitive skills in the domain of science through collaborative problem solving tasks, measure the collaborative skills, and gauge the potential feasibility of using game-like environments with avatar representation for the purposes of assessing the relevant skills. We are comparing students' performance in two conditions. In one condition, students work individually with two virtual agents in a game-like task. In the second condition, dyads of students work collaboratively with two virtual agents in the similar game-like task through a chat box. Our research is motivated by the distributed nature of cognition, extant research on computer-supported collaborative learning (CSCL) which has shown great value of collaborative activities for learning, and the framework for the Programme for International Student Assessment (PISA) framework. This chapter focuses on the development and implementation of a conceptual model to measure individuals' cognitive and social skills through collaborative activities.
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