How Long to Read Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010).

How Long Does it Take to Read Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010).?

It takes the average reader 5 hours and 59 minutes to read Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010). by International Working Group on Educational Data Mining

Assuming a reading speed of 250 words per minute. Learn more

Description

The Third International Conference on Data Mining (EDM 2010) was held in Pittsburgh, PA, USA. It follows the second conference at the University of Cordoba, Spain, on July 1-3, 2009 and the first edition of the conference held in Montreal in 2008, and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. EDM 2011 will be held in Eindhoven, Netherlands. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for analyzing the data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) Effort-based Tutoring: An Empirical Approach to Intelligent Tutoring (Ivon Arroyo, Hasmik Mehranian and Beverly P. Woolf); (2) An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools (Ryan S.J.d. Baker and Sujith M. Gowda); (3) On the Faithfulness of Simulated Student Performance Data (Michel C. Desmarais and Ildiko Pelczer); (4) Mining Bodily Patterns of Affective Experience during Learning (Sidney D'Mello and Art Graesser); (5) Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? (Mingyu Feng and Neil Heffernan); (6) Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System (Jon M. Fincham, John R. Anderson, Shawn Betts and Jennifer Ferris); (7) Using multiple Dirichlet distributions to improve parameter plausibility (Yue Gong, Joseph E. Beck and Neil T. Heffernan); (8) Examining Learner Control in a Structured Inquiry Cycle Using Process Mining (Larry Howard, Julie Johnson and Carin Neitzel); (9) Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models (Hogyeong Jeong, Gautam Biswas, Julie Johnson and Larry Howard); (10) Data Mining for Generating Hints in a Python Tutor (Anna Katrina Dominguez, Kalina Yacef and James R. Curran); (11) Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity (Blair Lehman, Whitney Cade and Andrew Olney); (12) Sentiment Analysis in Student Experiences of Learning (Sunghwan Mac Kim and Rafael A. Calvo); (13) Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (14) A Data Model to Ease Analysis and Mining of Educational Data (Andre Kruger, Agathe Merceron and Benjamin Wolf); (15) Identifying Students' Inquiry Planning Using Machine Learning (Orlando Montalvo, Ryan S.J.d. Baker, Michael A. Sao Pedro, Adam Nakama and Janice D. Gobert); (16) Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers (Rebecca Nugent, Nema Dean and Elizabeth Ayers); (17) Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm (Zachary Pardos and Neil Heffernan); (18) Mining Rare Association Rules from e-Learning Data (Cristobal Romero, Jose Raul Romero, Jose Maria Luna and Sebastian Ventura); (19) Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns (Michael Sao Pedro, Ryan S.J.d. Baker, Orlando Montalvo, Adam Nakama and Janice D. Gobert); (20) Identifying High-Level Student Behavior Using Sequence-based Motif Discovery (David H. Shanabrook, David G. Cooper, Beverly Park Woolf and Ivon Arroyo); (21) Unsupervised Discovery of Student Strategies (Benjamin Shih, Kenneth R. Koedinger and Richard Scheines); (22) Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data (Wenting Xiong, Diane Litman and Christian Schunn); (23) Using Numeric Optimization To Refine Semantic User Model Integration Of Adaptive Educational Systems (Michael Yudelson, Peter Brusilovsky, Antonija Mitrovic and Moffat Mathews); (24) An Annotations Approach to Peer Tutoring (John Champaign and Robin Cohen); (25) Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning (Mohammad Hassan Falakmasir and Jafar Habibi); (26) Mining Students' Interaction Data from a System that Support Learning by Reflection (Rajibussalim); (27) Process Mining to Support Students' Collaborative Writing (Vilaythong Southavilay, Kalina Yacef and Rafael A. Callvo); (28) Automatic Rating of User-Generated Math Solutions (Turadg Aleahmad, Vincent Aleven and Robert Kraut); (29) Tracking Students' Inquiry Paths through Student Transition Analysis (Matt Bachmann, Janice Gobert and Joseph Beck); (30) DISCUSS: Enabling Detailed Characterization of Tutorial Interactions Through Dialogue Annotation (Lee Becker, Wayne H. Ward and Sarel vanVuuren); (31) Data Mining of both Right and Wrong Answers from a Mathematics and a Science M/C Test given Collectively to 11,228 Students from India [1] in years 4, 6 and 8 (James Bernauer and Jay Powell); (32) Mining information from tutor data to improve pedagogical content knowledge (Suchismita Srinivas, Muntaquim Bagadia and Anupriya Gupta); (33) Clustering Student Learning Activity Data (Haiyun Bian); (34) Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments (Nabila