Proceedings of the 9th International Conference on Educational

Proceedings of the 9th International Conference on Educational Data Mining T. Barnes, M. Chi, and M. Feng (Eds) International Conference on Educational Data Mining (EDM) 2016 Proceedings of the 9th International Conference on Educational Data Mining Tiffany Barnes, Min Chi, Mingyu Feng (eds) Raleigh, June 29-July 2, 2016 Proceedings of the 9th International Conference on Educational Data Mining i Preface The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29 - July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis 2013, Chania 2012, Eindhoven 2011, Pittsburgh 2010, Cordoba 2009 and Montreal 2008). The EDM conference is the leading international forum for high-quality research that leverages educational data, learning analytics, and machine learning to answer research questions that shed light on the learning processes. Educational data may come from traces that students leave when they interact with learning management systems, interactive learning environments, intelligent tutoring systems, educational games or when they participate in other data-rich learning contexts. The types of data range from raw log files to data captured by eye-tracking devices or other kind of sensors. The methods used by EDM researchers include analytics, data science, data mining, machine learning, as well as social network analysis, graph mining, recommender systems, and model building. This years conference features three invited talks by: Rakesh Agrawal, President and Founder of Data Insights Laboratories; Marcia C. Linn, Professor of the University of California at Berkeley; and Judy Kay, Professor of the University of Sydney. Judy Kay’s invited paper entitled “Enabling people to harness and control EDM for lifelong, life-wide learning” is also presented in the proceedings. Together with the Journal of Educational Data Mining (JEDM), the EDM 2016 conference supports a JEDM Track that provides researchers a venue to deliver more substantial mature work than is possible in a conference proceedings and to present their work to a live audience. The papers submitted to this track followed the JEDM peer review process; three papers have been accepted to the track and will be presented at the conference. The abstracts of the invited talks and accepted JEDM Track papers can be found in the proceedings. The main conference invited contributions to the Research Track and Industry Track. We received 161 submissions (109 full, 45 short, and 7 industry). We accepted 16 exemplary full papers (15% acceptance rate), 14 full papers (27.5% acceptance rate) and 51 short papers for oral presentation (52% acceptance rate ) and an additional 40 for poster presentation. Of the industry papers, 3 were selected for oral presentations and 2 for posters. This year’s best paper and best student paper awards were generously sponsored by the Prof. Ram Kumar Memorial Foundation. The best paper was awarded to the paper entitled “How Deep is Knowledge Tracing?” while the best student paper was awarded to the paper entitled “Calibrated Self-Assessment.” The EDM conference traditionally provides opportunities for young researchers, and particularly for PhD students, to present their research ideas and receive feedback from the peers and more senior researchers. This year, the Doctoral Consortium features 6 such presentations. In addition to the main program, the conference also includes 3 workshops: WS1: Computer-Supported Peer Review in Education (CSPRED-2016), WS2: Writing Analytics, Data Mining, and Writing Studies; WS3: Educational Data Analysis using LearnSphere, and 2 tutorials: T1: SAS Tools for Educational Data Mining, and T2: Massively Scalable EDM with Spark. This year we expand our electronic presence with Whova a social app for conference attendees that provided services for personal scheduling, social linking and personalized recommendations of papers. We thank the sponsors of EDM 2016 for their generous support: Civitas Learning, Blackboard, MARi, SAS, Cengage Learning and the Prof. Ram Kumar Memorial Foundation. We thank North Carolina State University for their in-kind support, and the Sheraton Downtown Raleigh for their excellent conference services. We also thank the program committee members and reviewers, who with their enthusiastic contributions gave us invaluable support in putting this conference together. Last but not least we thank the organizing team: David Lindrum – Sponsorship Chair; Paul Inventado – Proceedings Chair & Webmaster; Collin Lynch – Posters Chair; Ed Gehringer – Student Volunteer Chair; Sidney D’Mello – DC Chair; Erica Snow and Jonathan Rowe – Workshop & Tutorials Chairs; and Piotr Mitros – Industry Track Chair. Min Chi North Carolina State University Program Co-chair Mingyu Feng SRI Program Co-chair Tiffany Barnes North Carolina State University General Chair Proceedings of the 9th International Conference on Educational Data Mining ii Organization Conference Chair Tiffany Barnes North Carolina State University, USA