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Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)
Γλώσσα: English
Συγγραφείς: Barnes, Tiffany, Desmarais, Michel, Romero, Cristobal, Ventura, Sebastian, International Working Group on Educational Data Mining
Πηγή: International Working Group on Educational Data Mining. 2009.
Διαθεσιμότητα: International Working Group on Educational Data Mining. Available from: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Peer Reviewed: N
Page Count: 338
Ημερομηνία έκδοσης: 2009
Τύπος εγγράφου: Collected Works - Proceedings
Education Level: Adult Education
Elementary Education
Elementary Secondary Education
Grade 10
Grade 12
Grade 4
Grade 7
Grade 8
Grade 9
High Schools
Higher Education
Junior High Schools
Middle Schools
Postsecondary Education
Secondary Education
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries, Intelligent Tutoring Systems, Automation, Comparative Analysis, Models, Information Retrieval, College Instruction, Computer Science Education, Data, Electronic Learning, Mathematics, Multivariate Analysis, Students, Artificial Intelligence, Computation, Computer Software, Educational Strategies, Knowledge Level, Matrices, Prediction, Programming, Student Behavior, Web Based Instruction, Accuracy, Bayesian Statistics, Classification, Computer Assisted Testing, Computer Managed Instruction, Computer System Design, Correlation, Feedback (Response), Online Courses, Predictor Variables, Skills, Statistical Analysis, Assignments, Case Studies, Cooperative Learning, Data Collection, Educational Assessment, Engineering Education, Hypothesis Testing, Integrated Learning Systems, Learning, Logical Thinking, Natural Language Processing, Prior Learning, Questioning Techniques, Regression (Statistics), Scaffolding (Teaching Technique), Self Control, Simulation
Γεωγραφικοί όροι: Australia, Czech Republic, Israel, Massachusetts, Netherlands, North Carolina, Pennsylvania, Slovakia, Spain, Utah, Washington
Assessment and Survey Identifiers: Massachusetts Comprehensive Assessment System
ISBN: 978-84-613-2308-1
Περίληψη: The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. 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 using those 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) A Comparison of Student Skill Knowledge Estimates (Elizabeth Ayers, Rebecca Nugent, Nema Dean); (2) Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task (Ryan S.J.d. Baker); (3) A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials (Dror Ben-Naim, Michael Bain, and Nadine Marcus); (4) Detecting Symptoms of Low Performance Using Production Rules (Javier Bravo and Alvaro Ortigosa); (5) Predicting Students Drop Out: A Case Study (Gerben W. Dekker, Mykola Pechenizkiy and Jan M. Vleeshouwers); (6) Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (Mingyu Feng, Joseph E. Beck and Neil T. Heffernan); (7) Does Self-Discipline impact students' knowledge and learning? (Yue Gong, Dovan Rai, Joseph E. Beck, and Neil T. Heffernan); (8) Consistency of Students' Pace in Online Learning (Arnon Hershkovitz and Rafi Nachmias); (9) Student Consistency and Implications for Feedback in Online Assessment Systems (Tara M. Madhyastha and Steven Tanimoto); (10) Edu-mining for Book Recommendation for Pupils (Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa, and Koichiro Morihiro); (11) Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students (Rebecca Nugent, Elizabeth Ayers, and Nema Dean); (12) Determining the Significance of Item Order In Randomized Problem Sets (Zachary A. Pardos and Neil T. Heffernan); (13) Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger); (14) Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (David Nadler Prata, Ryan S.J.d. Baker, Evandro d.B. Costa, Carolyn P. Rose, Yue Cui, Adriana M.J.B. de Carvalho); (15) Using Dirichlet priors to improve model parameter plausibility (Dovan Rai, Yue Gong, Joseph E. Beck); (16) Reducing the Knowledge Tracing Space (Steven Ritter, Thomas K. Harris, Tristan Nixon, Daniel Dickison, R. Charles Murray, and Brendon Towle); (17) Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor (Vasile Rus, Mihai Lintean, and Roger Azevedo); (18) Automatic Concept Relationships Discovery for an Adaptive E-course (Marian Simko, Maria Bielikova); (19) Unsupervised MDP Value Selection for Automating ITS Capabilities (John Stamper and Tiffany Barnes); (20) Recommendation in Higher Education Using Data Mining Techniques (Cesar Vialardi, Javier Bravo Agapito, Leila Shafti, Alvaro and Ortigosa); (21) Developing an Argument Learning Environment Using Agent-Based ITS (ALES) (Safia Abbas and Hajime Sawamura); (22) A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (Antonio R. Anaya and Jesus G. Boticario); (23) Dimensions of Difficulty in Translating Natural Language into First-Order Logic (Dave Barker-Plummer, Richard Cox, and Robert