Proceedings of the International Conference on Educational Data Mining (EDM) (6th, Memphis, Tennessee, July 6-9, 2013)
Author: D'Mello, S. K., Ed.
Date: 2013-07-00
Type: Collected Works - Proceedings
Subjects: Data Analysis,Educational Research,Educational Technology,Interdisciplinary Approach,Elementary Secondary Education,Higher Education,Educational Games,Prediction,Bayesian Statistics,Intelligent Tutoring Systems,Computer Uses in Education,Evaluation

Since its inception in 2008, the Educational Data Mining (EDM) conference series has featured some of the most innovative and fascinating basic and applied research centered on data mining, education, and learning technologies. This tradition of exemplary interdisciplinary research has been kept alive in 2013 as evident through an imaginative, exciting, and diverse set of papers spanning the fields of Machine Learning, Artificial Intelligence, Learning Technologies, Education, Linguistics, and Psychology. The following were presented at this sixth installment of the International Conference on Educational Data Mining (EDM 2013), held in Memphis, Tennessee from the 6th to 9th of July 2013: (1) Stealth Assessment in Games: Why, What, and How (Valerie Shute); (2) Discovering the Structure of Mathematical Problem Solving (John R. Anderson); (3) EDM in a Complex and Changing World (Ryan S. J. D. Baker); (4) Limits to Accuracy: How Well Can We Do at Student Modeling? (Joseph Beck and Xiaolu Xiong); (5) Student Profiling from Tutoring System Log Data: When do Multiple Graphical Representations Matter? (Ryan Carlson, Konstantin Genin, Martina Rau and Richard Scheines); (6) Unsupervised Classification of Student Dialogue Acts with Query-Likelihood Clustering (Aysu Ezen-Can and Kristy Elizabeth Boyer); (7) A Spectral Learning Approach to Knowledge Tracing (Mohammad H. Falakmasir, Zachary A. Pardos, Geoffrey J. Gordon and Peter Brusilovsky); (8) Optimal and Worst-Case Performance of Mastery Learning Assessment with Bayesian Knowledge Tracing (Stephen Fancsali, Tristan Nixon and Steven Ritter); (9) Automatically Recognizing Facial Expression: Predicting Engagement and Frustration (Joseph Grafsgaard, Joseph B. Wiggins, Kristy Elizabeth Boyer, Eric N. Wiebe and James Lester); (10) Investigating the Solution Space of an Open-Ended Educational Game Using Conceptual Feature Extraction (Erik Harpstead, Christopher J. MacLellan, Kenneth R. Koedinger, Vincent Aleven, Steven P. Dow and Brad A. Myers); (11) Extending the Assistance Model: Analyzing the Use of Assistance over Time (William Hawkins, Neil Heffernan, Yutao Wang and Ryan S. J. D. Baker); (12) Differential Pattern Mining of Students' Handwritten Coursework (James Herold, Alex Zundel and Thomas Stahovich); (13) Predicting Future Learning Better Using Quantitative Analysis of Moment-by-Moment Learning (Arnon Hershkovitz, Ryan S. J. D. Baker, Sujith M Gowda and Albert T. Corbett; (14) InVis: An Interactive Visualization Tool for Exploring Interaction Networks (Matthew Johnson, Michael Eagle and Tiffany Barnes); (15) Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics (Andrew Lan, Christoph Studer, Andrew Waters and Richard Baraniuk); (16) Discovering Student Models with a Clustering Algorithm Using Problem Content (Nan Li, William Cohen and Kenneth R. Koedinger); (17) Predicting Player Moves in an Educational Game: A Hybrid Approach (Yun-En Liu, Travis Mandel, Eric Butler, Erik Andersen, Eleanor O'Rourke, Emma Brunskill and Zoran Popovi); (18) Sequences of Frustration and Confusion, and Learning(Zhongxiu Liu, Visit Pataranutaporn, Jaclyn Ocumpaugh and Ryan S. J. D. Baker); (19) Data Mining in the Classroom: Discovering Groups Strategies at a Multi-Tabletop Environment (Roberto Martinez-Maldonado, Kalina Yacef and Judy Kay); (20) Meta-Reasoning Algorithm for Improving Analysis of Student Interactions with