Program Chairs Min Chi Mingyu Feng North Carolina State University, USA SRI International Workshop and Tutorials Chairs Jonathan Rowe Erica Snow North Carolina State University, USA SRI Poster Chair and Local Arrangements Chair Collin Lynch North Carolina State University, USA Doctoral Consortium Chair Sidney D’Mello University of Notre Dame, USA Student Volunteers Chair Ed Gehringer North Carolina State University, USA Industry Track Chair Piotr Mitros edX Sponsors Chair David Lindrum Soomo Learning Proceedings Chair and Webmaster Paul Salvador Inventado Carnegie Mellon University, USA Proceedings of the 9th International Conference on Educational Data Mining iii Steering Committee / IEDMS Board of Directors Ryan Baker Tiffany Barnes Michel Desmarais Sidney D’Mello Neil Heffernan III Agathe Merceron Mykola Pechenizkiy John Stamper Kalina Yacef Teachers College, Columbia University, USA University of North Carolina at Charlotte, USA Ecole Polytechnique de Montreal, Canada University of Notre Dame, USA Worcester Polytechnic Institute, USA Beuth University of Applied Sciences, Germany Eindhoven University of Technology, The Netherlands Carnegie Mellon University, USA The University of Sydney, Australia Program Committee Lalitha Agnihotri Esma Aimeur Vincent Aleven Ivon Arroyo Mirjam Augstein Roger Azevedo Costin Badica Ryan Baker Tiffany Barnes Joseph Beck Yoav Bergner Gautam Biswas Mary Jean Blink Jesus G. Boticario François Bouchet Alex Bowers Jared Boyce Kristy Elizabeth Boyer Javier Bravo Agapito Keith Brawner Emma Brunskill Renza Campagni Alberto Cano Min Chi Mihaela Cocea Scott Crossley Cynthia D’Angelo Sidney D’Mello Michel Desmarais Hendrik Drachsler Michael Eagle Stephen Fancsali Mingyu Feng Vladimir Fomichov McGraw Hill Education University of Montreal Human-Computer Interaction Institute, Carnegie Mellon University Worcester Polytechnic Institute Upper Austria University of Applied Sciences, Communication and Knowledge Media North Carolina State University University of Craiova, Software Engineering Department, Romania Teachers College, Columbia University North Carolina State University Worcester Polytechnic Institute Educational Testing Service Vanderbilt University TutorGen, Inc. UNED LIP6 - Universit Pierre et Marie Curie Teachers College, Columbia University SRI International University of Florida Universidad Autonoma de Madrid United States Army Research Laboratory Carnegie Mellon University Universit degli Studi di Firenze Department of Computer Sciences and Numerical Analysis North Carolina State University School of Computing, University of Portsmouth Georgia State University SRI International University of Notre Dame Ecole Polytechnique de Montreal Open University of the Netherlands North Carolina State University Carnegie Learning, Inc. SRI International Faculty of Business Informatics,National Research University Higher School of Economics Proceedings of the 9th International Conference on Educational Data Mining iv Davide Fossati April Galyardt Carlos Garca-Martnez Dragan Gasevic Eva Gibaja Daniela Godoy Ilya Goldin Yue Gong José González-Brenes Art Graesser Joseph Grafsgaard Philip Guo Neil Heffernan Arnon Hershkovitz Andrew Hicks Roland Hubscher Vladimir Ivančević Mike Joy Kenneth Koedinger Irena Koprinska Sotiris Kotsiantis Andrew Krumm James Lester Innar Liiv Ran Liu Wei Liu Vanda Luengo Ivan Luković J. M. Luna Mihai Lupu Maria Luque Collin Lynch Lina Markauskaite Noboru Matsuda Manolis Mavrikis Oleksiy Mazhelis Gordon McCalla Victor Menendez Agathe Merceron Donatella Merlini Cristian Mihaescu Carlos Monroy Behrooz Mostafavi Bradford Mott Tristan Nixon Roger Nkambou Benjamin Nye Andrew Olney Luc Paquette Abelardo Pardo Zach Pardos Mykola Pechenizkiy Carnegie Mellon University in Qatar University of Georgia Computing and Numerical Analysis Dept. Univ. of Crdoba University of Edinburgh Departmen of Computer Science and Numerical Analysis ISISTAN Research Institute Pearson CS department, Worcester Polytechnic Institute Pearson University of Memphis North Carolina State University, Computer Science University of Rochester Worcester Polytechnic Institute Tel Aviv University North Carolina State University Bentley University University of Novi Sad, Faculty of Technical Sciences University of Warwick Carnegie Mellon University The University of Sydney Univerisity of Patras SRI International North Carolina State University Tallinn University of Technology Carnegie Mellon University University of Technology, Sydney Laboratoire d’informatique de Paris, LIP6, Universit Pierre et Marie Curie University of Novi Sad, Faculty of Technical Sciences Dept. of Computer Science and Numerical Analysis Vienna University of Technology University of Cordoba North Carolina State University The University of Sydney Carnegie Mellon University London Knowledge Lab University of Jyvskyl University of Saskatchewan Universidad Autnoma de Yucatn Beuth University of Applied Sciences Berlin Universit di Firenze University of Craiova Rice University North Carolina State University