Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019)
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| Title: | Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019) |
|---|---|
| Language: | English |
| Authors: | Lynch, Collin F., Merceron, Agathe, Desmarais, Michel, Nkambou, Roger, International Educational Data Mining Society |
| Source: | International Educational Data Mining Society. 2019. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 823 |
| Publication Date: | 2019 |
| Document Type: | Collected Works - Proceedings |
| Education Level: | Higher Education Postsecondary Education Elementary Education Grade 8 Junior High Schools Middle Schools Secondary Education High Schools |
| Descriptors: | Data Collection, Data Analysis, Information Retrieval, Content Analysis, Educational Research, Ethics, Evidence Based Practice, Grade Point Average, Inquiry, Game Based Learning, Metacognition, Student Records, Undergraduate Students, Predictor Variables, Student Satisfaction, Skill Development, Prediction, Programming, Large Group Instruction, Online Courses, Dropouts, Academic Achievement, Computation, Models, Time to Degree, Measurement Techniques, Institutional Characteristics, Help Seeking, Student Motivation, Artificial Intelligence, Interaction, Teaching Methods, Knowledge Level, Video Games, Cooperative Learning, Problem Solving, Grades (Scholastic), Reinforcement, Intelligent Tutoring Systems, Learning Activities, Graphs, Semantics, Difficulty Level, Affective Behavior, Telecommunications, Handheld Devices, Feedback (Response), English (Second Language), Second Language Instruction, Language Teachers, Concept Formation, Prior Learning, Grouping (Instructional Purposes), Tests, Scores, Markov Processes, Enrollment Trends, Man Machine Systems, Classification, Eye Movements, Physiology, Motion, Equipment, Academic Failure, Science Instruction, Educational Games, Misconceptions, Error Patterns, Active Learning, Writing Processes, Mathematics Skills, Success, Test Selection, Creativity, Geometry, Second Language Learning, Interpersonal Communication, Academic Language, Peer Evaluation, Emotional Response, Student Characteristics, Gender Differences, Electronic Mail, Learning, Student Attitudes, Cheating, Educational Administration, Justice, Natural Language Processing, College Students, Student Behavior, Automation, Scoring, Essays, Mandarin Chinese, Tone Languages, Parent Role, Mathematics Instruction, Experiential Learning, Individual Differences, Search Strategies, Visualization, Performance Based Assessment, Independent Study, Grade 8, Mathematics Achievement, Writing Skills, High School Students, Web Based Instruction, Instructional Materials, Graduates, Employment, Minority Group Students, At Risk Students, Program Effectiveness, Resource Units, Educational Needs, STEM Education, Career Choice, Video Technology, Social Values, Moral Values, Thinking Skills, Student Evaluation, Integrated Learning Systems, Error Correction, Foreign Countries, Chinese, Grading, Social Influences, Friendship, Item Response Theory, Grammar, Cooperation, Elective Courses, Course Selection (Students), Modeling (Psychology), Reaction Time, Homework, Liberal Arts, Young Children, Computer Software |
| Geographic Terms: | Brazil |
| ISBN: | 978-1-73367-360-0 |
| Abstract: | The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks such as writing, design, and collaborative problem solving, and it has been used in new informal contexts where student actions are at best semi-structured. This iteration of the conference includes a range of work in these and other areas. This year's conference features three invited talks: Julita Vassileva, Professor at the Department of Computer Science, University of Saskatchewan, Canada; Steve Ritter, Co-Founder and Chief Scientist, Carnegie Learning Inc., Pittsburgh; and Michael Mozer, Professor Department of Computer Science and Institute of Cognitive Science University of Colorado. The number of accepted papers include 22 full papers and 42 short papers. An additional 47 papers were accepted to the poster track. The poster and demo track itself accepted 14 contributions out of 34 submissions. Together with the "Journal of Educational Data Mining" ("JEDM"), the EDM 2019 conference held a "JEDM" Track that provides researchers a venue to deliver more substantial mature work than is possible in a conference proceeding and to present their work to a live audience. The papers submitted to this track followed the "JEDM" peer review process. Two such papers are featured in the conference's program. Additionally this year, papers that were regularly published in the journal in 2018 were invited for presentation at the conference. Two authors accepted this invitation. The main conference invited contributions to an Industry Track in addition to the main track. The EDM 2019 Industry Track received eleven submissions of which six were accepted. The EDM conference continues its tradition of providing opportunities for young researchers to present their work and receive feedback from their peers and senior researchers. The doctoral consortium this year features eight such presentations. This year's conference includes also an invited talk by the authors of the 2018 winner of the EDM Test of Time Award. This year's talk is delivered by Mykola Pechenizkiy. In addition to the main program, there are three workshops: (1) Learning Analytics: Building bridges between the Education and the Computing communities; (2) Reinforcement Learning for Educational Data Mining; and (3) Workshop on EDM & Games: Leveling Up Engaged Learning with Data-Rich Analytics. Three tutorials were presented as well: (1) Sharing and Reusing Data and Analytic Methods with LearnSphere; (2) Causal Discovery with Tetrad in LearnSphere's Tigris and Designing and Developing Open; and (3) Pedagogically-Based Predictive Models using the Moodle Analytics API. |
