Educational Data Mining and Learning Analytics
暂无分享,去创建一个
[1] G. Tutz,et al. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.
[2] Linda Corrin,et al. Visualizing patterns of student engagement and performance in MOOCs , 2014, LAK.
[3] Matthew D. Pistilli,et al. Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.
[4] Mark Wilson,et al. A framework for item response models , 2004 .
[5] Kenneth R. Koedinger,et al. Is Over Practice Necessary? - Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining , 2007, AIED.
[6] W. F. Punch,et al. Predicting student performance: an application of data mining methods with an educational Web-based system , 2003, 33rd Annual Frontiers in Education, 2003. FIE 2003..
[7] Mykola Pechenizkiy,et al. Handbook of Educational Data Mining , 2010 .
[8] Taylor Martin,et al. Learning Programming with IPRO: The Effects of a Mobile, Social Programming Environment. , 2013 .
[9] Neil T. Heffernan,et al. Addressing the assessment challenge with an online system that tutors as it assesses , 2009, User Modeling and User-Adapted Interaction.
[10] R. Nisbett,et al. Immediate and delayed transfer of training effects in statistical reasoning. , 1991, Journal of experimental psychology. General.
[11] Ryan Shaun Joazeiro de Baker,et al. Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns , 2010, EDM.
[12] Ryan Shaun Joazeiro de Baker,et al. Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill , 2011, User Modeling and User-Adapted Interaction.
[13] Ryan Shaun Joazeiro de Baker,et al. Modeling and understanding students' off-task behavior in intelligent tutoring systems , 2007, CHI.
[14] Ryan Shaun Joazeiro de Baker,et al. Reengineering the Feature Distillation Process: A case study in detection of Gaming the System , 2014, EDM.
[15] Zlatko J. Kovacic,et al. Early Prediction of Student Success: Mining Students Enrolment Data , 2010 .
[16] Doug Clow. Data wranglers: human interpreters to help close the feedback loop , 2014, LAK '14.
[17] Charles Anderson,et al. The end of theory: The data deluge makes the scientific method obsolete , 2008 .
[18] Vanessa P. Dennen,et al. From Message Posting to Learning Dialogues: Factors affecting learner participation in asynchronous discussion , 2005 .
[19] B. Junker,et al. Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory , 2001 .
[20] Vincent Aleven,et al. Sensor-free automated detection of affect in a Cognitive Tutor for Algebra , 2012, EDM.
[21] C. Spearman. The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.
[22] John P. Campbell,et al. Academic Analytics: A New Tool for a New Era. , 2007 .
[23] Kenneth R. Koedinger,et al. Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement , 2006, Intelligent Tutoring Systems.
[24] Jessica Lin,et al. Finding Motifs in Time Series , 2002, KDD 2002.
[25] S. Chipman,et al. Cognitively diagnostic assessment , 1995 .
[26] David Nadler Prata,et al. Dialogue Analysis in Collaborative Learning , 2012 .
[27] Vania Dimitrova,et al. Visualising student tracking data to support instructors in web-based distance education , 2004, WWW Alt. '04.
[28] Stephen Fancsali. Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results , 2012, EDM.
[29] Judy Kay,et al. Analysing Frequent Sequential Patterns of Collaborative Learning Activity Around an Interactive Tabletop. Nominee for Best Paper Award , 2010, EDM.
[30] Robert A. Muenchen,et al. The Popularity of Data Analysis Software , 2013 .
[31] Albert T. Corbett,et al. A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.
[32] R. Almond,et al. A BRIEF INTRODUCTION TO EVIDENCE-CENTERED DESIGN , 2003 .
[33] Vivienne L. Ming,et al. Predicting student outcomes from unstructured data , 2012, UMAP Workshops.
[34] D. J. Summers,et al. Charm physics at Fermilab E791 , 1992 .
[35] Eitel J. M. Lauría,et al. Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative , 2014, J. Learn. Anal..
[36] Kalina Yacef,et al. Educational Data Mining: a Case Study , 2005, AIED.