Learning Objects using Supervised Learning (L. Dee Miller and Leen-Kiat Soh); (21) Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX (Zachary Pardos, Yoav Bergner, Daniel Seaton and David Pritchard); (22) Modeling and Optimizing Forgetting and Spacing Effects during Musical Interval Training (Philip I. Pavlik Jr., Henry Hua, Jamal Williams and Gavin Bidelman); (23) Tuned Models of Peer Assessment in MOOCs (Chris Piech, Jon Huang, Zhenghao Chen, Chuong Do, Andrew Ng and Daphne Koller); (24) Does Representational Understanding Enhance Fluency Or Vice Versa? Searching for Mediation Models (Martina Rau, Richard Scheines, Vincent Aleven and Nikol Rummel); (25) Predicting Standardized Test Scores from Cognitive Tutor Interactions (Steve Ritter, Ambarish Joshi, Stephen Fancsali and Tristan Nixon); (26) Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School (Maria Ofelia Clarissa San Pedro, Ryan S. J. D. Baker, Alex Bowers and Neil Heffernan); (27) Incorporating Scaffolding and Tutor Context into Bayesian Knowledge Tracing to Predict Inquiry Skill Acquisition (Michael Sao Pedro, Ryan S. J. D. Baker and Janice Gobert); (28) Applying Three Models of Learning to Individual Student Log Data (Brett van de Sande); (29) Evaluating Topic-Word Review Analysis for Understanding Student Peer Review Performance (Wenting Xiong and Diane Litman); (30) Mining Social Deliberation in Online Communication - If You Were Me and I Were You (Xiaoxi Xu, Tom Murray, Beverly Park Woolf and David Smith); (31) Paragraph Specific N-Gram Approaches to Automatically Assessing Essay Quality (Scott Crossley, Caleb Defore, Kris Kyle, Jianmin Dai and Danielle S. Mcnamara); (32) Degeneracy in Student Modeling with Dynamic Bayesian Networks in Intelligent Edu-Games (Alireza Davoodi and Cristina Conati); (33) Clustering and Visualizing Study State Sequences (Michel Desmarais and Francois Lemieux); (34) Analyzing the Mental Health of Engineering Students using Classification and Regression (Melissa Deziel, Dayo Olawo, Lisa Truchon and Lukasz Golab); (35) Hints: You Can't Have Just One (Ilya Goldin, Kenneth Koedinger and Vincent Aleven); (36) What and When do Students Learn? Fully Data-Driven Joint Estimation of Cognitive and Student Models (Jose Gonzalez-Brenes and Jack Mostow); (37) An Investigation of Psychometric Measures for Modelling Academic Performance in Tertiary Education (Geraldine Gray, Colm McGuinness and Philip Owende); (38) Modeling Affect in Student-Driven Learning Scenarios (Paul Salvador Inventado, Roberto Legaspi, Rafael Cabredo and Masayuki Numao); (39) An Algorithm for Reducing the Complexity of Interaction Networks (Matthew Johnson, Michael Eagle, John Stamper and Tiffany Barnes); (40) Mining Temporally-Interesting Learning Behavior Patterns (John Kinnebrew, Daniel Mack and Gautam Biswas); (41) Modeling Students' Learning and Variability of Performance in Problem Solving (Radek Pelanek, Petr Jarusek and Matej Klusacek); (42) Estimating Student Knowledge from Paired Interaction Data (Anna Raerty, Jodi Davenport and Emma Brunskill); (43) Using a Lexical Analysis of Students Self-Explanation to Predict Course Performance (Nicholas Rhodes, Matthew Ung, Alexander Zundel, Jim Herold and Thomas Stahovich); (44) A Meta-Learning Approach for Recommending a Subset of White-Box Classification Algorithms for Moodle Datasets (Cristobal Romero, Juan Luis Olmo and Sebastian Ventura); (45) Investigating the Effects of Off-Task Personalization on System Performance and Attitudes within a Game-Based Environment (Erica Snow, G. Tanner Jackson, Laura Varner and Danielle S. McNamara); (46) Students Walk through Tutoring: Using a Random Walk Analysis to Profile Students (Erica L. Snow, Aaron D. Likens, G. Tanner Jackson and Danielle S. McNamara); (47) From Events to Activities: Creating Abstraction Techniques for Mining Students Model-Based