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EDUCATIONAL DATA MINING 2021 The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners. The 14 th iteration of the conference, EDM 2021, will take place in a hybrid formatCALL FOR PAPERS
JEDM Journal Track Papers — Papers submitted to the Journal of Educational Data Mining track (and accepted before May 30, 2021) will be published in JEDM and presented during the JEDM track of the conference. Industry Papers — 6 pages. Should describe innovative uses of EDM techniques in a commercial setting. Doctoral Consortium— 2-4 pages.
TOWARDS FAIR EDUCATIONAL DATA MINING: A CASE STUDY ON2 0 1 1.67 2 2.33 2.67 3 3.33 3.67 4 GPA 0 5 10 15 20 GPA Proportion GPA distribution by race Black Nonblack Figure 2: GPA distribution by race. students is skewed towards lower GPAs, while average GPA QDKT: QUESTION-CENTRIC DEEP KNOWLEDGE TRACING Dataset Number of questions Avg. Obs. per question DKT (skill) DKT (question) ASSISTments 2017 1,183 145.76 0.72 0.74 ASSISTments 2009 16,891 19.27 0.74 0.68 ANALYZING STUDENT PROCRASTINATION IN MOOCS: A MULTIVARIATE2 Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach Mengfan Yao Department of Computer Science University at Albany - SUNY myao@albany.edu THE STATE OF EDUCATIONAL DATA MINING IN 2009: A REVIEW AND The State of Educational Data Mining in 2009: A Review and Future Visions RYAN S.J.D. BAKER Department of Social Science and Policy Studies Worcester Polytechnic Institute MODELING STUDENTS’ LEARNING AND VARIABILITY OF PERFORMANCE nding a global optimum. In our case there is a straightfor-ward way to get a good initial estimate of parameters: b p= mean of t sp(for the given p); a p= -1; s= mean of EDUCATIONALDATAMINING.ORGJEDMCONFERENCESTEST OF TIME AWARDSRESOURCESRELATED ORGSMAILING LIST educationaldatamining.org. Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from EDUCATIONAL DATA MINING 2020 The theme of this year’s conference is “Improving Learning Outcomes for All Learners”. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to improve learning outcomes. (2) Using EDM to promoting SUBMISSION – EDUCATIONAL DATA MINING 2021 JEDM Journal Track Papers — Papers submitted to the Journal of Educational Data Mining track (and accepted before May 30, 2021) will be published in JEDM and presented during the JEDM track of the conference. Industry Papers — 6 pages. Should describe innovative uses of EDM techniques in a commercial setting. Doctoral Consortium— 2-4 pages.
EDUCATIONAL DATA MINING 2021 The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners. The 14 th iteration of the conference, EDM 2021, will take place in a hybrid formatCALL FOR PAPERS
JEDM Journal Track Papers — Papers submitted to the Journal of Educational Data Mining track (and accepted before May 30, 2021) will be published in JEDM and presented during the JEDM track of the conference. Industry Papers — 6 pages. Should describe innovative uses of EDM techniques in a commercial setting. Doctoral Consortium— 2-4 pages.
TOWARDS FAIR EDUCATIONAL DATA MINING: A CASE STUDY ON2 0 1 1.67 2 2.33 2.67 3 3.33 3.67 4 GPA 0 5 10 15 20 GPA Proportion GPA distribution by race Black Nonblack Figure 2: GPA distribution by race. students is skewed towards lower GPAs, while average GPA QDKT: QUESTION-CENTRIC DEEP KNOWLEDGE TRACING Dataset Number of questions Avg. Obs. per question DKT (skill) DKT (question) ASSISTments 2017 1,183 145.76 0.72 0.74 ASSISTments 2009 16,891 19.27 0.74 0.68 ANALYZING STUDENT PROCRASTINATION IN MOOCS: A MULTIVARIATE2 Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach Mengfan Yao Department of Computer Science University at Albany - SUNY myao@albany.edu THE STATE OF EDUCATIONAL DATA MINING IN 2009: A REVIEW AND The State of Educational Data Mining in 2009: A Review and Future Visions RYAN S.J.D. BAKER Department of Social Science and Policy Studies Worcester Polytechnic Institute MODELING STUDENTS’ LEARNING AND VARIABILITY OF PERFORMANCE nding a global optimum. In our case there is a straightfor-ward way to get a good initial estimate of parameters: b p= mean of t sp(for the given p); a p= -1; s= mean of EDUCATIONAL DATA MINING 2021 The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners. The 14 th iteration of the conference, EDM 2021, will take place in a hybrid formatREGISTRATION