[37] Gautam Biswas,et al. Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution , 2012, EDM.
[38] Ryan Shaun Joazeiro de Baker,et al. WTF? detecting students who are conducting inquiry without thinking fastidiously , 2012, UMAP.
[39] Judy Kay,et al. Clustering and Sequential Pattern Mining of Online Collaborative Learning Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[40] Arnon Hershkovitz,et al. The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate? , 2009, AIED.
[41] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[42] Mykola Pechenizkiy,et al. Predicting Students Drop Out: A Case Study , 2009, EDM.
[43] David J. Hand,et al. Data Mining: Statistics and More? , 1998 .
[44] Osmar R. Zaïane,et al. Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques , 2011, EDM.
[45] Gregory K. W. K. Chung,et al. Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis , 2012, EDM 2012.
[46] S. Messick. The Interplay of Evidence and Consequences in the Validation of Performance Assessments , 1994 .
[47] H. P. Bahrick,et al. Maintenance of Foreign Language Vocabulary and the Spacing Effect , 1993 .
[48] Marta E. Zorrilla,et al. Social Network Analysis and Data Mining: An Application to the E-Learning Context , 2013, ICCCI.
[49] Ryan Shaun Joazeiro de Baker,et al. Adapting to When Students Game an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.
[50] Andrew Olney,et al. Mining Collaborative Patterns in Tutorial Dialogues , 2010, EDM 2010.
[51] Gautam Biswas,et al. Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning , 2012, EDM.
[52] Ryan Shaun Joazeiro de Baker,et al. Detecting Student Misuse of Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.
[53] J. Greeno. THE SITUATIVITY OF KNOWING, LEARNING, AND RESEARCH , 1998 .
[54] Taylor Martin,et al. Learning Fractions by Splitting: Using Learning Analytics to Illuminate the Development of Mathematical Understanding , 2015 .
[55] Chirag Patel,et al. Exploring a Joint Model of Conventional and Online Learning Systems , 2006 .
[56] J. Manyika. Big data: The next frontier for innovation, competition, and productivity , 2011 .
[57] Vincent Aleven,et al. Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. , 2012, EDM 2012.
[58] Dror Ben-Naim,et al. A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials , 2009, EDM.
[59] R. Schmidt,et al. New Conceptualizations of Practice: Common Principles in Three Paradigms Suggest New Concepts for Training , 1992 .
[60] Sébastien George,et al. TrAVis to enhance online tutoring and learning activities: Real-time visualization of students tracking data , 2011, Interact. Technol. Smart Educ..
[61] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .
[62] C. Lebiere,et al. The Atomic Components of Thought , 1998 .
[63] Kasia Muldner,et al. An analysis of students’ gaming behaviors in an intelligent tutoring system: predictors and impacts , 2011, User Modeling and User-Adapted Interaction.
[64] Sebastián Ventura,et al. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering , 2009, User Modeling and User-Adapted Interaction.
[65] Bruce M. McLaren,et al. Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques , 2010, Int. J. Artif. Intell. Educ..
[66] Tristan Nixon,et al. A Method for Finding Prerequisites Within a Curriculum , 2011, EDM.
[67] George Siemens,et al. Learning analytics and educational data mining: towards communication and collaboration , 2012, LAK.
[68] Alex Paramythis,et al. Activity sequence modelling and dynamic clustering for personalized e-learning , 2011, User Modeling and User-Adapted Interaction.
[69] Neil T. Heffernan,et al. Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School , 2013, EDM.
[70] E. Mandinach,et al. The role of cognitive engagement in classroom learning and motivation , 1983 .
[71] Tiffany Barnes,et al. The Q-matrix Method: Mining Student Response Data for Knowledge , 2005 .
[72] Michel C. Desmarais,et al. Improving matrix factorization techniques of student test data with partial order constraints , 2012, UMAP.
[73] John R. Anderson,et al. ACT-R: A Theory of Higher Level Cognition and Its Relation to Visual Attention , 1997, Hum. Comput. Interact..
