Table of Contents

• Stealth Assessment in Games: Why, What, and HowValerie Shute. 1 [doi]
• Discovering the Structure of Mathematical Problem SolvingJohn R. Anderson. 2 [doi]
• EDM in a Complex and Changing WorldRyan Baker. 3 [doi]
• Limits to accuracy: how well can we do at student modeling?Joseph Beck, Xiaolu Xiong. 4-11 [doi]
• Student Profiling from Tutoring System Log Data: When do Multiple Graphical Representations Matter?Ryan Carlson, Konstantin Genin, Martina Rau, Richard Scheines. 12-19 [doi]
• Unsupervised Classification of Student Dialogue Acts with Query-Likelihood ClusteringAysu Ezen-Can, Kristy Elizabeth Boyer. 20-27 [doi]
• A Spectral Learning Approach to Knowledge TracingMohammad Hassan Falakmasir, Zachary A. Pardos, Geoffrey J. Gordon, Peter Brusilovsky. 28-34 [doi]
• Optimal and Worst-Case Performance of Mastery Learning Assessment with Bayesian Knowledge TracingStephen Fancsali, Tristan Nixon, Steven Ritter. 35-42 [doi]
• Automatically Recognizing Facial Expression: Predicting Engagement and FrustrationJoseph F. Grafsgaard, Joseph B. Wiggins, Kristy Elizabeth Boyer, Eric N. Wiebe, James C. Lester. 43-50 [doi]
• Investigating the Solution Space of an Open-Ended Educational Game Using Conceptual Feature ExtractionErik Harpstead, Christopher J. MacLellan, Kenneth R. Koedinger, Vincent Aleven, Steven P. Dow, Brad A. Myers. 51-58 [doi]
• Extending the Assistance Model: Analyzing the Use of Assistance over TimeWilliam J. Hawkins, Neil T. Heffernan, Yutao Wang, Ryan Shaun Baker. 59-66 [doi]
• Differential Pattern Mining of Students' Handwritten CourseworkJames Herold, Alex Zundel, Thomas F. Stahovich. 67-73 [doi]
• Predicting Future Learning Better Using Quantitative Analysis of Moment-by-Moment LearningArnon Hershkovitz, Ryan Shaun Baker, Sujith M. Gowda, Albert T. Corbett. 74-81 [doi]
• InVis: An Interactive Visualization Tool for Exploring Interaction NetworksMatthew Johnson, Michael Eagle, Tiffany Barnes. 82-89 [doi]
• Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content AnalyticsAndrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk. 90-97 [doi]
• Discovering Student Models with a Clustering Algorithm Using Problem ContentNan Li, William W. Cohen, Kenneth R. Koedinger. 98-105 [doi]
• Predicting Player Moves in an Educational Game: A Hybrid ApproachYun-En Liu, Travis Mandel, Eric Butler, Erik Andersen, Eleanor O'Rourke, Emma Brunskill, Zoran Popovic. 106-113 [doi]
• Sequences of Frustration and Confusion, and LearningZhongxiu Liu, Visit Pataranutaporn, Jaclyn Ocumpaugh, Ryan Shaun Baker. 114-120 [doi]
• Data Mining in the Classroom: Discovering Groups' Strategies at a Multi-tabletop EnvironmentRoberto Martínez Maldonado, Kalina Yacef, Judy Kay. 121-128 [doi]
• Meta-Reasoning Algorithm for Improving Analysis of Student Interactions with Learning Objects using Supervised LearningLee Dee Miller, Leen-Kiat Soh. 129-136 [doi]
• Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edXZachary A. Pardos, Yoav Bergner, Daniel T. Seaton, David E. Pritchard. 137-144 [doi]
• Modeling and Optimizing Forgetting and Spacing Effects during Musical Interval TrainingPhilip I. Pavlik, Henry Hua, Jamal Williams, Gavin M. Bidelman. 145-152 [doi]
• Tuned Models of Peer Assessment in MOOCsChris Piech, Jonathan Huang, Zhenghao Chen, Chuong B. Do, Andrew Y. Ng, Daphne Koller. 153-160 [doi]
• Does Representational Understanding Enhance Fluency - Or Vice Versa? Searching for Mediation ModelsMartina A. Rau, Richard Scheines, Vincent Aleven, Nikol Rummel. 161-168 [doi]
• Predicting Standardized Test Scores from Cognitive Tutor InteractionsSteven Ritter, Ambarish Joshi, Stephen Fancsali, Tristan Nixon. 169-176 [doi]
• Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle SchoolMaria Ofelia Clarissa Z. San Pedro, Ryan Shaun Baker, Alex Bowers, Neil T. Heffernan. 177-184 [doi]
• Incorporating Scaffolding and Tutor Context into Bayesian Knowledge Tracing to Predict Inquiry Skill AcquisitionMichael Sao Pedro, Ryan Shaun Baker, Janice D. Gobert. 185-192 [doi]
• Applying three models of learning to individual student log dataBrett van de Sande. 193-199 [doi]
• Evaluating Topic-Word Review Analysis for Understanding Student Peer Review PerformanceWenting Xiong, Diane J. Litman. 200-207 [doi]
• Mining Social Deliberation in Online Communication - If You Were Me and I Were YouXiaoxi Xu, Tom Murray, Beverly Park Woolf, David A. Smith. 208-215 [doi]
• Paragraph Specific N-Gram Approaches to Automatically Assessing Essay QualityScott A. Crossley, Caleb Defore, Kris Kyle, Jianmin Dai, Danielle S. McNamara. 216-219 [doi]
• Degeneracy in Student Modeling with Dynamic Bayesian Networks in Intelligent Edu-GamesAlireza Davoodi, Cristina Conati. 220-223 [doi]
• Clustering and Visualizing Study State SequencesMichel Desmarais, François Lemieux. 224-227 [doi]
• Analyzing the Mental Health of Engineering Students using Classification and RegressionMelissa Deziel, Dayo Olawo, Lisa Truchon, Lukasz Golab. 228-231 [doi]
• Hints: You Can't Have Just OneIlya M. Goldin, Kenneth R. Koedinger, Vincent Aleven. 232-235 [doi]
• What and When do Students Learn? Fully Data-Driven Joint Estimation of Cognitive and Student ModelsJosé P. González-Brenes, Jack Mostow. 236-239 [doi]
• An Investigation of Psychometric Measures for Modelling Academic Performance in Tertiary EducationGeraldine Gray, Colm McGuinness, Philip Owende. 240-243 [doi]
• Modeling Affect in Student-driven Learning ScenariosPaul Salvador Inventado, Roberto S. Legaspi, Rafael Cabredo, Masayuki Numao. 244-247 [doi]
• An Algorithm for Reducing the Complexity of Interaction NetworksMatthew Johnson, Michael Eagle, John C. Stamper, Tiffany Barnes. 248-251 [doi]
• Mining Temporally-Interesting Learning Behavior PatternsJohn S. Kinnebrew, Daniel L. C. Mack, Gautam Biswas. 252-255 [doi]
• Modeling Students' Learning and Variability of Performance in Problem SolvingRadek Pelánek, Petr Jarusek, Matej Klusácek. 256-259 [doi]
• Estimating Student Knowledge from Paired Interaction DataAnna N. Rafferty, Jodi Davenport, Emma Brunskill. 260-263 [doi]
• Using a Lexical Analysis of Student's Self-Explanation to Predict Course PerformanceNicholas Rhodes, Matthew Ung, Alexander Zundel, James Herold, Thomas F. Stahovich. 264-267 [doi]
• A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasetsCristóbal Romero, Juan Luis Olmo, Sebastián Ventura. 268-271 [doi]
• Investigating the Effects of Off-Task Personalization on System Performance and Attitudes within a Game-Based EnvironmentErica L. Snow, G. Tanner Jackson, Laura K. Varner, Danielle S. McNamara. 272-275 [doi]
• Students' Walk through Tutoring: Using a Random Walk Analysis to Profile StudentsErica L. Snow, Aaron D. Likens, G. Tanner Jackson, Danielle S. McNamara. 276-279 [doi]
• From "Events" to "Activities": Creating Abstraction Techniques for Mining Students' Model-Based Inquiry ProcessesVilaythong Southavilay, Lina Markauskaite, Michael J. Jacobson. 280-283 [doi]
• A Comparison of Model Selection Metrics in DataShopJohn C. Stamper, Kenneth R. Koedinger, Elizabeth A. McLaughlin. 284-287 [doi]
• Measuring the moment of learning with an information-theoretic approachBrett van de Sande. 288-291 [doi]
• Test-size Reduction for Concept EstimationDivyanshu Vats, Christoph Studer, Andrew S. Lan, Lawrence Carin, Richard G. Baraniuk. 292-295 [doi]
