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, Beuth University of Applied Sciences Berlin, 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 Acknowledgement <p>The Editor and Associate Editors would like to warmly thank the editorial board and&nbsp;colleagues who kindly served as reviewers in 2016. Their professionalism and support are&nbsp;much appreciated.</p> Andrew M. Olney Ryan S. Baker Michel C. Desmarais Agathe Merceron Mykola Pechenizkiy Kalina Yacef Copyright (c) 2017 Andrew M. Olney, Ryan S. Baker, Michel C. Desmarais, Agathe Merceron, Mykola Pechenizkiy, Kalina Yacef 2017-12-23 2017-12-23 9 2 i ii Concept Landscapes - A New Way of Using Concept Maps <span style="color: #000000;">This article presents </span><span style="color: #800000;">concept landscapes</span><span style="color: #000000;"> - a novel way of investigating the state and development of knowledge structures in groups of persons using concept maps. Instead of focusing on the assessment and evaluation of single maps, the data of many persons is aggregated and data mining approaches are used in analysis. New insights into the ``shared'' knowledge of groups of learners are possible in this way. Electronic collection of concept maps makes it feasible to aggregate the data of a large group of persons, which in turn favors a data mining approach to the analysis.</span><pre style="-qt-paragraph-type: empty; -qt-block-indent: 0; text-indent: 0px; margin: 0px;"> </pre><pre style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;"><span style="color: #000000;">The educational theories underlying the approach, the definition of concept landscapes, and the accompanying analysis methods are presented. Cluster analysis and Pathfinder networks are used on the aggregated data, allowing new insights into the structural configuration of the knowledge of learners. Two real-world research projects serve as case studies for experimental results. The data structures and analysis methods necessary for working with concept landscapes have been implemented as a GNU-R package that is freely available.</span></pre> Andreas Muehling Copyright (c) 2017 JEDM - Journal of Educational Data Mining 2017-12-23 2017-12-23 9 2 1 30 EEG Based Analysis of Cognitive Load Enhance Instructional Analysis <p>One of the recommended approaches in instructional design methods is to optimize the value of working memory capacity and avoid cognitive overload. Educational neuroscience offers novel processes and methodologies to analyze cognitive load based on physiological measures. Observing psychophysiological changes when they occur in response to the course of a learning session allows adjustments in the learning session based on the individual learner’s capabilities. The availability of non-invasive electroencephalogram (EEG)-based devices and advanced near-real-time analysis techniques have improved our understanding of the underlying mechanisms and have impacted the way we design instructional methods and adapt them to the current learner’s cognitive load and valence states. We will review Cognitive Load Theory, how cognitive load may be measured, and how analysis of EEG data can be applied to enhance learning through real-time measurements of the learner’s cognitive load. We show a learning experiment in an attempt to provide a proof of concept of learning and real-time measures of EEG as indicators of mental states.</p> Alex Dan Miriam Reiner Copyright (c) 2017 JEDM - Journal of Educational Data Mining 2017-12-23 2017-12-23 9 2 31 44 WordBytes: Exploring an Intermediate Constraint Format for Rapid Classification of Student Answers on Constructed Response Assessments <p>Computerized classification of student answers offers the possibility of instant feedback and improved learning.  However, there are tradeoffs between formative assessment and ease of classification with different question types.  Open response (OR) questions provide greater insight into student thinking and understanding than more constrained multiple choice (MC) questions, but development of automated classifiers is more difficult, often requiring training a machine learning system with many human-classified answers.  Here we explore a novel intermediate-constraint question format called WordBytes (WB) where students assemble one-sentence answers to two different college evolutionary biology questions by choosing, then ordering, fixed tiles containing words and phrases.  We found WB allowed students to construct hundreds to thousands of different answers, with multiple ways to express correct answers and incorrect answers with different misconceptions.  WB offers the possibility of more rapid development of classifiers, as we found humans could specify rules for an automated grader that could accurately classify answers as correct/incorrect with Cohen’s kappa of 0.89 or higher, near the measured intra-rater reliability of two human graders and the performance of machine classification of OR answers (Nehm et al. 2012).  Finer-grained classification to identify specific misconception had much lower accuracy (Cohen’s kappa &lt; 0.70), which could be improved either by using a machine learner or human rules, but both required inspecting and classifying many student answers.  We thus find that the intermediate constraints of our WB format allows the possibility of accurate grading of the correctness without the labor-intensive step of collecting hundreds of student answers.</p> Kerry J Kim Denise S Pope Daniel Wendel Eli Meir Copyright (c) 2017 JEDM - Journal of Educational Data Mining 2017-12-23 2017-12-23 9 2 45 71