http://jedm.educationaldatamining.org/index.php/JEDM/issue/feed JEDM | Journal of Educational Data Mining 2017-12-23T18:54:20+00:00 Andrew Olney info@jedm.educationaldatamining.org Open Journal Systems <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>&nbsp;</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</li> <li class="show">►Improving understanding of learners' domain representations</li> <li class="show">►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>&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="mailto:jedm.editor@gmail.com">jedm.editor@gmail.com</a>.</div> <div>&nbsp;</div> http://jedm.educationaldatamining.org/index.php/JEDM/article/view/273 Editorial Acknowledgement 2017-12-23T18:54:20+00:00 Andrew M. Olney aolney@memphis.edu Ryan S. Baker ryanshaunbaker@gmail.com Michel C. Desmarais michel.desmarais@polymtl.ca Agathe Merceron merceron@beuth-hochschule.de Mykola Pechenizkiy mykola.pechenizkiy@gmail.com Kalina Yacef kalina.yacef@sydney.edu.au <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> 2017-12-23T18:45:38+00:00 Copyright (c) 2017 Andrew M. Olney, Ryan S. Baker, Michel C. Desmarais, Agathe Merceron, Mykola Pechenizkiy, Kalina Yacef http://jedm.educationaldatamining.org/index.php/JEDM/article/view/138 Concept Landscapes - A New Way of Using Concept Maps 2017-12-23T18:54:20+00:00 Andreas Muehling andreas.muehling@tum.de <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> 2017-12-23T17:59:23+00:00 Copyright (c) 2017 JEDM - Journal of Educational Data Mining http://jedm.educationaldatamining.org/index.php/JEDM/article/view/160 EEG Based Analysis of Cognitive Load Enhance Instructional Analysis 2017-12-23T18:54:20+00:00 Alex Dan alexda@technion.ac.il Miriam Reiner miriamr@technion.ac.il <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> 2017-12-23T18:00:16+00:00 Copyright (c) 2017 JEDM - Journal of Educational Data Mining http://jedm.educationaldatamining.org/index.php/JEDM/article/view/209 WordBytes: Exploring an Intermediate Constraint Format for Rapid Classification of Student Answers on Constructed Response Assessments 2017-12-23T18:54:20+00:00 Kerry J Kim kerryjkim@simbio.com Denise S Pope dspope@simbio.com Daniel Wendel starlogodaniel@gmail.com Eli Meir meir@simbio.com <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> 2017-12-23T18:01:03+00:00 Copyright (c) 2017 JEDM - Journal of Educational Data Mining