[74] Joel D. Martin,et al. Student assessment using Bayesian nets , 1995, Int. J. Hum. Comput. Stud..
[75] Marcelo Worsley,et al. Towards the development of multimodal action based assessment , 2013, LAK '13.
[76] Gurmak Singh,et al. Implementing eLearning Programmes for Higher Education: A Review of the Literature , 2004, J. Inf. Technol. Educ..
[77] Jonathan P. Rowe,et al. When Off-Task is On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments , 2011, AIED.
[78] Joseph E. Beck,et al. Exploring User Data From a Game-like Math Tutor: A Case Study in Causal Modeling , 2011, EDM.
[79] Valerie J. Shute,et al. SMART: Student modeling approach for responsive tutoring , 1995, User Modeling and User-Adapted Interaction.
[80] Cristina Conati,et al. Combining Unsupervised and Supervised Classification to Build User Models for Exploratory , 2009, EDM 2009.
[81] Gautam Biswas,et al. Using Hidden Markov Models to Characterize Student Behaviors in Learning-by-Teaching Environments , 2008, Intelligent Tutoring Systems.
[82] Lei Qu,et al. Classifying Learner Engagement through Integration of Multiple Data Sources , 2006, AAAI.
[83] Ryan Shaun Joazeiro de Baker,et al. An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools , 2010, EDM.
[84] Chris Piech,et al. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.
[85] Ryan Shaun Joazeiro de Baker,et al. Developing a generalizable detector of when students game the system , 2008, User Modeling and User-Adapted Interaction.
[86] Cristina Conati,et al. Incorporating an Affective Behavior Model into an Educational Game , 2009, FLAIRS Conference.
[87] Mitsuru Ikeda,et al. Proceedings of the 8th international conference on Intelligent Tutoring Systems , 2006 .
[88] Sebastián Ventura,et al. Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[89] Arthur C. Graesser,et al. Automatic detection of learner’s affect from conversational cues , 2008, User Modeling and User-Adapted Interaction.
[90] P. Spirtes,et al. Causation, prediction, and search , 1993 .
[91] Neil T. Heffernan,et al. Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.
[92] Beverly Park Woolf,et al. Identifying High-Level Student Behavior Using Sequence-based Motif Discovery , 2010, EDM.
[93] Mathieu d'Aquin,et al. Interpreting data mining results with linked data for learning analytics: motivation, case study and directions , 2013, LAK '13.
[94] Kikumi K. Tatsuoka,et al. Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. , 1995 .
[95] Antonija Mitrovic,et al. Evaluation of a Constraint-Based Tutor for a Database Language , 1999 .
[96] R Scheines,et al. The TETRAD Project: Constraint Based Aids to Causal Model Specification. , 1998, Multivariate behavioral research.
[97] Russell G. Almond,et al. Bayesian Networks in Educational Assessment , 2015 .
[98] Vincent Aleven,et al. Intelligent Tutoring Goes To School in the Big City , 1997 .
[99] Rebecca Ferguson,et al. Learning analytics: drivers, developments and challenges , 2012 .
[100] John R Anderson,et al. Using a model to compute the optimal schedule of practice. , 2008, Journal of experimental psychology. Applied.
[101] Caroline Haythornthwaite,et al. Exploring Multiplexity: Social Network Structures in a Computer-Supported Distance Learning Class , 2001, Inf. Soc..
[102] Albert T. Corbett,et al. A Cognitive Tutor for Genetics Problem Solving: Learning Gains and Student Modeling , 2010 .
[103] Taylor Martin,et al. Using Learning Analytics to Understand the Learning Pathways of Novice Programmers , 2013 .
[104] Ryan Shaun Joazeiro de Baker,et al. Automatically Detecting a Student's Preparation for Future Learning: Help Use is Key , 2011, EDM.
[105] Ryan Shaun Joazeiro de Baker,et al. The Relationship between Carelessness and Affect in a Cognitive Tutor , 2011, ACII.
