Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, ...
Author: Bhatt, Chintan
Publisher: IGI Global
Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.
The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems
Author: Cristobal Romero
Publisher: CRC Press
Category: Business & Economics
Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making. Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.
Author: Alejandro Peña-AyalaPublish On: 2013-11-08
This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research.
Author: Alejandro Peña-Ayala
This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.
The thesis titled "Educational Data Mining of Curriculum and Student Data" looks at some of the important factors that are contributing to the growing interest and research in the Educational Data Mining domain such as scope of online ...
Author: Taniya Neogi
Category: Data mining
The thesis titled "Educational Data Mining of Curriculum and Student Data" looks at some of the important factors that are contributing to the growing interest and research in the Educational Data Mining domain such as scope of online education, availability of huge repositories of relevant data and need to identify potentials and address pitfalls. It is an attempt at analyzing a publicly available data set to understand the process of data mining while trying to discover the potential factors related to curriculum and student data that might have an impact on the success or failure of a student in a particular course. Despite several limitations, it was successful in identifying "Activity-Grade" predictors that may have considerable impact on a student's grade and may be able to identify patterns in student learning behavior as an indicator of student success. It identified predictors like student posts in course forums, number of active days, assignments completed, discipline of study, etc. that impact and determine a student's success in terms of grade. Different approaches such feature selection, clustering, regression modeling and decision tree modeling were used to identify and validate these factors along with predicting the target grade of the students. It is hoped that this research may lay the foundation for future research into development of course recommender systems based on student and curriculum data which might benefit students looking to pursue online courses.
An archetype that is covered is that of learning by example. This is a guide for EDM implementation using R and Rattle open source data mining tools.
Author: R. S. Kamath
Publisher: River Publishers
Educational Data Mining (EDM) is one of the emerging fields in the pedagogy and andragogy paradigm, it concerns the techniques which research data coming from the educational domain. An archetype that is covered is that of learning by example. This is a guide for EDM implementation using R and Rattle open source data mining tools.
Author: International Educational Data Mining SocietyPublish On: 2012
The 5th International Conference on Educational Data Mining (EDM 2012) is held in picturesque Chania on the beautiful Crete island in Greece, under the auspices of the International Educational Data Mining Society (IEDMS).
Author: International Educational Data Mining Society
The 5th International Conference on Educational Data Mining (EDM 2012) is held in picturesque Chania on the beautiful Crete island in Greece, under the auspices of the International Educational Data Mining Society (IEDMS). The EDM 2012 conference is a leading international forum for high quality research that mines large data sets of educational data to answer educational research questions. These data sets may come from learning management systems, interactive learning environments, intelligent tutoring systems, or any system used in a learning context. The following papers are presented at the conference: (1) Stream Mining in Education? Dealing with Evolution (Myra Spiliopoulou); (2) From Text to Feedback: Leveraging Data Mining to Build Educational Technologies (Danielle S. McNamara); (3) Five Aspirations for Educational Data Mining (Bob Dolan and John Behrens); (4) Assisting Instructional Assessment of Undergraduate Collaborative Wiki and SVN Activities (Jihie Kim, Erin Shaw, Hao Xu and Adarsh G V); (5) Automated Student Model Improvement (Kenneth R. Koedinger, Elizabeth A. McLaughlin and John C. Stamper); (6) Automatic Discovery of Speech Act Categories in Educational Games (Vasile Rus, Arthur Graesser, Cristian Moldovan and Nobal Niraula); (7) Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction (Shubhendu Trivedi, Zachary Pardos, Gabor Sarkozy and Neil Heffernan); (8) Comparison of methods to trace multiple subskills: Is LR-DBN best? (Yanbo Xu and Jack Mostow); (9) Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models (Jose Gonzalez-Brenes and Jack Mostow); (10) Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution (John S. Kinnebrew and Gautam Biswas); (11) Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning (Francois Bouchet, John S. Kinnebrew, Gautam Biswas and Roger Azevedo); (12) Learner Differences in Hint Processing (Ilya Goldin, Kenneth R. Koedinger and Vincent Aleven); (13) Methods to find the number of