Welcome to EDM 2021! Please note that this year, we wanted to ensure a low price for students to increase their participation to the conference. Therefore, student registration fee is in most cases half price. We encourage you to register as soon as possible, as all on-site registrations are refundable 100% up to May 15 23:59 CEST(UTC+2) (end
THE POTENTIALS OF EDUCATIONAL DATA MINING FOR RESEARCHING Our article introduces the Journal of Educational Data Mining's Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for data mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM. A PROCRASTINATION INDEX FOR ONLINE LEARNING BASED ON A Procrastination Index for Online Learning Based on Assignment Start Time Lalitha Agnihotri1, Ryan S. Baker2, Steve Stalzer1 McGraw Hill Education1, University of Pennsylvania2 Lalitha.Agnihotri@mheducation.com, rybaker@upenn.edu, Steve.Stalzer@mheducation.com ONLINE ACADEMIC COURSE PERFORMANCE PREDICTION USING Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network Hamid Karimi1, Tyler Derr1, Jiangtao Huang2, Jiliang Tang1 1 Michigan state University, {karimiha, derrtyle, tangjili}@msu.edu 2 Nanning Normal University, China, hjt@gxtc.edu.cn ABSTRACT Online learning has attracted a large numberof partici-
LEARNING CURVES VERSUS PROBLEM DIFCULTY: AN ANALYSIS OF 2 4 6 8 10 12 14 - 2.4 - 2.2 - 2.0 - 1.8 - 1.6 - 1.4 - 1.2 2874 opportunity t difficulty Figure 1. Learning curves for the rst KC listed in Table1. Difculty THE READING ABILITY OF COLLEGE FRESHMEN The Reading Ability of College Freshmen Andrew M. Olney Institute for Intelligent Systems University of Memphis Memphis, TN 38152aolney@memphis.edu
PREDICTING STUDENTS DROP OUT: A CASE STUDY Predicting Students Drop Out: A Case Study Gerben W. Dekker1, Mykola Pechenizkiy2 and Jan M. Vleeshouwers1 g.w.dekker@student.tue.nl, {m.pechenizkiy, j.m.vleeshouwers}@tue.nl 1Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands 2Department of Computer Science, Eindhoven University of Technology,the Netherlands
HIERARCHICAL CLUSTER ANALYSIS HEATMAPS AND PATTERN activity (Cluster 1) either received a grade of F or withdrew (W) from the course, whereas many students with higher activity (Cluster 3) received a grade of A. NEXT-TERM STUDENT PERFORMANCE PREDICTION: A RECOMMENDER An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course EDUCATIONALDATAMINING.ORGJEDMCONFERENCESTEST OF TIME AWARDSRESOURCESRELATED ORGSMAILING LIST educationaldatamining.org. Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from THE READING ABILITY OF COLLEGE FRESHMEN The Reading Ability of College Freshmen Andrew M. Olney Institute for Intelligent Systems University of Memphis Memphis, TN 38152aolney@memphis.edu
LEARNING CURVES VERSUS PROBLEM DIFCULTY: AN ANALYSIS OF 2 4 6 8 10 12 14 - 2.4 - 2.2 - 2.0 - 1.8 - 1.6 - 1.4 - 1.2 2874 opportunity t difficulty Figure 1. Learning curves for the rst KC listed in Table1. Difculty MODELING STUDENTS’ LEARNING AND VARIABILITY OF PERFORMANCE nding a global optimum. In our case there is a straightfor-ward way to get a good initial estimate of parameters: b p= mean of t sp(for the given p); a p= -1; s= mean of QDKT: QUESTION-CENTRIC DEEP KNOWLEDGE TRACING Dataset Number of questions Avg. Obs. per question DKT (skill) DKT (question) ASSISTments 2017 1,183 145.76 0.72 0.74 ASSISTments 2009 16,891 19.27 0.74 0.68PROCEEDINGS