Bousbia, Jean-Marc Labat, Amar Balla and Issam Rebai); (35) Using Topic Models to Bridge Coding Schemes of Differing Granularity (Whitney L. Cade and Andrew Olney); (36) A Distillation Approach to Refining Learning Objects (John Champaign and Robin Cohen); (37) A Preliminary Investigation of Hierarchical Hidden Markov Models for Tutorial Planning (Kristy Elizabeth Boyer, Robert Phillips, Eun Young Ha, Michael D. Wallis, Mladen A. Vouk, and James C. Lester); (38) Higher Contributions Correlate with Higher Learning Gains (Carol Forsyth, Heather Butler, Arthur C. Graesser, Diane Halpern); (39) Pinpointing Learning Moments; A finer grain P(J) model (Adam Goldstein, Ryan S.J.d. Baker and Neil T. Heffernan); (40) Predicting Task Completion from Rich but Scarce Data (Jose P. Gonzalez-Brenes and Jack Mostow); (41) Hierarchical Structures of Content Items in LMS (Sharon Hardof-Jaffe, Arnon Hershkovitz, Ronit Azran and Rafi Nachmias); (42) Is Students' Activity in LMS Persistent? (Arnon Hershkovitz and Rafi Nachmias); (43) EDM Visualization Tool: Watching Students Learn (Matthew M. Johnson and Tiffany Barnes); (44) Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks (Nabila Khodeir, Nayer Wanas, Nevin Darwish and Nadia Hegazy); (45) Using LiMS (the Learner Interaction Monitoring System) to Track Online Learner Engagement and Evaluate Course Design (Leah P. Macfadyen and Peter Sorenson); (46) Observing Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (47) When Data Exploration and Data Mining meet while Analysing Usage Data of a Course (Andre Kruger, Agathe Merceron and Benjamin Wolf); (48) AutoJoin: Generalizing an Example into an EDM query (Jack Mostow and Bao Hong (Lucas) Tan); (49) Conceptualizing Procedural Knowledge Targeted at Students with Different Skill Levels (Martin Mozina, Matej Guid, Aleksander Sadikov, Vida Groznik, Jana Krivec, and Ivan Bratko); (50) Data Reduction Methods Applied to Understanding Complex Learning Hypotheses (Philip I. Pavlik Jr.); (51) Analysis of a causal modeling approach: a case study with an educational intervention (Dovan Rai and Joseph E. Beck); (52) Peer Production of Online Learning Resources: A Social Network Analysis (Beijie Xu and Mimi M. Recker); (53) Class Association Rules Mining from Students' Test Data (Cristobal Romero, Sebastian Ventura, Ekaterina Vasilyeva and Mykola Pechenizkiy); (54) Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for Epistemic Games (Andre A. Rupp, Shauna J. Sweet and Younyoung Choi); (55) Multiple Test Forms Construction based on Bees Algorithm (Pokpong Songmuang and Maomi Ueno); (56) Can Order of Access to Learning Resources Predict Success? (Hema Soundranayagam and Kalina Yacef); (57) A Data Driven Approach to the Discovery of Better Cognitive Models (Kenneth R. Koedinger and John C. Stamper); (58) Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems (John C. Stamper, Tiffany Barnes and Marvin Croy); (59) A Review of Student Churn in the Light of Theories on Business Relationships (Jaan Ubi and Innar Liiv); (60) Towards EDM Framework for Personalization of Information Services in RPM Systems (Ekaterina Vasilyeva, Mykola Pechenizkiy, Aleksandra Tesanovic, Evgeny Knutov, Sicco Verwer and Paul De Bra); (61) A Case Study: Data Mining Applied to Student Enrollment (Cesar Vialardi, Jorge Chue, Alfredo Barrientos, Daniel Victoria, Jhonny Estrella, Juan Pablo Peche and Alvaro Ortigosa); (62) Representing Student Performance with Partial Credit (Yutao Wang, Neil T. Heffernan and Joseph E. Beck); (63) Where in the World? Demographic Patterns in Access Data (Mimi M. Recker, Beijie Xu, Sherry Hsi, and Christine Garrard); and (64) Pundit: Intelligent Recommender of Courses (Ankit Ranka, Faisal Anwar, Hui Soo Chae). Individual papers contain tables, figures, footnotes and references.

How long is Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010).?

Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010). by International Working Group on Educational Data Mining is 354 pages long, and a total of 89,916 words.

This makes it 119% the length of the average book. It also has 110% more words than the average book.

How Long Does it Take to Read Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010). Aloud?

The average oral reading speed is 183 words per minute. This means it takes 8 hours and 11 minutes to read Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010). aloud.

What Reading Level is Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010).?

Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010). is suitable for students ages 12 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010).?

Proceedings of the International Conference on Educational Data Mining(Edm) (3Rd, Pittsburgh, Pa, July 11-13, 2010). by International Working Group on Educational Data Mining is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

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