latent skills (Behzad Beheshti, Michel C. Desmarais and Rhouma Naceur); (14) Mining Student Behavior Patterns in Reading Comprehension Tasks (Terry Peckham and Gordon McCalla); (15) Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory (Yoav Bergner, Stefan Droschler, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton and David E. Pritchard); (16) Predicting drop-out from social behaviour of students (Tomas Obsivac, Lubomir Popelinsky, Jaroslav Bayer, Jan Geryk and Hana Bydzovska); (17) Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations (Martina A. Rau and Richard Scheines); (18) The Impact on Individualizing Student Models on Necessary Practice Opportunities (Jung In Lee and Emma Brunskill); (19) Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra (Ryan S.J.D. Baker, Sujith M. Gowda, Michael Wixon, Jessica Kalka, Angela Z. Wagner, Aatish Salvi, Vincent Aleven, Gail W. Kusbit, Jaclyn Ocumpaugh and Lisa Rossi); (20) Using Edit Distance to Analyse Errors in a Natural Language to Logic Translation Corpus (Dave Barker-Plummer, Robert Dale, and Richard Cox); (21) Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain (Leigh Ann Sudol, Kelly Rivers and Thomas K. Harris); (22) Classification via clustering for predicting final marks based on student participation in forums (M.I. Lopez, J.M. Luna, C. Romero, and S. Ventura); (23) Development of a Workbench to Address the Educational Data Mining Bottleneck (Ma. Mercedes T. Rodrigo, Ryan S. J. D. Baker, Bruce McLaren, Alejandra Jayme and Thomas T. Dy); (24) Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models (Jennifer L. Sabourin, Bradford W. Mott and James C. Lester); (25) Identifying Successful Learners from Interaction Behaviour (Judi McCuaig and Julia Baldwin); (26) Interaction Networks: Generating High Level Hints Based on Network Community Clusterings (Michael Eagle, Matthew Johnson and Tiffany Barnes); (27) Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques (Martina A. Rau and Zachary A. Pardos); (28) Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning (Carol Forsyth, Philip Pavlik Jr, Arthur C. Graesser, Zhiqiang Cai, Mae-Lynn Germany, Keith Millis, Heather Butler, Diane Halpern and Robert P. Dolan); (29) Leveraging First Response Time into the Knowledge Tracing Model (Yutao Wang and Neil T. Heffernan); (30) Meta-learning Approach for Automatic Parameter Tuning: A case of study with educational datasets (M.M. Molina, J.M. Luna, C. Romero, and S. Ventura); (31) Mining Concept Maps to Understand University Students' Learning (Jin Soung Yoo and Moon-Heum Cho); (32) Policy Building--An Extension To User Modeling (Michael V. Yudelson and Emma Brunskill); (33) The real world significance of performance prediction (Zachary A. Pardos, Qing Yang Wang and Shubhendu Trivedi); (34) The Rise of the Super Experiment (John C. Stamper, Derek Lomas, Dixie Ching, Steven Ritter, Kenneth R. Koedinger and Jonathan Steinhart); (35) Using Student Modeling to Estimate Student Knowledge Retention (Yutao Wang and Joseph Beck); (36) A promising classification method for predicting distance students' performance (Diego Garcia-Saiz and Marta Zorrilla); (37) Analyzing paths in a student database (Donatella Merlini, Renza Campagni and Renzo Sprugnoli); (38) Analyzing the behavior of a teacher network in a Web 2.0 environment (Eliana Scheihing, Carolina Aros and Daniel Guerra); (39) Automated Detection of Mentors and Players in an Educational Game (Fazel Keshtkar, Brent Morgan and Arthur Graesser); (40) Categorizing Students' Response Patterns using the Concept of Fractal Dimension (Rasil Warnakulasooriya and William Galen); (41) CurriM: Curriculum Mining (M. Pechenizkiy, N. Trcka, P. De Bra and Pedro A. Toledo); (42) Data mining techniques for design of ITS student models (Ritu Chaturvedi and C. I. Ezeife); (43) Deciding on Feedback Polarity and Timing (Stuart Johnson and Osmar Zaiane); (44) Finding Dependent Test Items: An Information Theory Based Approach (Xiaoxun Sun); (45) Fit-to-Model Statistics for Evaluating Quality of Bayesian Student Ability Estimation (Ling Tan); (46) Inferring learners' knowledge from observed actions (Anna N. Rafferty, Michelle M. Lamar and Thomas L. Griffiths); (47) Learning Paths in a Non-Personalizing e-Learning Environment (Agathe Merceron, Sebastian Schwarzrock, Margarita Elkina, Andreas Pursian, Liane Beuster, Albrecht Fortenbacher, Leonard Kappe, and Boris Wenzlaff); (48) Similarity Functions for Collaborative Master Recommendations (Alexandru Surpatean, Evgueni Smirnov and Nicolai Manie); (49) Social Networks Analysis for Quantifying Students' Performance in Teamwork (Pedro Crespo and Claudia Antunes); (50) Speaking (and touching) to learn: a method for mining the digital footprints of face-to-face collaboration (Roberto Martinez Maldonado, Kalina Yacef and Judy Kay); (51) Stress Analytics in Education (Rafal Kocielnik, Mykola Pechenizkiy and Natalia Sidorova); and (52) Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results (Stephen E. Fancsali). Individual papers contain figures, tables, references and footnotes. [Support for this publication was provided by Carnegie Learning, Pearson and LearnLab.].
You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in.