Proceedings. This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. A PROCRASTINATION INDEX FOR ONLINE LEARNING BASED ON A Procrastination Index for Online Learning Based on Assignment Start Time Lalitha Agnihotri1, Ryan S. Baker2, Steve Stalzer1 McGraw Hill Education1, University of Pennsylvania2 Lalitha.Agnihotri@mheducation.com, rybaker@upenn.edu, Steve.Stalzer@mheducation.com JOURNAL OF EDUCATIONAL DATA MINING The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100) is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is completely and permanently freeand open-access to
ANALYZING STUDENT PROCRASTINATION IN MOOCS: A MULTIVARIATE Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach Mengfan Yao Department of Computer Science University at Albany - SUNY myao@albany.edu TOWARDS FAIR EDUCATIONAL DATA MINING: A CASE STUDY ON 0 1 1.67 2 2.33 2.67 3 3.33 3.67 4 GPA 0 5 10 15 20 GPA Proportion GPA distribution by race Black Nonblack Figure 2: GPA distribution by race. students is skewed towards lower GPAs, while average GPA EDUCATIONALDATAMINING.ORGJEDMCONFERENCESTEST OF TIME AWARDSRESOURCESRELATED ORGSMAILING LIST educationaldatamining.org. Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from JOURNAL OF EDUCATIONAL DATA MINING The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100) is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is completely and permanently freeand open-access to
TOWARDS FAIR EDUCATIONAL DATA MINING: A CASE STUDY ON 0 1 1.67 2 2.33 2.67 3 3.33 3.67 4 GPA 0 5 10 15 20 GPA Proportion GPA distribution by race Black Nonblack Figure 2: GPA distribution by race. students is skewed towards lower GPAs, while average GPA QDKT: QUESTION-CENTRIC DEEP KNOWLEDGE TRACING Dataset Number of questions Avg. Obs. per question DKT (skill) DKT (question) ASSISTments 2017 1,183 145.76 0.72 0.74 ASSISTments 2009 16,891 19.27 0.74 0.68 A PROCRASTINATION INDEX FOR ONLINE LEARNING BASED ON A Procrastination Index for Online Learning Based on Assignment Start Time Lalitha Agnihotri1, Ryan S. Baker2, Steve Stalzer1 McGraw Hill Education1, University of Pennsylvania2 Lalitha.Agnihotri@mheducation.com, rybaker@upenn.edu, Steve.Stalzer@mheducation.comPROCEEDINGS
Proceedings. This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. LEARNING CURVES VERSUS PROBLEM DIFCULTY: AN ANALYSIS OF 2 4 6 8 10 12 14 - 2.4 - 2.2 - 2.0 - 1.8 - 1.6 - 1.4 - 1.2 2874 opportunity t difficulty Figure 1. Learning curves for the rst KC listed in Table1. Difculty MODELING STUDENTS’ LEARNING AND VARIABILITY OF PERFORMANCE nding a global optimum. In our case there is a straightfor-ward way to get a good initial estimate of parameters: b p= mean of t sp(for the given p); a p= -1; s= mean of THE READING ABILITY OF COLLEGE FRESHMEN The Reading Ability of College Freshmen Andrew M. Olney Institute for Intelligent Systems University of Memphis Memphis, TN 38152aolney@memphis.edu
ANALYZING STUDENT PROCRASTINATION IN MOOCS: A MULTIVARIATE Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach Mengfan Yao Department of Computer Science University at Albany - SUNY myao@albany.edu EDUCATIONAL DATA MINING 2020 The theme of this year’s conference is “Improving Learning Outcomes for All Learners”. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to improve learning outcomes. (2) Using EDM to promoting EDUCATIONAL DATA MINING 2021 The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners. The 14 th iteration of the conference, EDM 2021, will take place in a hybrid format SUBMISSION – EDUCATIONAL DATA MINING 2021 JEDM Journal Track Papers — Papers submitted to the Journal of Educational Data Mining track (and accepted before May 30, 2021) will be published in JEDM and presented during the JEDM track of the conference. Industry Papers — 6 pages. Should describe innovative uses of EDM techniques in a commercial setting. Doctoral Consortium— 2-4 pages.