North Carolina State University University of Memphis Universit du Qubec Montral (UQAM) University of Southern California (Institute for Creative Technologies) University of Memphis Teachers College, Columbia University The University of Sydney UC Berkeley Department of Computer Science, Eindhoven University of Technology Proceedings of the 9th International Conference on Educational Data Mining v Radek Pelánek Niels Pinkwart Paul Stefan Popescu Kaska Porayska-Pomsta Thomas Price David Pritchard Martina Rau Steven Ritter Robby Robson Ma. Mercedes T. Rodrigo Cristobal Romero Jos Ral Romero Carolyn Rose Jonathan Rowe Vasile Rus Maria Ofelia San Pedro Olga C. Santos George Siemens Erica Snow John Stamper Jun-Ming Su Ling Tan Stefan Trausan-Matu Sebastián Ventura Lucian Vintan Alina Von Davier Feng-Hsu Wang Yutao Wang Stephan Weibelzahl Fridolin Wild Joseph Jay Williams Marcelo Worsley Kalina Yacef Kalina Yacef Michael Yudelson Amelia Zafra Gómez Alfredo Zapata González Diego Zapata-Rivera Marta Zorrilla Masaryk University Brno Humboldt-Universitt zu Berlin Faculty of Automation Conputers an Electronics Craiova London Knowledge Lab North Carolina State University MIT University of Wisconsin - Madison, Department of Educational Psychology Carnegie Learning, Inc. Eduworks Department of Information Systems and Computer Science, Ateneo de Manila University Department of Computer Sciences and Numerical Analysis University of Cordoba Carnegie Mellon University North Carolina State University The University of Memphis Teachers College, Columbia University aDeNu Research Group (UNED) UT Arlington Arizona State University Carnegie Mellon University Department of Information and Learning Technology, National University of Tainan Australian Council for Educational Research University Politehnica of Bucharest Department of Computer Sciences and Numerical Analysis “Lucian Blaga” University of Sibiu Educational Testing Service Ming Chuan University WPI Private University of Applied Sciences Gttingen The Open University Harvard University Stanford University The University of Sydney The University of Sydney Carnegie Learning, Inc. Department of Computer Sciences and Numerical Analysis Universidad Autonoma de Yucatan Educational Testing Service University of Cantabria Proceedings of the 9th International Conference on Educational Data Mining vi Sponsors Proceedings of the 9th International Conference on Educational Data Mining vii Awards EDM 2016 thanks the Prof. Ram Kumar Memorial Foundation for generously sponsoring the 2016 best paper and best student paper awards. Best papers and exemplary paper selection A total of 16 exemplary papers were selected by the program chairs as those that represent the best work submitted to EDM 2016. Candidates for exemplary papers were selected among those accepted as full papers using the following criteria: 1) the average ratings of all reviewers and 2) at least one reviewer indicated the paper should be considered for best paper. Program Chairs and the General Chair then performed meta-reviews for all of these papers to make the final exemplary paper selections. Finally, the Best Paper Committee was formed to review the top papers in the conference to select best paper nominations. We used random selection to divide both the 16 exemplary papers and the 10 committee members into two groups. All members in the same Best Paper Sub-committee received the same 8 exemplary papers together with their reviews. Sub-committee members were asked to rank the three best papers from the 8 papers, and to provide a 1-2 sentence justification for each of the top 3 they chose. Based on these rankings, four Best Paper nominees were selected. Best Paper Committee: Koedinger, Kenneth Goldin, Ilya Pavlik Jr., Philip I. Heffernan, Neil Aleven, Vincent Ritter, Steven Baker, Ryan Olney, Andrew Galyardt, April Pechenizkiy, Mykola Award winners Best paper How Deep is Knowledge Tracing? Mohammad Khajah, Robert Lindsey and Michael Mozer Best student paper Calibrated Self-Assessment Igor Labutov and Christoph Studer Best paper nominees LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos Arjun Sharma, Arijit Biswas, Ankit Gandhi, Sonal Patil and Om Deshmukh How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations Martina Rau, Blake Mason and Robert Nowak Measuring Gameplay Affordances of User-Generated Content in an Educational Game Andrew Hicks, Zhongxiu Liu and Tiffany Barnes Proceedings of the 9th International Conference on Educational Data Mining viii Table of Contents Invited Talks (abstracts) Data-Driven Education: Some opportunities and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rakesh Agrawal 2 WISE Ways to Strengthen Inquiry Science Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marcia Linn 3 Enabling people to harness and control EDM for lifelong, life-wide learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Judy Kay 4 JEDM Track Journal Papers (abstracts) Toward Data-Driven Design of Educational Courses: A Feasibility Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rakesh Agrawal, Behzad Golshan and Evangelos Papalexakis 6 Next-Term Student Performance Prediction: A Recommender Systems Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mack Sweeney, Jaime Lester, Huzefa Rangwala and Aditya Johri 7 Exploring the Effect of Student Confusion in Massive Open Online Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diyi Yang, Robert Kraut, and Carolyn Rosé 8 Invited Paper Enabling people to harness and control EDM for lifelong, life-wide learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Judy Kay Full Papers {ENTER}ing the Time Series {SPACE}: Uncovering the Writing Process through Keystroke Analyses . . . . . . . . . 