Proceedings of the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, June 25-28, 2017)
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| Publicado no: | International Educational Data Mining Society (Jun 2017), p. 1-520 |
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| Publicado em: |
International Educational Data Mining Society
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| Acesso em linha: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2461133038 | ||
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| 035 | |a 2461133038 | ||
| 045 | 2 | |b d20170601 |b d20170630 | |
| 084 | |a ED596512 | ||
| 245 | 1 | |a Proceedings of the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, June 25-28, 2017) | |
| 260 | |b International Educational Data Mining Society |c Jun 2017 | ||
| 513 | |a Conference Proceeding | ||
| 520 | 3 | |a The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer Science and Technology at Tsinghua University; and Dr. Ron Cole, President of Boulder Learning Inc. The main conference invited contributions to the Research Track and Industry Track. 122 submissions were received (71 full, 47 short, 4 industry). 18 full papers papers were accepted (25% acceptance rate) and 32 short papers for oral presentation (42% acceptance rate) and an additional 39 for poster presentations, 3 demonstrations. The industry track includes all 4 submitted industry papers and 1 paper initially submitted as a full paper. The EDM conference provides opportunities for young researchers, and particularly Ph.D. students, to present their research ideas and receive feedback from the peers and more senior researchers. This year, the Doctoral Consortium features 6 such presentations. In addition to the main program, the conference includes 3 workshops: (1) Graph-based Educational Data Mining (G-EDM 2017); (2) Sharing and Reusing Data & Analytics Methods with LearnSphere; and (3) Deep Learning with Educational Data; and 2 tutorials: (1) Why Data Standards are Critical for EDM and AIED; and (2) Principal Stratification for EDM Experiments. [For the 2016 proceedings, see ED592609.] | |
| 651 | 4 | |a China | |
| 653 | |a Data Analysis | ||
| 653 | |a Data Collection | ||
| 653 | |a Graphs | ||
| 653 | |a Data Use | ||
| 653 | |a Educational Research | ||
| 653 | |a Standards | ||
| 653 | |a Automation | ||
| 653 | |a Classification | ||
| 653 | |a Attention | ||
| 653 | |a Educational Technology | ||
| 653 | |a Technology Uses in Education | ||
| 653 | |a Teaching Methods | ||
| 653 | |a Behavior | ||
| 653 | |a Large Group Instruction | ||
| 653 | |a Online Courses | ||
| 653 | |a Intelligent Tutoring Systems | ||
| 653 | |a Peer Teaching | ||
| 653 | |a Persistence | ||
| 653 | |a Models | ||
| 653 | |a Electronic Publishing | ||
| 653 | |a Measurement | ||
| 653 | |a Group Discussion | ||
| 653 | |a Problem Solving | ||
| 653 | |a Educational Games | ||
| 653 | |a Grades (Scholastic) | ||
| 653 | |a Prediction | ||
| 653 | |a Learner Engagement | ||
| 653 | |a Coding | ||
| 653 | |a Vocabulary | ||
| 653 | |a Generalization | ||
| 653 | |a Affective Behavior | ||
| 653 | |a Empathy | ||
| 653 | |a Cooperative Learning | ||
| 653 | |a Network Analysis | ||
| 653 | |a Learning Theories | ||
| 653 | |a Cues | ||
| 653 | |a Independent Study | ||
| 653 | |a Computer Literacy | ||
| 653 | |a Adults | ||
| 653 | |a Eye Movements | ||
| 653 | |a Mathematics Achievement | ||
| 653 | |a Language | ||
| 653 | |a Blended Learning | ||
| 653 | |a Knowledge Level | ||
| 653 | |a Feedback (Response) | ||
| 653 | |a Programming | ||
| 653 | |a Outcomes of Treatment | ||
| 653 | |a Misconceptions | ||
| 653 | |a Inquiry | ||
| 653 | |a Sciences | ||
| 653 | |a Student Behavior | ||
| 653 | |a Lecture Method | ||
| 653 | |a Interaction | ||
| 653 | |a Natural Language Processing | ||
| 653 | |a Reading Comprehension | ||
| 653 | |a Academic Persistence | ||
| 653 | |a Incidence | ||
| 653 | |a Cheating | ||
| 653 | |a Internship Programs | ||
| 653 | |a Moral Values | ||
| 653 | |a Artificial Intelligence | ||
| 653 | |a Content Analysis | ||
| 653 | |a Learning Strategies | ||
| 653 | |a Academic Achievement | ||
| 653 | |a Networks | ||
| 653 | |a Dropouts | ||
| 653 | |a Secondary School Science | ||
| 653 | |a Middle Schools | ||
| 653 | |a Dormitories | ||
| 653 | |a Mathematics | ||
| 653 | |a Tests | ||
| 653 | |a Integrated Learning Systems | ||
| 653 | |a Social Networks | ||
| 653 | |a Web Sites | ||
| 653 | |a Curriculum | ||
| 653 | |a STEM Education | ||
| 653 | |a Reading Difficulties | ||
| 653 | |a Scoring | ||
| 653 | |a Writing (Composition) | ||
| 653 | |a Relevance (Education) | ||
| 653 | |a Reading Skills | ||
| 653 | |a College Freshmen | ||
| 653 | |a Computation | ||
| 653 | |a Bayesian Statistics | ||
| 653 | |a Questioning Techniques | ||
| 653 | |a Metacognition | ||
| 653 | |a College Students | ||
| 653 | |a Study Habits | ||
| 653 | |a Experiments | ||
| 653 | |a Evaluation Methods | ||
| 653 | |a Group Activities | ||
| 653 | |a Reflection | ||
| 653 | |a Computer Simulation | ||
| 653 | |a Essays | ||
| 653 | |a Journal Articles | ||
| 653 | |a Foreign Countries | ||
| 653 | |a Home Programs | ||
| 653 | |a Early Childhood Education | ||
| 653 | |a Success | ||
| 653 | |a Mathematics Instruction | ||
| 653 | |a Computer Assisted Testing | ||
| 653 | |a Information Dissemination | ||
| 653 | |a Transformative Learning | ||
| 773 | 0 | |t International Educational Data Mining Society |g (Jun 2017), p. 1-520 | |
| 786 | 0 | |d ProQuest |t ERIC | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2461133038/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://eric.ed.gov/?id=ED596512 |