JEDM | Journal of Educational Data Mining <p>The Journal of Educational Data Mining (JEDM;&nbsp;<strong>ISSN 2157-2100</strong>) is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is&nbsp;<strong>completely and permanently free and open-access to both authors and readers</strong>.</p> <div>Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings in which they learn.</div> <div>The journal welcomes basic and applied papers describing mature work involving computational approaches of educational data mining. Specifically, it welcomes high-quality original work including but not limited to the following topics:</div> <div> <ul> <li class="show">processes or methodologies followed to analyse educational data,</li> <li class="show">integrating the data mining with pedagogical theories,</li> <li class="show">describing the way findings are used for improving educational software or teacher support,&amp;</li> <li class="show">improving understanding of learners' domain representations, and improving assessment of learners' engagement in the learning tasks.</li> </ul> </div> <div>From time to time, the journal also welcomes survey articles, theoretical articles, and position papers, in as much as these articles build on existing work and advance our understanding of the challenges and opportunities unique to this area of research.</div> <div>&nbsp;</div> <div>&nbsp;</div> <div><strong>Editor</strong>: Andrew Olney, University of Memphis, USA</div> <div>&nbsp;</div> <div><strong>Associate Editors</strong>:</div> <div>Ryan S. Baker, University of Pennsylvania, USA</div> <div>Michel C. Desmarais, Polytechnique Montreal, Canada (editor, 2013-2017)</div> <div>Agathe Merceron, University of Applied Sciences, Germany</div> <div>Mykola Pechenizkiy, Technische Universiteit Eindhoven, Netherlands</div> <div> <div>Kalina Yacef, University of Sydney, Australia (founding editor 2008-2013)</div> <div>&nbsp;</div> </div> <div><strong>Web Editor</strong>: Behzad Beheshti, Polytechnique Montreal, Canada</div> <div>&nbsp;</div> <div>Author guidelines and submission guidelines can be found&nbsp;<a href="/index.php/JEDM/about/submissions#authorGuidelines">here</a>. All other inquiries should be emailed to:&nbsp;<a href=""></a>.</div> <div>&nbsp;</div> en-US JEDM | Journal of Educational Data Mining 2157-2100 <p><strong>Authors who publish with this journal agree to the following terms: </strong></p><ol><li>The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.</li><li>Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.</li><li>The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a <a href="" target="_blank">Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International)</a>, or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:</li><ol><li>Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;</li><li>Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;</li><li>No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.</li></ol><li>The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post online a pre-publication <em>manuscript</em> (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see <a href="" target="_blank">The Effect of Open Access</a>). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.</li><li>Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.</li><li>The Author represents and warrants that:</li><ol><li>the Work is the Author’s original work;</li><li>the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;</li><li>the Work is not pending review or under consideration by another publisher;</li><li>the Work has not previously been published;</li><li>the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and</li><li>the Work contains no libel, invasion of privacy, or other unlawful matter.</li></ol><li>The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.</li></ol> Editorial Acknowledgements and Introduction to the Special Issue on EDM Journal Track <p>The EDM Conference was held in Wuhan this year, from June 25 to June 28, and for the third time it held a Journal track which was edited by Agathe Merceron and Radek Pelánek this year. The Journal track allows papers submitted to JEDM to be presented at the conference. A summary is available in the proceedings, and the full text is published in the Journal.</p> Agathe Merceron Andrew Olney Radek Pelánek Copyright (c) 2017 Agathe Merceron, Andrew Olney, Radek Pelanek 2017-09-19 2017-09-19 9 1 i i Modeling Student Behavior With Two-Layer Hidden Markov Models Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. To address this problem, we propose a student behavior representation method alongside a method for automatically discovering those student behavior patterns by leveraging the click log data that can be obtained from the MOOC platform itself. Specifically, we propose the use of a two-layer hidden Markov model (2L-HMM) to extract our desired behavior representation, and show that patterns extracted by such a 2L-HMM are interpretable, meaningful, and unique. We demonstrate that features extracted from a trained 2L-HMM can be shown to correlate with educational outcomes. Chase Geigle ChengXiang Zhai Copyright (c) 2017 JEDM - Journal of Educational Data Mining 2017-09-19 2017-09-19 9 1 1 24 Closing the loop: Automated data-driven cognitive model discoveries lead to improved instruction and learning gains <p class="Abstract">As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it yields better student learning when applied. Such an evaluation has been referred to as "closing the loop", as it completes cycle of system design, deployment, data analysis, and discovery leading back to design. Here, we present an instance of “closing the loop” on an automated cognitive modeling improvement discovered by Learning Factors Analysis (Cen, Koedinger, &amp; Junker, 2006). We discuss our findings from a process in which we interpret the automated improvements yielded by the best-fitting cognitive model, validate the interpretation on novel data, use it to make changes to classroom-deployed educational technology, and show that the changes lead to significant learning gains relative to a control condition.</p> Ran Liu Kenneth R Koedinger Copyright (c) 2017 JEDM - Journal of Educational Data Mining 2017-09-19 2017-09-19 9 1 25 41 RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests Various forms of Peer-Learning Environments are increasingly being used in postsecondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior. Hassan Khosravi Kendra Cooper Kirsty Kitto Copyright (c) 2017 JEDM - Journal of Educational Data Mining 2017-05-24 2017-05-24 9 1 42 67