[106] Dieter Fensel,et al. Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..
[107] Daniel L. Schwartz,et al. Inventing to Prepare for Future Learning: The Hidden Efficiency of Encouraging Original Student Production in Statistics Instruction , 2004 .
[108] Taylor Martin,et al. Nanogenetic learning analytics: illuminating student learning pathways in an online fraction game , 2013, LAK '13.
[109] Kenneth R. Koedinger,et al. Automated Student Model Improvement , 2012, EDM.
[110] Edward A. Feigenbaum,et al. The fifth generation - artificial intelligence and Japan's computer challenge to the world , 1991 .
[111] Dragan Gamberger,et al. Combining Unsupervised and Supervised Machine Learning , 2001, AIME.
[112] Rebecca Ferguson,et al. Visualizing social learning ties by type and topic: rationale and concept demonstrator , 2013, LAK '13.
[113] Valerie J. Shute,et al. A Large-Scale Evaluation of an Intelligent Discovery World: Smithtown , 1990, Interact. Learn. Environ..
[114] Judy Kay,et al. The Big Five and Visualisations of Team Work Activity , 2006, Intelligent Tutoring Systems.
[115] Kenneth R. Koedinger,et al. A Response Time Model For Bottom-Out Hints as Worked Examples , 2008, EDM.
[116] Jeff Grann,et al. Competency map: visualizing student learning to promote student success , 2014, LAK.
[117] I. E. Allen,et al. Staying the course: online education in the United States, 2008 , 2008 .
[118] John O'Malley,et al. Students Perceptions of Distance Learning, Online Learning and the Traditional Classroom , 1999 .
[119] Mathieu d'Aquin. Putting Linked Data to Use in a Large Higher-Education Organisation , 2012, ILD@ESWC.
[120] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[121] Daniel D. Suthers,et al. A unified framework for multi-level analysis of distributed learning , 2011, LAK.
[122] Johan Jeuring,et al. University students' achievement goals and help-seeking strategies in an intelligent tutoring system , 2014, Comput. Educ..
[123] Vincent Aleven,et al. Learner Differences in Hint Processing , 2012, EDM.
[124] Lei Qu,et al. Mathematics motivation and achievement as predictors of high school students' guessing and help-seeking with instructional software , 2008, J. Comput. Assist. Learn..
[125] Jody Clarke,et al. Predicting Successful Inquiry Learning in a Virtual Performance Assessment for Science , 2013, UMAP.
[126] K. Tatsuoka. RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY , 1983 .
[127] Zachary A. Pardos,et al. The sum is greater than the parts: ensembling models of student knowledge in educational software , 2012, SKDD.
[128] Mladen A. Vouk,et al. Experimental Analysis of the Q-Matrix Method in Knowledge Discovery , 2005, ISMIS.
[129] Robert J. Mislevy,et al. An Application of Exploratory Data Analysis in the Development of Game-Based Assessments , 2015 .
[130] Judy Kay,et al. Exploiting Readily Available Web Data for Scrutable Student Models , 2005, AIED.
[131] Beverly Park Woolf,et al. Inferring learning and attitudes from a Bayesian Network of log file data , 2005, AIED.
[132] Mimi Recker,et al. Understanding Teacher Users of a Digital Library Service: A Clustering Approach , 2011, EDM 2011.
[133] Ryan Shaun Joazeiro de Baker,et al. Off-task behavior in the cognitive tutor classroom: when students "game the system" , 2004, CHI.
[134] Neil T. Heffernan,et al. How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis , 2011, Int. J. Artif. Intell. Educ..
[135] Ryan Shaun Joazeiro de Baker,et al. Cross-System Transfer of Machine Learned and Knowledge Engineered Models of Gaming the System , 2015, UMAP.
[136] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.
[137] Jing Zhang,et al. EDUCATIONAL DATA MINING , 2016 .
[138] Michel C. Desmarais,et al. Item to Skills Mapping: Deriving a Conjunctive Q-matrix from Data , 2012, ITS.