Inquiry Processes (Vilaythong Southavilay, Lina Markauskaite and Michael J. Jacobson); (48) A Comparison of Model Selection Metrics in DataShop (John Stamper, Kenneth Koedinger and Elizabeth McLaughlin); (49) Measuring the Moment of Learning with An Information-Theoretic Approach (Brett van de Sande); (50) Test-Size Reduction for Concept Estimation (Divyanshu Vats, Christoph Studer, Andrew S. Lan, Lawrence Carin and Richard Baraniuk); (51) Reading into the Text: Investigating the Influence of Text Complexity on Cognitive Engagement (Benjamin Vega, Shi Feng, Blair Lehman, Art Graesser and Sidney D'Mello); (52) Using Students' Programming Behavior to Predict Success in an Introductory Mathematics Course (Arto Vihavainen, Matti Luukkainen and Jaakko Kurhila); (53) Do Students Really Learn an Equal Amount Independent of Whether They Get an Item Correct or Wrong? (Seth Adjei, Seye Salehizadeh, Yutao Wang and Neil Heffernan); (54) Analysis of Students Clustering Results Based on Moodle Log Data (Angela Bovo, Stephane Sanchez, Olivier Heguy and Yves Duthen); (55) Mining the Impact of Course Assignments on Student Performance (Ritu Chaturvedi and Christie Ezeife); (56) Mining Users Behaviors in Intelligent Educational Games: Prime Climb a Case Study (Alireza Davoodi, Samad Kardan and Cristina Conati); (57) Bringing Student Backgrounds Online: MOOC User Demographics, Site Usage, and Online Learning (Jennifer Deboer, Glenda S. Stump, Daniel Seaton, Andrew Ho, David E. Pritchard and Lori Breslow); (58) Detecting Player Goals from Game Log Files (Kristen Dicerbo and Khusro Kidwai); (59) A Prediction Model that Uses the Sequence of Attempts and Hints to Better Predict Knowledge: Better to Attempt the Problem First, Rather Than Ask for a Hint (Hien Duong, Linglong Zhu, Yutao Wang and Neil Heffernan); (60) Towards the Development of a Classification Service for Predicting Students' Performance (Diego Garca-Saiz and Marta Zorrilla); (61) Identifying and Visualizing the Similarities Between Course Content at a Learning Object, Module and Program Level (Kyle Goslin and Markus Hofmann); (62) Using ITS Generated Data to Predict Standardized Test Scores (Kim Kelly, Ivon Arroyo and Neil Heffernan); (63) Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data (Andrew Lan, Christoph Studer, Andrew Waters and Richard Baraniuk); (64) Component Model in Discourse Analysis (Haiying Li, Art Graesser and Zhiqiang Cai); (65) Modeling Student Retention in an Environment with Delayed Testing (Shoujing Li, Xiaolu Xiong and Joseph Beck); (66) Predicting Group Programming Project Performance using SVN Activity Traces (Sen Liu, Jihie Kim and Sofus Macskassy); (67) Toward Predicting Test Score Gains with Online Behavior Data of Teachers (Keith Maull and Tamara Sumner); (68) Domain-Independent Proximity Measures in Intelligent Tutoring Systems; (Bassam Mokbel, Sebastian Gross, Benjamin Paassen, Niels Pinkwart and Barbara Hammer); (69) Exploring Exploration: Inquiries into Exploration Behavior in Complex Problem Solving Assessment (Jonas Muller, Andre Kretzschmar and Samuel Greiff); (70) The Complex Dynamics of Aggregate Learning Curves (Tristan Nixon, Stephen Fancsali and Steven Ritter); (71) Extracting Time-Evolving Latent Skills from Examination Time Series (Shinichi Oeda and Kenji Yamanishi); (72) Uncovering Class-Wide Patterns in Responses to True/False Questions (Andrew Pawl); (73) Causal Modeling to Understand the Relationship between Student Attitudes, Affect and Outcomes (Dovan Rai, Joseph Beck and Ivon Arroyo); (74) Determining Review Coverage by Extracting Topic Sentences Using A Graph-Based Clustering Approach (Lakshmi Ramachandran, Balaraman Ravindran and Edward Gehringer); (75) Affective State Detection in Educational Systems through Mining Multimodal Data Sources (Sergio Salmeron-Majadas, Olga C. Santos and Jesus