The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

Integrated information systems are increasingly used in schools, and the advent of the technology-rich classroom requires a new degree of ongoing classroom assessment. Able to track web searches, resources used, task completion time, and a variety of other classroom behaviors, technology-rich classrooms offer a wealth of potential information about teaching and learning. This information can be used to track student progress in languages, STEM, and in 21st Century skills, for instance. However, despite these changes, there has been little change in (...) the kind of data made available to teachers, administrators, students, and parents. Measuring and Visualizing Learning in the Information-Rich Classroom collects research on the implementation of classroom assessment techniques in technology-enhanced learning environments. Building on research conducted by a multinational and multidisciplinary team of learning technology experts, and specialists from around the globe, this book addresses these discrepancies. With contributions from major researchers in education technology, testing and assessment, and education psychology, this book contributes to a holistic approach for building the information infrastructure of the 21st Century school. (shrink)