22 Laura Allen, Matthew Jacovina, Mihai Dascalu, Rod Roscoe, Kevin Kent, Aaron Likens and Danielle McNamara Automatic Gaze-Based Detection of Mind Wandering during Film Viewing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Caitlin Mills, Robert Bixler, Xinyi Wang and Sidney D’Mello Riding an emotional roller-coaster: A multimodal study of young child’s math problem solving activities . . . . . . . 38 Lujie Chen, Xin Li, Zhuyun Xia, Zhanmei Song, Louis-Philippe Morency and Artur Dubrawski Joint Discovery of Skill Prerequisite Graphs and Student Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Yetian Chen, José González-Brenes and Jin Tian Gauging MOOC Learners’ Adherence to the Designed Learning Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Daniel Davis, Guanliang Chen, Claudia Hauff and Geert-Jan Houben Dynamics of Peer Grading: An Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Luca de Alfaro and Michael Shavlovsky Sequence Matters, But How Exactly? A Method for Evaluating Activity Sequences from Data . . . . . . . . . . . . . . . . . 70 Shayan Doroudi, Kenneth Holstein, Vincent Aleven and Emma Brunskill Measuring Gameplay Affordances of User-Generated Content in an Educational Game . . . . . . . . . . . . . . . . . . . . . . . . . 78 Andrew Hicks, Zhongxiu Liu, Michael Eagle and Tiffany Barnes Proceedings of the 9th International Conference on Educational Data Mining ix The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Stephen Hutt, Caitlin Mills, Shelby White, Patrick J. Donnelly and Sidney K. D’Mello How Deep is Knowledge Tracing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Mohammad Khajah, Robert Lindsey and Michael Mozer Temporally Coherent Clustering of Student Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Severin Klingler, Tanja Käser, Barbara Solenthaler and Markus Gross Web as a textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Igor Labutov and Hod Lipson Calibrated Self-Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Igor Labutov and Christoph Studer MOOC Learner Behaviors by Country and Culture; an Exploratory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Zhongxiu Liu, Rebecca Brown, Collin Lynch, Tiffany Barnes, Ryan Baker, Yoav Bergner and Danielle Mcnamara Effect of student ability and question difficulty on duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Yijun Ma, Lalitha Agnihotri, Ryan Baker and Shirin Mojarad Modeling the Influence of Format and Depth during Effortful Retrieval Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Jaclyn K. Maass and Philip I. Pavlik Jr. The Apprentice Learner architecture: Closing the loop between learning theory and educational data . . . . . . . . . . 151 Christopher Maclellan, Erik Harpstead, Rony Patel and Kenneth Koedinger Mining behaviors of students in autograding submission system logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Jessica McBroom, Bryn Jeffries, Irena Koprinska and Kalina Yacef Modelling the way: Using action sequence archetypes to differentiate learning pathways from learning outcomes 167 Kelvin H. R. Ng, Kevin Hartman, Kai Liu and Andy W H Khong A Coupled User Clustering Algorithm for Web-based Learning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Ke Niu, Zhendong Niu, Xiangyu Zhao, Can Wang, Kai Kang and Min Ye Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming . . . . . . . . 183 Benjamin Paaßen, Joris Jensen and Barbara Hammer Generating Data-driven Hints for Open-ended Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Thomas Price, Yihuan Dong and Tiffany Barnes How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Martina Rau, Blake Mason and Robert Nowak Student Usage Predicts Treatment Effect Heterogeneity in the Cognitive Tutor Algebra I Program . . . . . . . . . . . . . 207 Adam Sales, Asa Wilks and John Pane LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos . . . . . . 215 Arjun Sharma, Arijit Biswas, Ankit Gandhi, Sonal Patil and Om Deshmukh Semantic Features of Math Problems: Relationships to Student Learning and Engagement . . . . . . . . . . . . . . . . . . . . . 223 Stefan Slater, Jaclyn Ocumpaugh, Ryan Baker, Peter Scupelli, Paul Salvador Inventado and Neil Heffernan An Ensemble Method to Predict Student Performance in an Online Math Learning Environment. . . . . . . . . . . . . . . 