• Reading into the Text: Investigating the Influence of Text Complexity on Cognitive EngagementBenjamin Vega, Shi Feng, Blair Lehman, Arthur C. Graesser, Sidney K. D'Mello. 296-299 [doi]
• Using Students' Programming Behavior to Predict Success in an Introductory Mathematics CourseArto Vihavainen, Matti Luukkainen, Jaakko Kurhila. 300-303 [doi]
• Do students really learn an equal amount independent of whether they get an item correct or wrong?Seth Adjei, Seye Salehizadeh, Yutao Wang, Neil T. Heffernan. 304-305 [doi]
• Analysis of students clustering results based on Moodle log dataAngela Bovo, Stéphane Sanchez, Olivier Heguy, Yves Duthen. 306-307 [doi]
• Mining the Impact of Course Assignments on Student PerformanceRitu Chaturvedi, Christie I. Ezeife. 308-309 [doi]
• Mining User's Behaviors in Intelligent Educational Games: Prime Climb a Case StudyAlireza Davoodi, Samad Kardan, Cristina Conati. 310-311 [doi]
• Bringing student backgrounds online: MOOC user demographics, site usage, and online learningJennifer DeBoer, Glenda S. Stump, Daniel T. Seaton, Andrew Ho, David E. Pritchard, Lori Breslow. 312-313 [doi]
• Detecting Player Goals from Game Log FilesKristen E. DiCerbo, Khusro Kidwai. 314-315 [doi]
• 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, Neil T. Heffernan. 316-317 [doi]
• Towards the development of a classification service for predicting students' performanceDiego García-Saiz, Marta E. Zorrilla. 318-319 [doi]
• Identifying and Visualizing the Similarities Between Course Content at a Learning Object, Module and Program LevelKyle Goslin, Markus Hofmann. 320-321 [doi]
• Using ITS Generated Data to Predict Standardized Test ScoresKim M. Kelly, Ivon Arroyo, Neil T. Heffernan. 322-323 [doi]
• Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response DataAndrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk. 324-325 [doi]
• Component Model in Discourse AnalysisHaiying Li, Arthur C. Graesser, Zhiqiang Cai. 326-327 [doi]
• Modeling student retention in an environment with delayed testingShoujing Li, Xiaolu Xiong, Joseph Beck. 328-329 [doi]
• Predicting Group Programming Project Performance using SVN Activity TracesSen Liu, Jihie Kim, Sofus A. Macskassy. 330-331 [doi]
• Toward Predicting Test Score Gains With Online Behavior Data of TeachersKeith E. Maull, Tamara Sumner. 332-333 [doi]
• Domain-Independent Proximity Measures in Intelligent Tutoring SystemsBassam Mokbel, Sebastian Gross, Benjamin Paaßen, Niels Pinkwart, Barbara Hammer. 334-335 [doi]
• Exploring Exploration: Inquiries into Exploration Behavior in Complex Problem Solving AssessmentJonas C. Müller, André Kretzschmar, Samuel Greiff. 336-337 [doi]
• The Complex Dynamics of Aggregate Learning CurvesTristan Nixon, Stephen Fancsali, Steven Ritter. 338-339 [doi]
• Extracting Time-evolving Latent Skills from Examination Time SeriesShinichi Oeda, Kenji Yamanishi. 340-341 [doi]
• Uncovering Class-Wide Patterns in Responses to True/False QuestionsAndrew Pawl. 342-343 [doi]
• Causal Modeling to Understand the Relationship between Student Attitudes, Affect and OutcomesDovan Rai, Joseph Beck, Ivon Arroyo. 344-345 [doi]
• Determining Review Coverage by Extracting Topic Sentences Using A Graph-based Clustering ApproachLakshmi Ramachandran, Balaraman Ravindran, Edward F. Gehringer. 346-347 [doi]
• Affective State Detection in Educational Systems through Mining Multimodal Data SourcesSergio Salmeron-Majadas, Olga C. Santos, Jesus Boticario. 348-349 [doi]
• Exploring the relationship between course structure and etext usage in blended and open online coursesDaniel T. Seaton, Yoav Bergner, David E. Pritchard. 350-351 [doi]
• Data preprocessing using a priori knowledgeJean Simon. 352-353 [doi]