G. Boticario); (76) Exploring the Relationship between Course Structure and eText Usage in Blended and Open Online Courses (Daniel T. Seaton, Yoav Bergner and David E. Pritchard); (77) Data Preprocessing Using a Priori Knowledge (Jean Simon); (78) Discovering the Relationship between Student Effort and Ability for Predicting the Performance of Technology-Assisted Learning in a Mathematics After-School Program (Jun Xie, Xudong Huang, Henry Hua, Jin Wang, Quan Tang, Scotty Craig, Arthur Graesser, King-Ip Lin and Xiangen Hu); (79) Using Item Response Theory to Rene Knowledge Tracing (Yanbo Xu and Jack Mostow); (80) Estimating the Benefits of Student Model Improvements on a Substantive Scale (Michael Yudelson and Kenneth Koedinger); (81) A Dynamic Group Composition Method to Refine Collaborative Learning Group Formation (Zhilin Zheng); (82) Educational Data Mining: Illuminating Student Learning Pathways in an Online Fraction Game (Ani Aghababyan, Taylor Martin, Nicole Forsgren Velasquez and Philip Janisiewicz); (83) Automatic Gaze-Based Detection of Mind Wandering during Reading (Sidney D'Mello, Jonathan Cobian and Matthew Hunter); (84) DARE: Deep Anaphora Resolution in Dialogue based Intelligent Tutoring Systems (Nobal B. Niraula, Vasile Rus and Dan Stefanescu); (85) Are You Committed? Investigating Interactions among Reading Commitment, Natural Language Input, and Students Learning Outcomes (Laura K. Varner, G. Tanner Jackson, Erica L. Snow and Danielle S. McNamara); (86) Using Multi-level Models to Assess Data From an Intelligent Tutoring System (Jennifer Weston and Danielle S. McNamara); (87) Evaluation of Automatically Generated Hint Feedback (Michael Eagle and Tiffany Barnes); (88) Analysing Engineering Expertise of High School Students Using Eye Tracking and Multimodal Learning Analytics (July Gomes, Mohamed Yassine, Marcelo Worsley and Paulo Blikstein); (89) Investigating the Efficacy of Algorithmic Student Modelling in Predicting Students at Risk of Failing in Tertiary Education (Geraldine Gray, Colm McGuinness and Philip Owende); (90) BOTS: Harnessing Player Data and Player Effort to Create and Evaluate Levels in a Serious Game (Andrew Hicks); (91) Helping Students Manage Personalized Learning Scenarios (Paul Salvador Inventado, Roberto Legaspi and Masayuki Numao); (92) Determining Problem Selection for a Logic Proof Tutor (Behrooz Mostafavi and Tiffany Barnes); (93) Demonstration of a Moodle Student Monitoring Web Application (Angela Bovo, Stephane Sanchez, Olivier Heguy and Yves Duthen); (94) Students Activity Visualization Tool (Marius Stefan Chiritoiu, Cristian Mihaescu and Dumitru Dan Burdescu); (95) FlexCCT: Software for Maximum Likelihood Cultural Consensus Theory (Stephen France, Mahyar Vaghe and William Batchelder); (96) Visual Exploration of Interactions and Performance with LeMo. (Agathe Merceron, Sebastian Schwarzrock, Margarita Elkina, Andreas Pursian, Liane Beuster, Albrecht Fortenbacher, Leonard Kappe and Boris Wenzla); (97) Project CASSI: A Social-Graph Based Tool for Classroom Behavior Analysis and Optimization (Robert Olson, Zachary Daily, John Malayny and Robert Szkutak); (98) A Moodle Block for Selecting, Visualizing and Mining Students' Usage Data (Cristobal Romero, Cristobal Castro and Sebastian Ventura); (99) SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language Inputs (Vasile Rus, Rajendra Banjade, Mihai Lintean, Nobal Niraula and Dan Stefanescu); (100) Gathering Emotional Data from Multiple Sources (Sergio Salmeron-Majadas, Olga C. Santos, Jesus G. Boticario, Raul Cabestrero, Pilar Quiros and Mar Saneiro); and (101) A Tool for Speech Act Classification Using Interactive Machine Learning (Borhan Samei, Fazel Keshtkar and Arthur C. Graesser). Individual presentations contain references. [For &quot;Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)&quot;, see ED537074.]