231 Martin Stapel, Zhilin Zheng and Niels Pinkwart Proceedings of the 9th International Conference on Educational Data Mining x Predicting Post-Test Performance from Student Behavior: A High School MOOC Case Study . . . . . . . . . . . . . . . . . . 239 Sabina Tomkins, Arti Ramesh and Lise Getoor The Affective Impact of Tutor Questions: Predicting Frustration and Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Alexandria Vail, Joseph Wiggins, Joseph Grafsgaard, Kristy Boyer, Eric Wiebe and James Lester Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams . . . . . . . . . . . . . . . 255 Linting Xue, Collin Lynch and Min Chi Short Papers Investigating Swarm Intelligence for Performance Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Mohammad Majid Al-Rifaie, Matthew Yee-King and Mark d’Inverno Predicting Student Progress from Peer-Assessment Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Michael Mogessie Ashenafi, Marco Ronchetti and Giuseppe Riccardi Topic-wise Classification of MOOC Discussions: A Visual Analytics Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Thushari Atapattu, Katrina Falkner and Hamid Tarmazdi Document Segmentation for Labeling with Academic Learning Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 Divyanshu Bhartiya, Danish Contractor, Sovan Biswas, Bikram Sengupta and Mukesh Mohania Semi-Automatic Detection of Teacher Questions from Human-Transcripts of Audio in Live Classrooms . . . . . . . . . 288 Nathaniel Blanchard, Patrick Donnelly, Andrew Olney, Borhan Samei, Sean Kelly, Xiaoyi Sun, Brooke Ward, Martin Nystrand and Sidney D’Mello Modeling Interactions Across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 Anthony F. Botelho, Seth Adjei and Neil Heffernan Robust Predictive Models on MOOCs : Transferring Knowledge across Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 Sebastien Boyer and Kalyan Veeramachaneni A Comparative Analysis of Techniques for Predicting Student Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Hana Bydžovská Course Enrollment Recommender System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Hana Bydžovská Data-driven Automated Induction of Prerequisite Structure Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Devendra Singh Chaplot, Yiming Yang, Jaime Carbonell and Kenneth R. Koedinger Exploring Learning Management System Interaction Data: Combining Data-driven and Theory-driven Approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 Hongkyu Choi, Ji Eun Lee, Won-Joon Hong, Kyumin Lee, Mimi Recker and Andy Walker A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations . . . . . . . . . . . . . . . . . . . . . . . 330 Benjamin Clement, Pierre-Yves Oudeyer and Manuel Lopes Automatic Assessment of Constructed Response Data in a Chemistry Tutor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Scott Crossley, Kris Kyle, Jodi Davenport and Danielle McNamara Choosing versus Receiving Feedback: The Impact of Feedback Valence on Learning in an Assessment Game. . . . 341 Maria Cutumisu and Daniel L. Schwartz Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Yiling Dai, Yasuhito Asano and Masatoshi Yoshikawa Proceedings of the 9th International Conference on Educational Data Mining xi Student Emotion, Co-occurrence, and Dropout in a MOOC Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 John Dillon, Nigel Bosch, Malolan Chetlur, Nirandika Wanigasekara, G. Alex Ambrose, Bikram Sengupta and Sidney D’Mello Semi-Markov model for simulating MOOC students. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358 Louis Faucon, Lukasz Kidziński and Pierre Dillenbourg Investigating Gender Difference on Homework in Middle School Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Mingyu Feng, Jeremy Roschelle, Craig Mason and Ruchi Bhanot Investigating Difficult Topics in a Data Structures Course Using Item Response Theory and Logged Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Eric Fouh, Mohammed F. Farghally, Sally Hamouda, Kyu Han Koh and Clifford A. Shaffer Acting the Same Differently: A Cross-Course Comparison of User Behavior in MOOCs . . . . . . . . . . . . . . . . . . . . . . . . 376 Ben Gelman, Matt Revelle, Carlotta Domeniconi, Kalyan Veeramachaneni and Aditya Johri Collaborative Problem Solving Skills versus Collaboration Outcomes: Findings from Statistical Analysis and Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 Jiangang Hao, Lei Liu, Alina von Davier, Patrick Kyllonen and Christopher Kitchen Hint Availability Slows Completion Times in Summer Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 Paul Salvador Inventado, Peter Scupelli, Eric Van Inwegen, Korinn Ostrow, Neil Heffernan III, Jaclyn Ocumpaugh, Ryan Baker, Stefan Slater and Mia Almeda On Competition for Undergraduate Co-op Placements: A Graph Mining Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Yuheng Jiang and Lukasz Golab Expediting Support for Social Learning with Behavior Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Yohan Jo, Gaurav Singh Tomar, Oliver Ferschke, Carolyn Rose and Dragan Gasevic On generalizability