A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit <a rel="external noopener" href="http://pact.cs.cmu.edu:80/pubs/Reformatted-Koedinger-EDM-for-WIRES-cogsci.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6c2zlhmvdrepppm4c24xcrea24" style="color: black;">Wiley Interdisciplinary Reviews: Cognitive Science</a>
An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from many kinds of educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/wcs.1350">doi:10.1002/wcs.1350</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26263424">pmid:26263424</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3olx4w3borhnlfptqksutuqyqa">fatcat:3olx4w3borhnlfptqksutuqyqa</a> </span>
more »... student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss 1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, 2) how better cognitive models can be discovered from data and used to improve instruction, 3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and 4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or discussion board.
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