Dale); (24) Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (Suleyman Cetintas, Luo Si, Yan Ping Xin, and Casey Hord); (25) Back to the future: a non-automated method of constructing transfer models (Ming Feng and Joseph Beck); (26) How do Students Organize Personal Information Spaces? (Sharon Hardof-Jaffe, Arnon Hershkovitz, Hama Abu-Kishk, Ofer Bergman, and Rafi Nachmias); (27) Improving Student Question Classification (Cecily Heiner and Joseph L. Zachary); (28) Why, What, and How to Log? Lessons from LISTEN (Jack Mostow and Joseph E. Beck); (29) Process Mining Online Assessment Data (Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil van der Aalst, and Paul De Bra); (30) Obtaining Rubric Weights For Assessments By More Than One Lecturer Using A Pairwise Learning Model (J. R. Quevedo and E. Montanes); (31) Collaborative Data Mining Tool for Education (Enrique Garcia, Cristobal Romero, Sebastian Ventura, Miguel Gea, and Carlos de Castro); (32) Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming (Amelia Zafra and Sebastian Ventura); and (33) Visualization of Differences in Data Measuring Mathematical Skills (Lukas Zoubek and Michal Burda). Individual papers contain tables, figures, footnotes, references and appendices.
Abstractor: ERIC
Entry Date: 2013
Αριθμός Καταχώρησης: ED539041
Βάση Δεδομένων: ERIC
FullText Text:
  Availability: 0
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AccessLevel: 3
PubType: Conference
PubTypeId: conference
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  Data: Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)
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  Data: The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. 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 using those 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) A Comparison of Student Skill Knowledge Estimates (Elizabeth Ayers, Rebecca Nugent, Nema Dean); (2) Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task (Ryan S.J.d. Baker); (3) A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials (Dror Ben-Naim, Michael Bain, and Nadine Marcus); (4) Detecting Symptoms of Low Performance Using Production Rules (Javier Bravo and Alvaro Ortigosa); (5) Predicting Students Drop Out: A Case Study (Gerben W. Dekker, Mykola Pechenizkiy and Jan M. Vleeshouwers); (6) Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (Mingyu Feng, Joseph E. Beck and Neil T. Heffernan); (7) Does Self-Discipline impact students' knowledge and learning? (Yue Gong, Dovan Rai, Joseph E. Beck, and Neil T. Heffernan); (8) Consistency of Students' Pace in Online Learning (Arnon Hershkovitz and Rafi Nachmias); (9) Student Consistency and Implications for Feedback in Online Assessment Systems (Tara M. Madhyastha and Steven Tanimoto); (10) Edu-mining for Book Recommendation for Pupils (Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa, and Koichiro Morihiro); (11) Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students (Rebecca Nugent, Elizabeth Ayers, and Nema Dean); (12) Determining the Significance of Item Order In Randomized Problem Sets (Zachary A. Pardos and Neil T. Heffernan); (13) Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger); (14) Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (David Nadler Prata, Ryan S.J.d. Baker, Evandro d.B. Costa, Carolyn P. Rose, Yue Cui, Adriana M.J.B. de Carvalho); (15) Using Dirichlet priors to improve model parameter plausibility (Dovan Rai, Yue Gong, Joseph E. Beck); (16) Reducing the Knowledge Tracing Space (Steven Ritter, Thomas K. Harris, Tristan Nixon, Daniel Dickison, R. Charles Murray, and Brendon Towle); (17) Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor (Vasile Rus, Mihai Lintean, and Roger Azevedo); (18) Automatic Concept Relationships Discovery for an Adaptive E-course (Marian Simko, Maria Bielikova); (19) Unsupervised MDP Value Selection for Automating ITS Capabilities (John Stamper and Tiffany Barnes); (20) Recommendation in Higher Education Using Data Mining Techniques (Cesar Vialardi, Javier Bravo Agapito, Leila Shafti, Alvaro and Ortigosa); (21) Developing an Argument Learning Environment Using Agent-Based ITS (ALES) (Safia Abbas and Hajime Sawamura); (22) A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (Antonio R. Anaya and Jesus G. Boticario); (23) Dimensions of Difficulty in Translating Natural Language into First-Order Logic (Dave Barker-Plummer, Richard Cox, and Robert Dale); (24) Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (Suleyman Cetintas, Luo Si, Yan Ping Xin, and Casey Hord); (25) Back to the future: a non-automated method of constructing transfer models (Ming Feng and Joseph Beck); (26) How do Students Organize Personal Information Spaces? (Sharon Hardof-Jaffe, Arnon Hershkovitz, Hama Abu-Kishk, Ofer Bergman, and Rafi Nachmias); (27) Improving Student Question Classification (Cecily Heiner