PROCEEDINGS
Workshop and Tutorial proposals: Jan 14, 2018 Abstracts for all papers, posters, and demos: Feb. 28, 2018 Full and short paper: Mar. 7, 2018 JEDM track papers: Mar. 7, 2018 Industry papers: Mar. 7, 2018 Doctoral consortium papers: Mar 26, 2018 All due 11:59 PM PSTPROCEEDINGS
Proceedings. This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. HOW DEEP IS KNOWLEDGE TRACING? cation) and descriptions of the work in the blogosphere (e.g., ). Piech et al. reported substantial improvements in prediction performance with DKT over BKT on two real- THE STATE OF EDUCATIONAL DATA MINING IN 2009: A REVIEW AND The State of Educational Data Mining in 2009: A Review and Future Visions RYAN S.J.D. BAKER Department of Social Science and Policy Studies Worcester Polytechnic Institute STUDYING MOOC COMPLETION AT SCALE USING THE MOOC Studying MOOC Completion at Scale Using the MOOC Replication Framework Juan Miguel L. Andres Ryan S. Baker University of Pennsylvania Philadelphia, PA 19104 A PILOT STUDY ON LOGIC PROOF TUTORING USING HINTS A pilot study on logic proof tutoring using hints generated from historical student data Tiffany Barnes1, John Stamper1, Lorrie Lehman1, and Marvin Croy2 {tbarnes2, jcstampe, ljlehman,mjcroy}@uncc.edu
EARLY DETECTION OF STUDENTS AT RISK To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and private university to predict student dropout as a basis for a targeted intervention. EDUCATIONALDATAMINING.ORGJEDMCONFERENCESTEST OF TIME AWARDSRESOURCESRELATED ORGSMAILING LIST educationaldatamining.org. Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from JOURNAL OF EDUCATIONAL DATA MINING The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100) is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is completely and permanently freeand open-access to
TOWARDS FAIR EDUCATIONAL DATA MINING: A CASE STUDY ON 0 1 1.67 2 2.33 2.67 3 3.33 3.67 4 GPA 0 5 10 15 20 GPA Proportion GPA distribution by race Black Nonblack Figure 2: GPA distribution by race. students is skewed towards lower GPAs, while average GPA QDKT: QUESTION-CENTRIC DEEP KNOWLEDGE TRACING Dataset Number of questions Avg. Obs. per question DKT (skill) DKT (question) ASSISTments 2017 1,183 145.76 0.72 0.74 ASSISTments 2009 16,891 19.27 0.74 0.68 A PROCRASTINATION INDEX FOR ONLINE LEARNING BASED ON A Procrastination Index for Online Learning Based on Assignment Start Time Lalitha Agnihotri1, Ryan S. Baker2, Steve Stalzer1 McGraw Hill Education1, University of Pennsylvania2 Lalitha.Agnihotri@mheducation.com, rybaker@upenn.edu, Steve.Stalzer@mheducation.comPROCEEDINGS
Proceedings. This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. LEARNING CURVES VERSUS PROBLEM DIFCULTY: AN ANALYSIS OF 2 4 6 8 10 12 14 - 2.4 - 2.2 - 2.0 - 1.8 - 1.6 - 1.4 - 1.2 2874 opportunity t difficulty Figure 1. Learning curves for the rst KC listed in Table1. Difculty MODELING STUDENTS’ LEARNING AND VARIABILITY OF PERFORMANCE nding a global optimum. In our case there is a straightfor-ward way to get a good initial estimate of parameters: b p= mean of t sp(for the given p); a p= -1; s= mean of THE READING ABILITY OF COLLEGE FRESHMEN The Reading Ability of College Freshmen Andrew M. Olney Institute for Intelligent Systems University of Memphis Memphis, TN 38152aolney@memphis.edu
ANALYZING STUDENT PROCRASTINATION IN MOOCS: A MULTIVARIATE Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach Mengfan Yao Department of Computer Science University at Albany - SUNY myao@albany.edu EDUCATIONALDATAMINING.ORGJEDMCONFERENCESTEST OF TIME AWARDSRESOURCESRELATED ORGSMAILING LIST educationaldatamining.org. Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from THE READING ABILITY OF COLLEGE FRESHMEN The Reading Ability of College Freshmen Andrew M. Olney Institute for Intelligent Systems University of Memphis Memphis, TN 38152aolney@memphis.edu
LEARNING CURVES VERSUS PROBLEM DIFCULTY: AN ANALYSIS OF 2 4 6 8 10 12 14 - 2.4 - 2.2 - 2.0 - 1.8 - 1.6 - 1.4 - 1.2 2874 opportunity t difficulty Figure 1. Learning curves for the rst KC listed in Table1. Difculty MODELING STUDENTS’ LEARNING AND VARIABILITY OF PERFORMANCE nding a global optimum. In our case there is a straightfor-ward way to get a good initial estimate of parameters: b p= mean of t sp(for the given p); a p= -1; s= mean of QDKT: QUESTION-CENTRIC DEEP KNOWLEDGE TRACING Dataset Number of questions Avg. Obs. per question DKT (skill) DKT (question) ASSISTments 2017 1,183 145.76 0.72 0.74 ASSISTments 2009 16,891 19.27 0.74 0.68PROCEEDINGS