of MOOC models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406 Lukasz Kidziński, Kshitij Sharma, Mina Shirvani Boroujeni and Pierre Dillenbourg Closing the Loop with Quantitative Cognitive Task Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 Kenneth Koedinger and Elizabeth McLaughlin Does a Peer Recommender Foster Students’ Engagement in MOOCs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 Hugues Labarthe, François Bouchet, Remi Bachelet and Kalina Yacef A Contextual Bandits Framework for Personalized Learning Action Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Andrew Lan and Richard Baraniuk How Good Is Popularity? Summary Grading in Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Haiying Li, Zhiqiang Cai and Art Graesser Beyond Log Files: Using Multi-Modal Data Streams Towards Data-Driven KC Model Improvement . . . . . . . . . . . . 436 Ran Liu, Jodi Davenport and John Stamper Seeking Programming-related Information from Large Scaled Discussion Forums, Help or Harm? . . . . . . . . . . . . . . . 442 Yihan Lu and Sharon Hsiao Classifying behavior to elucidate elegant problem solving in an educational game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 Laura Malkiewich, Ryan S. Baker, Valerie Shute, Shimin Kai and Luc Paquette Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data . . . . . . . . . . . . . 454 Wookhee Min, Joseph Wiggins, Lydia Pezzullo, Alexandria Vail, Kristy Elizabeth Boyer, Bradford Mott, Megan Frankosky, Eric Wiebe and James Lester Proceedings of the 9th International Conference on Educational Data Mining xii Exploring the Impact of Data-driven Tutoring Methods on Students’ Demonstrative Knowledge in Logic Problem Solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Behrooz Mostafavi and Tiffany Barnes Properties and Applications of Wrong Answers in Online Educational Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 Radek Pelánek and Jiřı́ Řihák Using Inverse Planning for Personalized Feedback. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472 Anna Rafferty, Rachel Jansen and Thomas Griffiths Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations . . . . . . . 478 Martina Rau Predicting Performance on MOOC Assessments using Multi-Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484 Zhiyun Ren, Huzefa Rangwala and Aditya Johri Validating Game-based Measures of Implicit Science Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 Elizabeth Rowe, Jodi Asbell-Clarke, Michael Eagle, Andrew Hicks, Tiffany Barnes, Rebecca Brown and Teon Edwards Assessing Student-Generated Design Justifications in Virtual Engineering Internships . . . . . . . . . . . . . . . . . . . . . . . . . . 496 Vasile Rus, Dipesh Gautam, Zach Swiecki, David Shaffer and Art Graesser Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain . . . . . . . . . . . . . . . 502 Shaghayegh Sahebi, Yu-Ru Lin and Peter Brusilovsky Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning. . . . . . . . . . . . . . . . . . . . . . . . 507 Shitian Shen and Min Chi Personalization of Learning Paths in Online Communities of Creators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Mingxuan Sun and Seungwon Yang Modeling Visitor Behavior in a Game-Based Engineering Museum Exhibit with Hidden Markov Models . . . . . . . 517 Mike Tissenbaum, Matthew Berland and Vishesh Kumar Learning Curves for Problems with Multiple Knowledge Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Brett van de Sande A Nonlinear State Space Model for Identifying At-Risk Students in Open Online Courses . . . . . . . . . . . . . . . . . . . . . . 527 Feng Wang and Li Chen Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Miaomiao Wen, Keith Maki, Xu Wang, Steven Dow, James Herbsleb and Carolyn Rose Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation . . . . . . . . . . 539 Kevin Wilson, Yan Karklin, Bojian Han and Chaitanya Ekanadham Going Deeper with Deep Knowledge Tracing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 Xiaolu Xiong, Siyuan Zhao, Eric Vaninwegen and Joseph Beck Boosted Decision Tree for Q-matrix Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Peng Xu and Michel Desmarais Individualizing Bayesian Knowledge Tracing. Are Skill Parameters More Important Than Student Parameters? 556 Michael Yudelson Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading . . . . 562 Yuan Zhang, Rajat Shah and Min Chi Proceedings of the 9th International Conference on Educational Data Mining xiii Posters and Demo Redefining “What” in Analyses of Who Does What in MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Alok Baikadi, Carrie Demmans Epp, Yanjin Long and Christian Schunn Text Classification of Student Self-Explanations in College Physics Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Sameer Bhatnagar, Michel Desmarais, Nathaniel Lasry and Elizabeth Charles Automated Feedback on the Quality of Collaborative Processes: An Experience Report . . . . . . . . . . . . . . . . . . . . . . . . 