and Joseph L. Zachary); (28) Why, What, and How to Log? Lessons from LISTEN (Jack Mostow and Joseph E. Beck); (29) Process Mining Online Assessment Data (Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil van der Aalst, and Paul De Bra); (30) Obtaining Rubric Weights For Assessments By More Than One Lecturer Using A Pairwise Learning Model (J. R. Quevedo and E. Montanes); (31) Collaborative Data Mining Tool for Education (Enrique Garcia, Cristobal Romero, Sebastian Ventura, Miguel Gea, and Carlos de Castro); (32) Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming (Amelia Zafra and Sebastian Ventura); and (33) Visualization of Differences in Data Measuring Mathematical Skills (Lukas Zoubek and Michal Burda). Individual papers contain tables, figures, footnotes, references and appendices.
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  Data: 2013
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  Data: ED539041
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED539041
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 338
    Subjects:
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Educational Research
        Type: general
      – SubjectFull: Conferences (Gatherings)
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Information Retrieval
        Type: general
      – SubjectFull: College Instruction
        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Data
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: Multivariate Analysis
        Type: general
      – SubjectFull: Students
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Computation
        Type: general
      – SubjectFull: Computer Software
        Type: general
      – SubjectFull: Educational Strategies
        Type: general
      – SubjectFull: Knowledge Level
        Type: general
      – SubjectFull: Matrices
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Web Based Instruction
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Computer Assisted Testing
        Type: general
      – SubjectFull: Computer Managed Instruction
        Type: general
      – SubjectFull: Computer System Design
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Skills
        Type: general
      – SubjectFull: Statistical Analysis
        Type: general
      – SubjectFull: Assignments
        Type: general
      – SubjectFull: Case Studies
        Type: general
      – SubjectFull: Cooperative Learning
        Type: general
      – SubjectFull: Data Collection
        Type: general
      – SubjectFull: Educational Assessment
        Type: general
      – SubjectFull: Engineering Education
        Type: general
      – SubjectFull: Hypothesis Testing
        Type: general
      – SubjectFull: Integrated Learning Systems
        Type: general
      – SubjectFull: Learning
        Type: general
      – SubjectFull: Logical Thinking
        Type: general
      – SubjectFull: Natural Language Processing
        Type: general
      – SubjectFull: Prior Learning
        Type: general
      – SubjectFull: Questioning Techniques
        Type: general
      – SubjectFull: Regression (Statistics)
        Type: general
      – SubjectFull: Scaffolding (Teaching Technique)
        Type: general
      – SubjectFull: Self Control
        Type: general
      – SubjectFull: Simulation
        Type: general
      – SubjectFull: Australia
        Type: general
      – SubjectFull: Czech Republic
        Type: general
      – SubjectFull: Israel
        Type: general
      – SubjectFull: Massachusetts
        Type: general
      – SubjectFull: Netherlands
        Type: general
      – SubjectFull: North Carolina
        Type: general
      – SubjectFull: Pennsylvania
        Type: general
      – SubjectFull: Slovakia
        Type: general
      – SubjectFull: Spain
        Type: general
      – SubjectFull: Utah
        Type: general
      – SubjectFull: Washington
        Type: general
      – SubjectFull: Massachusetts Comprehensive Assessment System
        Type: general
    Titles:
      – TitleFull: Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: International Working Group on Educational Data Mining
      – PersonEntity:
          Name:
            NameFull: Barnes, Tiffany
      – PersonEntity:
          Name:
            NameFull: Desmarais, Michel
      – PersonEntity:
          Name:
            NameFull: Romero, Cristobal
      – PersonEntity:
          Name:
            NameFull: Ventura, Sebastian
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      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Type: published
              Y: 2009
          Identifiers:
            – Type: isbn-print
              Value: 978-84-613-2308-1
          Titles:
            – TitleFull: International Working Group on Educational Data Mining
              Type: main
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