• Discovering the Relationship between Student Effort and Ability for Predicting the Performance of Technology-Assisted Learning in a Mathematics After-School ProgramJun Xie, Xudong Huang, Henry Hua, Jin Wang, Quan Tang, Scotty D. Craig, Arthur C. Graesser, King-Ip (David) Lin, Xiangen Hu. 354-355 [doi]
• Using Item Response Theory to Refine Knowledge TracingYanbo Xu, Jack Mostow. 356-357 [doi]
• Estimating the benefits of student model improvements on a substantive scaleMichael Yudelson, Kenneth R. Koedinger. 358-359 [doi]
• A Dynamic Group Composition Method to Refine Collaborative Learning Group FormationZhilin Zheng. 360-361 [doi]
• Educational Data Mining: Illuminating Student Learning Pathways in an Online Fraction GameAni Aghababyan, Taylor Martin, Nicole Forsgren Velasquez, Philip Janisiewicz. 362-363 [doi]
• Automatic Gaze-Based Detection of Mind Wandering during ReadingSidney K. D'Mello, Jonathan Cobian, Matthew Hunter. 364-365 [doi]
• DARE: Deep Anaphora Resolution in Dialogue based Intelligent Tutoring SystemsNobal B. Niraula, Vasile Rus, Dan Stefanescu. 366-367 [doi]
• Are You Committed? Investigating Interactions among Reading Commitment, Natural Language Input, and Students' Learning OutcomesLaura K. Varner, G. Tanner Jackson, Erica L. Snow, Danielle S. McNamara. 368-369 [doi]
• Using Multi-level Models to Assess Data From an Intelligent Tutoring SystemJennifer L. Weston, Danielle S. McNamara. 370-371 [doi]
• Evaluation of Automatically Generated Hint FeedbackMichael Eagle, Tiffany Barnes. 372-374 [doi]
• Analysing Engineering Expertise of High School Students Using Eye Tracking and Multimodal Learning AnalyticsJuly Gomes, Mohamed Yassine, Marcelo Worsley, Paulo Blikstein. 375-377 [doi]
• Investigating the efficacy of algorithmic student modelling in predicting students at risk of failing in tertiary educationGeraldine Gray, Colm McGuinness, Philip Owende. 378-380 [doi]
• BOTS: Harnessing Player Data and Player Effort to Create and Evaluate Levels in a Serious GameAndrew Hicks. 381-383 [doi]
• Helping Students Manage Personalized Learning ScenariosPaul Salvador Inventado, Roberto S. Legaspi, Masayuki Numao. 384-386 [doi]
• Determining Problem Selection for a Logic Proof TutorBehrooz Mostafavi, Tiffany Barnes. 387-389 [doi]
• Demonstration of a Moodle student monitoring web applicationAngela Bovo, Stéphane Sanchez, Olivier Heguy, Yves Duthen. 390-391 [doi]
• Students Activity Visualization ToolMarius Stefan Chiritoiu, Marian Cristian Mihaescu, Dumitru Dan Burdescu. 392-393 [doi]
• FlexCCT: Software for Maximum Likelihood Cultural Consensus TheoryStephen L. France, Mahyar Vaghefi, William H. Batchelder. 394-395 [doi]
• Visual Exploration of Interactions and Performance with LeMoAgathe Merceron, Sebastian Schwarzrock, Margarita Elkina, Andreas Pursian, Liane Beuster, Albrecht Fortenbacher, Leonard Kappe, Boris Wenzlaff. 396-397 [doi]
• Project CASSI: A Social-Graph Based Tool for Classroom Behavior Analysis and OptimizationRobert Olson, Zachary Daily, John Malayny, Robert Szkutak. 398-399 [doi]
• A Moodle Block for Selecting, Visualizing and Mining Students' Usage DataCristóbal Romero, Cristobal Castro, Sebastián Ventura. 400-401 [doi]
• SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language InputsVasile Rus, Rajendra Banjade, Mihai C. Lintean, Nobal B. Niraula, Dan Stefanescu. 402-403 [doi]
• Gathering Emotional Data from Multiple SourcesSergio Salmeron-Majadas, Olga C. Santos, Jesus Boticario, Raúl Cabestrero, Pilar Quirós, Mar Saneiro. 404-405 [doi]
• A Tool for Speech Act Classification Using Interactive Machine LearningBorhan Samei, Fazel Keshtkar, Arthur C. Graesser. 406-407 [doi]