573 Marcela Borge and Carolyn Rosé Mining Sequences of Gameplay for Embedded Assessment in Collaborative Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 575 Philip Buffum, Megan Frankosky, Kristy Boyer, Eric Wiebe, Bradford Mott and James Lester Can Word Probabilities from LDA be Simply Added up to Represent Documents? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577 Zhiqiang Cai, Haiying Li, Xiangen Hu and Art Graesser Examining the necessity of problem diagrams using MOOC AB experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 Zhongzhou Chen, Neset Demirci and David Pritchard Identifying relevant user behavior and predicting learning and persistence in an ITS-based afterschool program 581 Scotty Craig, Xudong Huang, Jun Xie, Ying Fang and Xiangen Hu Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data . . . . 583 Nicholas Diana, Michael Eagle, John Stamper and Kenneth Koedinger Exploring Social Influence on the Usage of Resources in an Online Learning Community . . . . . . . . . . . . . . . . . . . . . . . 585 Ogheneovo Dibie, Tamara Sumner, Keith Maull and David Quigley Time Series Cross Section method for monitoring students’ page views of course materials and improving classroom teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Konomu Dobashi Predicting STEM Achievement with Learning Management System Data: Prediction Modeling and a Test of an Early Warning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 Michelle Dominguez, Matthew Bernacki and Merlin Uesbeck Comparison of Selection Criteria for Multi-Feature Hierarchical Activity Mining in Open Ended Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 Yi Dong, John S. Kinnebrew and Gautam Biswas A Data-Driven Framework of Modeling Skill Combinations for Deeper Knowledge Tracing . . . . . . . . . . . . . . . . . . . . . 593 Yun Huang, Julio Guerra and Peter Brusilovsky Generating Semantic Concept Map for MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Zhuoxuan Jiang, Peng Li, Yan Zhang and Xiaoming Li How to Judge Learning on Online Learning: Minimum Learning Judgment System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 Jaechoon Jo and Heuiseok Lim Guiding Students Towards Frequent High-Utility Paths in an Ill-Defined Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 Igor Jugo, Božidar Kovačić and Vanja Slavuj Portrait of an Indexer - Computing Pointers Into Instructional Videos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Andrew Lamb, Jose Hernandez, Jeffrey Ullman and Andreas Paepcke Hierarchical Cluster Analysis Heatmaps and Pattern Analysis: An Approach for Visualizing Learning Management System Interaction Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Ji Eun Lee, Mimi Recker, Alex Bowers and Min Yuan Proceedings of the 9th International Conference on Educational Data Mining xiv Understanding Engagement in MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605 Qiujie Li and Rachel Baker How quickly can wheel spinning be detected? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 Noboru Matsuda, Sanjay Chandrasekaran and John Stamper Exploring and Following Students’ Strategies When Completing Their Weekly Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Jessica McBroom, Bryn Jeffries, Irena Koprinska and Kalina Yacef Identifying Student Behaviors Early in the Term for Improving Online Course Performance . . . . . . . . . . . . . . . . . . . . 611 Makoto Mori and Philip Chan Time Series Analysis of VLE Activity Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Ewa Mlynarska, Pádraig Cunningham and Derek Greene Massively Scalable EDM with Spark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Tristan Nixon Study on Automatic Scoring of Descriptive Type Tests using Text Similarity Calculations . . . . . . . . . . . . . . . . . . . . . . 616 Izuru Nogaito, Keiji Yasuda and Hiroaki Kimura Equity of Learning Opportunities in the Chicago City of Learning Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618 David Quigley, Ogheneovo Dibie, Arafat Sultan, Katie Van Horne, William R. Penuel, Tamara Sumner, Ugochi Acholonu and Nichole Pinkard Novel features for capturing cooccurrence behavior in dyadic collaborative problem solving tasks . . . . . . . . . . . . . . . 620 Vikram Ramanarayanan and Saad Khan Adding eye-tracking AOI data to models of representation skills does not improve prediction accuracy . . . . . . . . . 622 Martina Rau and Zach Pardos MATHia X: The Next Generation Cognitive Tutor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 Steven Ritter and Stephen Fancsali Towards Integrating Human and Automated Tutoring Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626 Steven Ritter, Michael Yudelson, Stephen Fancsali and Susan Berman Toward Revision-Sensitive Feedback in Automated Writing Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 Rod Roscoe, Matthew Jacovina, Laura Allen, Adam Johnson and Danielle McNamara Preliminary Results On Dialogue Act and Subact Classification in Chat-based Online Tutorial Dialogues . . . . . . . 