Proceedings. This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. A PROCRASTINATION INDEX FOR ONLINE LEARNING BASED ON A Procrastination Index for Online Learning Based on Assignment Start Time Lalitha Agnihotri1, Ryan S. Baker2, Steve Stalzer1 McGraw Hill Education1, University of Pennsylvania2 Lalitha.Agnihotri@mheducation.com, rybaker@upenn.edu, Steve.Stalzer@mheducation.com JOURNAL OF EDUCATIONAL DATA MINING The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100) is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is completely and permanently freeand open-access to
ANALYZING STUDENT PROCRASTINATION IN MOOCS: A MULTIVARIATE Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach Mengfan Yao Department of Computer Science University at Albany - SUNY myao@albany.edu TOWARDS FAIR EDUCATIONAL DATA MINING: A CASE STUDY ON 0 1 1.67 2 2.33 2.67 3 3.33 3.67 4 GPA 0 5 10 15 20 GPA Proportion GPA distribution by race Black Nonblack Figure 2: GPA distribution by race. students is skewed towards lower GPAs, while average GPA EDUCATIONAL DATA MINING 2020 The theme of this year’s conference is “Improving Learning Outcomes for All Learners”. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to improve learning outcomes. (2) Using EDM to promoting EDUCATIONAL DATA MINING 2021 The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners. The 14 th iteration of the conference, EDM 2021, will take place in a hybrid format SUBMISSION – EDUCATIONAL DATA MINING 2021 JEDM Journal Track Papers — Papers submitted to the Journal of Educational Data Mining track (and accepted before May 30, 2021) will be published in JEDM and presented during the JEDM track of the conference. Industry Papers — 6 pages. Should describe innovative uses of EDM techniques in a commercial setting. Doctoral Consortium— 2-4 pages.
PROCEEDINGS
Workshop and Tutorial proposals: Jan 14, 2018 Abstracts for all papers, posters, and demos: Feb. 28, 2018 Full and short paper: Mar. 7, 2018 JEDM track papers: Mar. 7, 2018 Industry papers: Mar. 7, 2018 Doctoral consortium papers: Mar 26, 2018 All due 11:59 PM PSTPROCEEDINGS
Proceedings. This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. HOW DEEP IS KNOWLEDGE TRACING? cation) and descriptions of the work in the blogosphere (e.g., ). Piech et al. reported substantial improvements in prediction performance with DKT over BKT on two real- THE STATE OF EDUCATIONAL DATA MINING IN 2009: A REVIEW AND The State of Educational Data Mining in 2009: A Review and Future Visions RYAN S.J.D. BAKER Department of Social Science and Policy Studies Worcester Polytechnic Institute STUDYING MOOC COMPLETION AT SCALE USING THE MOOC Studying MOOC Completion at Scale Using the MOOC Replication Framework Juan Miguel L. Andres Ryan S. Baker University of Pennsylvania Philadelphia, PA 19104 A PILOT STUDY ON LOGIC PROOF TUTORING USING HINTS A pilot study on logic proof tutoring using hints generated from historical student data Tiffany Barnes1, John Stamper1, Lorrie Lehman1, and Marvin Croy2 {tbarnes2, jcstampe, ljlehman,mjcroy}@uncc.edu
EARLY DETECTION OF STUDENTS AT RISK To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and private university to predict student dropout as a basis for a targeted intervention. educationaldatamining.org hosted by the International Educational Data Mining Society* Home
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EDUCATIONAL DATA MINING is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from students’ use of interactive learning environments, computer-supported collaborative learning, or administrative data from schools and universities, it often has multiple levels of meaningful hierarchy, which often need to be determined by properties of the data itself, rather than in advance. Issues of time, sequence, and context also play important roles in the study of educational data. The International Educational Data Mining Society’s aim is to support collaboration and scientific development in this new discipline, through the organization of the EDM conference series, the Journal of Educational Data Mining , and mailing lists, as well as the
development of community resources, to support the sharing of data andtechniques.
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RECENT NEWS
Proceedings of EDM 2020 now available here The Prof. Ram Kumar Educational Data Mining Test of Time Awardsnow available
The latest issue of the Journal of Educational Data Mining (JEDM), Vol. 11 (2019), is now available hereUPCOMING CONFERENCE
Fourteenth International Conference on Educational Data Mining (EDM 2021), June 29 – July 2, Paris, France Contactadmin@educationaldatamining.org join our mailing listsDetails
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