| Abstractor: | ERIC |
| Entry Date: | 2019 |
| Accession Number: | ED599096 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED599096 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Header | DbId: eric DbLabel: ERIC An: ED599096 AccessLevel: 3 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019) – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lynch%2C+Collin+F%2E%22">Lynch, Collin F.</searchLink><br /><searchLink fieldCode="AR" term="%22Merceron%2C+Agathe%22">Merceron, Agathe</searchLink><br /><searchLink fieldCode="AR" term="%22Desmarais%2C+Michel%22">Desmarais, Michel</searchLink><br /><searchLink fieldCode="AR" term="%22Nkambou%2C+Roger%22">Nkambou, Roger</searchLink><br /><searchLink fieldCode="AR" term="%22International+Educational+Data+Mining+Society%22">International Educational Data Mining Society</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 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The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks such as writing, design, and collaborative problem solving, and it has been used in new informal contexts where student actions are at best semi-structured. This iteration of the conference includes a range of work in these and other areas. This year's conference features three invited talks: Julita Vassileva, Professor at the Department of Computer Science, University of Saskatchewan, Canada; Steve Ritter, Co-Founder and Chief Scientist, Carnegie Learning Inc., Pittsburgh; and Michael Mozer, Professor Department of Computer Science and Institute of Cognitive Science University of Colorado. The number of accepted papers include 22 full papers and 42 short papers. An additional 47 papers were accepted to the poster track. The poster and demo track itself accepted 14 contributions out of 34 submissions. Together with the "Journal of Educational Data Mining" ("JEDM"), the EDM 2019 conference held a "JEDM" Track that provides researchers a venue to deliver more substantial mature work than is possible in a conference proceeding and to present their work to a live audience. The papers submitted to this track followed the "JEDM" peer review process. Two such papers are featured in the conference's program. Additionally this year, papers that were regularly published in the journal in 2018 were invited for presentation at the conference. Two authors accepted this invitation. The main conference invited contributions to an Industry Track in addition to the main track. The EDM 2019 Industry Track received eleven submissions of which six were accepted. The EDM conference continues its tradition of providing opportunities for young researchers to present their work and receive feedback from their peers and senior researchers. The doctoral consortium this year features eight such presentations. This year's conference includes also an invited talk by the authors of the 2018 winner of the EDM Test of Time Award. This year's talk is delivered by Mykola Pechenizkiy. In addition to the main program, there are three workshops: (1) Learning Analytics: Building bridges between the Education and the Computing communities; (2) Reinforcement Learning for Educational Data Mining; and (3) Workshop on EDM & Games: Leveling Up Engaged Learning with Data-Rich Analytics. Three tutorials were presented as well: (1) Sharing and Reusing Data and Analytic Methods with LearnSphere; (2) Causal Discovery with Tetrad in LearnSphere's Tigris and Designing and Developing Open; and (3) Pedagogically-Based Predictive Models using the Moodle Analytics API. – Name: AbstractInfo Label: Abstractor Group: Ab Data: ERIC – Name: DateEntry Label: Entry Date Group: Date Data: 2019 – Name: AN Label: Accession Number Group: ID Data: ED599096 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED599096 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 823 Subjects: – SubjectFull: Data Collection Type: general – SubjectFull: Data Analysis Type: general – SubjectFull: Information Retrieval Type: general – SubjectFull: Content Analysis Type: general – SubjectFull: Educational Research Type: general – SubjectFull: Ethics Type: general – SubjectFull: Evidence Based Practice Type: general – SubjectFull: Grade Point Average Type: general – SubjectFull: Inquiry Type: general – SubjectFull: Game Based Learning Type: general – SubjectFull: Metacognition Type: general – SubjectFull: Student Records Type: general – SubjectFull: Undergraduate Students Type: general – SubjectFull: Predictor Variables Type: general – SubjectFull: Student Satisfaction Type: general – SubjectFull: Skill Development Type: general – SubjectFull: Prediction Type: general – SubjectFull: Programming Type: general – SubjectFull: Large Group Instruction Type: general – SubjectFull: Online Courses Type: general – SubjectFull: Dropouts Type: general – SubjectFull: Academic Achievement Type: general – SubjectFull: Computation Type: general – SubjectFull: Models Type: general – SubjectFull: Time to Degree Type: general – SubjectFull: Measurement Techniques Type: general – SubjectFull: Institutional Characteristics Type: general – SubjectFull: Help Seeking Type: general – SubjectFull: Student Motivation Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Interaction Type: general – SubjectFull: Teaching Methods Type: general – SubjectFull: Knowledge Level Type: general – SubjectFull: Video Games Type: general – SubjectFull: Cooperative Learning Type: general – SubjectFull: Problem Solving Type: general – SubjectFull: Grades (Scholastic) Type: general – SubjectFull: Reinforcement Type: general – SubjectFull: Intelligent Tutoring Systems Type: general – SubjectFull: Learning Activities Type: general – SubjectFull: Graphs Type: general – SubjectFull: Semantics Type: general – SubjectFull: Difficulty Level Type: general – SubjectFull: Affective Behavior Type: general – SubjectFull: Telecommunications Type: general – SubjectFull: Handheld Devices Type: general – SubjectFull: Feedback (Response) Type: general – SubjectFull: English (Second Language) Type: general – SubjectFull: Second Language Instruction Type: general – SubjectFull: Language Teachers Type: general – SubjectFull: Concept Formation Type: general – SubjectFull: Prior Learning Type: general – SubjectFull: Grouping (Instructional Purposes) Type: general – SubjectFull: Tests Type: general – SubjectFull: Scores Type: general – SubjectFull: Markov Processes Type: general – SubjectFull: Enrollment Trends Type: general – SubjectFull: Man Machine Systems Type: general – SubjectFull: Classification Type: general – SubjectFull: Eye Movements Type: general – SubjectFull: Physiology Type: general – SubjectFull: Motion Type: general – SubjectFull: Equipment Type: general – SubjectFull: Academic Failure Type: general – SubjectFull: Science Instruction Type: general – SubjectFull: Educational Games Type: general – SubjectFull: Misconceptions Type: general – SubjectFull: Error Patterns Type: general – SubjectFull: Active Learning Type: general – SubjectFull: Writing Processes Type: general – SubjectFull: Mathematics Skills Type: general – SubjectFull: Success Type: general – SubjectFull: Test Selection Type: general – SubjectFull: Creativity Type: general – SubjectFull: Geometry Type: general – SubjectFull: Second Language Learning Type: general – SubjectFull: Interpersonal Communication Type: general – SubjectFull: Academic Language Type: general – SubjectFull: Peer Evaluation Type: general – SubjectFull: Emotional Response Type: general – SubjectFull: Student Characteristics Type: general – SubjectFull: Gender Differences Type: general – SubjectFull: Electronic Mail Type: general – SubjectFull: Learning Type: general – SubjectFull: Student Attitudes Type: general – SubjectFull: Cheating Type: general – SubjectFull: Educational Administration Type: general – SubjectFull: Justice Type: general – SubjectFull: Natural Language Processing Type: general – SubjectFull: College Students Type: general – SubjectFull: Student Behavior Type: general – SubjectFull: Automation Type: general – SubjectFull: Scoring Type: general – SubjectFull: Essays Type: general – SubjectFull: Mandarin Chinese Type: general – SubjectFull: Tone Languages Type: general – SubjectFull: Parent Role Type: general – SubjectFull: Mathematics Instruction Type: general – SubjectFull: Experiential Learning Type: general – SubjectFull: Individual Differences Type: general – SubjectFull: Search Strategies Type: general – SubjectFull: Visualization Type: general – SubjectFull: Performance Based Assessment Type: general – SubjectFull: Independent Study Type: general – SubjectFull: Grade 8 Type: general – SubjectFull: Mathematics Achievement Type: general – SubjectFull: Writing Skills Type: general – SubjectFull: High School Students Type: general – SubjectFull: Web Based Instruction Type: general – SubjectFull: Instructional Materials Type: general – SubjectFull: Graduates Type: general – SubjectFull: Employment Type: general – SubjectFull: Minority Group Students Type: general – SubjectFull: At Risk Students Type: general – SubjectFull: Program Effectiveness Type: general – SubjectFull: Resource Units Type: general – SubjectFull: Educational Needs Type: general – SubjectFull: STEM Education Type: general – SubjectFull: Career Choice Type: general – SubjectFull: Video Technology Type: general – SubjectFull: Social Values Type: general – SubjectFull: Moral Values Type: general – SubjectFull: Thinking Skills Type: general – SubjectFull: Student Evaluation Type: general – SubjectFull: Integrated Learning Systems Type: general – SubjectFull: Error Correction Type: general – SubjectFull: Foreign Countries Type: general – SubjectFull: Chinese Type: general – SubjectFull: Grading Type: general – SubjectFull: Social Influences Type: general – SubjectFull: Friendship Type: general – SubjectFull: Item Response Theory Type: general – SubjectFull: Grammar Type: general – SubjectFull: Cooperation Type: general – SubjectFull: Elective Courses Type: general – SubjectFull: Course Selection (Students) Type: general – SubjectFull: Modeling (Psychology) Type: general – SubjectFull: Reaction Time Type: general – SubjectFull: Homework Type: general – SubjectFull: Liberal Arts Type: general – SubjectFull: Young Children Type: general – SubjectFull: Computer Software Type: general – SubjectFull: Brazil Type: general Titles: – TitleFull: Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019) Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: International Educational Data Mining Society – PersonEntity: Name: NameFull: Lynch, Collin F. – PersonEntity: Name: NameFull: Merceron, Agathe – PersonEntity: Name: NameFull: Desmarais, Michel – PersonEntity: Name: NameFull: Nkambou, Roger IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Type: published Y: 2019 Identifiers: – Type: isbn-print Value: 978-1-73367-360-0 Titles: – TitleFull: International Educational Data Mining Society Type: main |
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