[139] Peter Maurer,et al. The Cambridge Handbook of the Learning Sciences , 2022 .
[140] Peter Norvig,et al. The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.
[141] Vincent Aleven,et al. Argument graph classification with Genetic Programming and C4.5 , 2008, EDM.
[142] D. Klahr,et al. All other things being equal: acquisition and transfer of the control of variables strategy. , 1999, Child development.
[143] Ryan Shaun Joazeiro de Baker,et al. Towards Predicting Future Transfer of Learning , 2011, AIED.
[144] Shane Dawson,et al. SNAPP: a bird's-eye view of temporal participant interaction , 2011, LAK.
[145] F. Collins,et al. The Human Genome Project: Lessons from Large-Scale Biology , 2003, Science.
[146] Rebecca Ferguson,et al. Social Learning Analytics , 2012, J. Educ. Technol. Soc..
[147] Neil T. Heffernan,et al. Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.
[148] Sebastián Ventura,et al. Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..
[149] Diane Jass Ketelhut,et al. Automatic Grading of Scientific Inquiry , 2012, BEA@NAACL-HLT.
[150] Carolyn Penstein Rosé,et al. Challenging Assumptions: using sliding window visualizations to reveal time-based irregularities in CSCL processes , 2012, ICLS.
[151] Rajeev Motwani,et al. Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.
[152] Alex Pentland,et al. Sensing and modeling human networks using the sociometer , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..
[153] S. Natasha Beretvas,et al. Comparing Multidimensional and Unidimensional Proficiency Classifications: Multidimensional IRT as a Diagnostic Aid , 2003 .
[154] Albert T. Corbett,et al. The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning , 2012, Cogn. Sci..
[155] Krzysztof Z. Gajos,et al. Understanding in-video dropouts and interaction peaks inonline lecture videos , 2014, L@S.
[156] John R. Anderson,et al. Knowledge tracing: Modeling the acquisition of procedural knowledge , 2005, User Modeling and User-Adapted Interaction.
[157] V. Aleven,et al. Help Seeking and Help Design in Interactive Learning Environments , 2003 .
[158] Judy Kay,et al. An Interactive Teacher's Dashboard for Monitoring Groups in a Multi-tabletop Learning Environment , 2012, ITS.
[159] Alex J. Bowers. Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis. , 2010 .
[160] Gautam Biswas,et al. Developing Learning by Teaching Environments That Support Self-Regulated Learning , 2004, Intelligent Tutoring Systems.
[161] Vincent Aleven,et al. Can Help Seeking Be Tutored? Searching for the Secret Sauce of Metacognitive Tutoring , 2007, AIED.
[162] Albert T. Corbett,et al. Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology , 2008, Intelligent Tutoring Systems.
[163] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[164] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[165] Ramakrishnan Srikant,et al. Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.
[166] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[167] Kalina Yacef,et al. Interestingness Measures for Associations Rules in Educational Data , 2008, EDM.
[168] Bertrand Meyer,et al. EMPIRICAL STUDY OF NOVICE ERRORS AND ERROR PATHS IN OBJECT-ORIENTED PROGRAMMING , 2006 .
[169] Neil T. Heffernan,et al. Towards Live Informing and Automatic Analyzing of Student Learning: Reporting in ASSISTment System , 2007 .
[170] Paul Kline,et al. An easy guide to factor analysis , 1993 .
[171] Jody Clarke,et al. Towards Identifying Students' Causal Reasoning Using Machine Learning , 2013, AIED.
[172] Neil T. Heffernan,et al. Population validity for educational data mining models: A case study in affect detection , 2014, Br. J. Educ. Technol..
[173] Carolyn Penstein Rosé,et al. Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions , 2007, AIED.
[174] Kenneth R. Koedinger,et al. A Data Repository for the EDM Community: The PSLC DataShop , 2010 .
[175] Richard Scheines,et al. Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations , 2012, EDM.
[176] John D. Storey. The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .
[177] Ryan S. Baker,et al. The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.
[178] Arthur C. Graesser,et al. Automatic Discovery of Speech Act Categories in Educational Games , 2012, EDM.
[179] Judy Kay,et al. Analyzing Collaborative Interactions with Data Mining Methods for the Benefit of Learning , 2011 .
[180] Beverly Park Woolf,et al. Repairing Disengagement With Non-Invasive Interventions , 2007, AIED.
[181] Shane Dawson,et al. Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..
[182] Kimberly E. Arnold. Signals: Applying Academic Analytics. , 2010 .
[183] Michel C. Desmarais. Conditions for Effectively Deriving a Q-Matrix from Data with Non-negative Matrix Factorization. Best Paper Award , 2011, EDM.
[184] Kenneth R. Koedinger,et al. Performance Factors Analysis - A New Alternative to Knowledge Tracing , 2009, AIED.
[185] Ryan Shaun Joazeiro de Baker,et al. Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor , 2010, UMAP.
[186] Ryan Shaun Joazeiro de Baker,et al. Development of a Workbench to Address the Educational Data Mining Bottleneck , 2012, EDM.
[187] Zachary A. Pardos,et al. Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes , 2013, LAK '13.
[188] David E. Pritchard,et al. Correlating skill and improvement in 2 MOOCs with a student's time on tasks , 2014, L@S.
[189] Shane Dawson,et al. A Study of the Relationship between Student Social Networks and Sense of Community , 2008, J. Educ. Technol. Soc..
[190] Brian C. Nelson,et al. Presence and Middle School Students' Participation in a Virtual Game Environment to Assess Science Inquiry , 2012, J. Educ. Technol. Soc..
[191] Albert T. Corbett,et al. Why Students Engage in “Gaming the System” Behavior in Interactive Learning Environments , 2008 .
[192] Steven F. Quigley,et al. Computer-based formative assessment to promote reflection and learner autonomy , 2006 .
[193] Zachary A. Pardos,et al. MOOCdb: Developing Data Standards for MOOC Data Science , 2013 .
[194] R. Sawyer. The Cambridge Handbook of the Learning Sciences: Introduction , 2014 .
[195] Janice D. Gobert,et al. From Log Files to Assessment Metrics: Measuring Students' Science Inquiry Skills Using Educational Data Mining , 2013, Journal of the Learning Sciences.
[196] I. E. Allen,et al. Changing Course: Ten Years of Tracking Online Education in the United States. , 2013 .
[197] Vincent Aleven,et al. Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor , 2006, Int. J. Artif. Intell. Educ..
[198] Arnon Hershkovitz,et al. Developing a Log-based Motivation Measuring Tool , 2008, EDM.
[199] Luo Si,et al. Automatic Detection of Off-Task Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques , 2010, IEEE Transactions on Learning Technologies.
[200] Sebastián Ventura,et al. A Survey on Pre-Processing Educational Data , 2014 .
[201] Lara M. Triona,et al. Point and Click or Grab and Heft: Comparing the Influence of Physical and Virtual Instructional Materials on Elementary School Students' Ability to Design Experiments , 2003 .
[202] Mike Sharkey,et al. Academic analytics landscape at the University of Phoenix , 2011, LAK.
[203] Anthony Chow,et al. College students' perceived threat and preference for seeking help in traditional, distributed, and distance learning environments , 2007, Comput. Educ..
[204] Mark Warschauer,et al. Predicting MOOC performance with Week 1 Behavior , 2014, EDM.
[205] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[206] Ryan Shaun Joazeiro de Baker,et al. Improving construct validity yields better models of systematic inquiry, even with less information , 2012, UMAP.
[207] Ryan Shaun Joazeiro de Baker,et al. Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics , 2011, AIED.
[208] Carolyn Penstein Rosé,et al. Towards Academically Productive Talk Supported by Conversational Agents , 2012, ITS.
[209] Vincent Aleven,et al. Educational Software Features that Encourage and Discourage "Gaming the System" , 2009, AIED.