630 Vasile Rus, Rajendra Banjade, Nabin Maharjan, Donald Morrison, Steve Ritter and Michael Yudelson SAS Tools for Educational Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632 Jennifer Sabourin, Scott Mcquiggan and André De Waal Applicability of Educational Data Mining in Afghanistan: Opportunities and Challenges . . . . . . . . . . . . . . . . . . . . . . . 634 Abdul Rahman Sherzad Browsing-Pattern Mining from e-Book Logs with Non-negative Matrix Factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 Atsushi Shimada, Fumiya Okubo and Hiroaki Ogata How employment constrains participation in MOOCs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638 Mina Shirvani Boroujeni, Lukasz Kidziński and Pierre Dillenbourg Quantifying How Students Use an Online Learning System: A Focus on Transitions and Performance . . . . . . . . . . 640 Erica Snow, Andrew Krumm, Timothy Podkul, Mingyu Feng and Alex Bowers A Platform for Integrating and Analyzing Data to Evaluate the Impacts of Educational Technologies . . . . . . . . . . 642 Daniel Stanhope and Karl Rectanus Proceedings of the 9th International Conference on Educational Data Mining xv Educational Technology: What 49 Schools Discovered about Usage when the Data were Uncovered . . . . . . . . . . . . 644 Daniel Stanhope and Karl Rectanus Learning curves versus problem difficulty: an analysis of the Knowledge Component picture for a given context 646 Brett van de Sande Validating Automated Triggers and Notifications @ Scale in Blackboard Learn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 John Whitmer, Aleksander Dietrichson and Bryan O’Haver Discovering ’Tough Love’ Interventions Despite Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650 Joseph Jay Williams, Anthony Botelho, Adam Sales, Neil Heffernan and Charles Lang Stimulating collaborative activity in online social learning environments with Markov decision processes. . . . . . . . 652 Matthew Yee-King and Mark D’Inverno Predicting student grades from online, collaborative social learning metrics using K-NN . . . . . . . . . . . . . . . . . . . . . . . . 654 Matthew Yee-King, Andreu Grimalt-Reynes and Mark D’Inverno Meta-learning for predicting the best vote aggregation method: Case study in collaborative searching of LOs . . . 656 Alfredo Zapata González, Victor Menendez, Cristobal Romero and Manuel E. Prieto Méndez Soft Clustering of Physics Misconceptions Using a Mixed Membership Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 Guoguo Zheng, Seohyun Kim, Yanyan Tan and April Galyardt Perfect Scores Indicate Good Students !? The Case of One Hundred Percenters in a Math Learning System . . . . 660 Zhilin Zheng, Martin Stapel and Niels Pinkwart Doctoral Consortium Towards the Understanding of Gestures and Vocalization Coordination in Teaching Context . . . . . . . . . . . . . . . . . . . 663 Roghayeh Barmaki and Charles E. Hughes Towards Modeling Chunks in a Knowledge Tracing Framework for Students’ Deep Learning. . . . . . . . . . . . . . . . . . . . 666 Yun Huang and Peter Brusilovsky Using Case-Based Reasoning to Automatically Generate High-Quality Feedback for Programming Exercises . . . . 669 Angelo Kyrilov Predicting Off-task Behaviors for Adaptive Vocabulary Learning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 Sungjin Nam Estimation of prerequisite skills model from large scale assessment data using semantic data mining . . . . . . . . . . . . 675 Bruno Penteado Designing Interactive and Personalized Concept Mapping Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678 Shang Wang Industry Track - Short Papers Analysing and Refining Pilot Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 Bruno Emond, Scott Buffett, Cyril Goutte and Jaff Guo A Scalable Learning Analytics Platform for Automated Writing Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 Jacqueline Feild, Nicolas Lewkow, Neil Zimmerman, Mark Riedesel and Alfred Essa An Automated Test of Motor Skills for Job Selection and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Bhanu Pratap Singh Rawat and Varun Aggarwal Proceedings of the 9th International Conference on Educational Data Mining xvi Industry Track - Posters Studying Assignment Size and Student Performance Using Propensity Score Matching . . . . . . . . . . . . . . . . . . . . . . . . . 701 Shirin Mojarad Toward Automated Support for Teacher-Facilitated Formative Feedback on Student Writing . . . . . . . . . . . . . . . . . . . 703 Jennifer Sabourin, Lucy Kosturko, Kristin Hoffmann and Scott Mcquiggan TutorSpace: Content-centric Platform for Enabling Blended Learning in Developing Countries . . . . . . . . . . . . . . . . . 705 Kuldeep Yadav, Kundan Shrivastava, Ranjeet Kumar, Saurabh Srivastava and Om Deshmukh Proceedings of the 9th International Conference on Educational Data Mining xvii

Proceedings of the 9th International Conference on Educational

Related documents

Products

Support

Proceedings of the 9th International Conference on Educational

Related documents

Add this document to collection(s)

Add this document to saved

Suggest us how to improve StudyLib