Author: Gerardus Blokdyk
How do you manage Educational data mining risk? Are there Educational data mining problems defined? How do you ensure that the Educational data mining opportunity is realistic? What sources do you use to gather information for a Educational data mining study? What are the Educational data mining investment costs? This best-selling Educational Data Mining self-assessment will make you the trusted Educational Data Mining domain adviser by revealing just what you need to know to be fluent and ready for any Educational Data Mining challenge. How do I reduce the effort in the Educational Data Mining work to be done to get problems solved? How can I ensure that plans of action include every Educational Data Mining task and that every Educational Data Mining outcome is in place? How will I save time investigating strategic and tactical options and ensuring Educational Data Mining costs are low? How can I deliver tailored Educational Data Mining advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Educational Data Mining essentials are covered, from every angle: the Educational Data Mining self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Educational Data Mining outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Educational Data Mining practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Educational Data Mining are maximized with professional results. Your purchase includes access details to the Educational Data Mining self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Educational Data Mining Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author: International Educational Data Mining SocietyPublish On: 2013
Since its inception in 2008, the Educational Data Mining (EDM) conference series has featured some of the most innovative and fascinating basic and applied research centered on data mining, education, and learning technologies.
Author: International Educational Data Mining Society
With An Information-Theoretic Approach (Brett van de Sande); (50) Test-Size Reduction for Concept Estimation (Divyanshu Vats, Christoph Studer, Andrew S. Lan, Lawrence Carin and Richard Baraniuk); (51) Reading into the Text: Investigating the Influence of Text Complexity on Cognitive Engagement (Benjamin Vega, Shi Feng, Blair Lehman, Art Graesser and Sidney D'Mello); (52) Using Students' Programming Behavior to Predict Success in an Introductory Mathematics Course (Arto Vihavainen, Matti Luukkainen and Jaakko Kurhila); (53) Do Students Really Learn an Equal Amount Independent of Whether They Get an Item Correct or Wrong? (Seth Adjei, Seye Salehizadeh, Yutao Wang and Neil Heffernan); (54) Analysis of Students Clustering Results Based on Moodle Log Data (Angela Bovo, Stephane Sanchez, Olivier Heguy and Yves Duthen); (55) Mining the Impact of Course Assignments on Student Performance (Ritu Chaturvedi and Christie Ezeife); (56) Mining Users Behaviors in Intelligent Educational Games: Prime Climb a Case Study (Alireza Davoodi, Samad Kardan and Cristina Conati); (57) Bringing Student Backgrounds Online: MOOC User Demographics, Site Usage, and Online Learning (Jennifer Deboer, Glenda S. Stump, Daniel Seaton, Andrew Ho, David E. Pritchard and Lori Breslow); (58) Detecting Player Goals from Game Log Files (Kristen Dicerbo and Khusro Kidwai); (59) A Prediction Model that Uses the Sequence of Attempts and Hints to Better Predict Knowledge: Better to Attempt the Problem First, Rather Than Ask for a Hint (Hien Duong, Linglong Zhu, Yutao Wang and Neil Heffernan); (60) Towards the Development of a Classification Service for Predicting Students' Performance (Diego Garca-Saiz and Marta Zorrilla); (61) Identifying and Visualizing the Similarities Between Course Content at a Learning Object, Module and Program Level (Kyle Goslin and Markus Hofmann); (62) Using ITS Generated Data to Predict Standardized Test Scores (Kim Kelly, Ivon Arroyo and Neil Heffernan); (63) Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data (Andrew Lan, Christoph Studer, Andrew Waters and Richard Baraniuk); (64) Component Model in Discourse Analysis (Haiying Li, Art Graesser and Zhiqiang Cai); (65) Modeling Student Retention in an Environment with Delayed Testing (Shoujing Li, Xiaolu Xiong and Joseph Beck); (66) Predicting Group Programming Project Performance using SVN Activity Traces (Sen Liu, Jihie Kim and Sofus Macskassy); (67) Toward Predicting Test Score Gains with Online Behavior Data of Teachers (Keith Maull and Tamara Sumner); (68) Domain-Independent Proximity Measures in Intelligent Tutoring Systems; (Bassam Mokbel, Sebastian Gross, Benjamin Paassen, Niels Pinkwart and Barbara Hammer); (69) Exploring Exploration: Inquiries into Exploration Behavior in Complex Problem Solving Assessment (Jonas Muller, Andre Kretzschmar and Samuel Greiff); (70) The Complex Dynamics of Aggregate Learning Curves (Tristan Nixon, Stephen Fancsali and Steven Ritter); (71) Extracting Time-Evolving Latent Skills from Examination Time Series (Shinichi Oeda and Kenji Yamanishi); (72) Uncovering Class-Wide Patterns in Responses to True/False Questions (Andrew Pawl); (73) Causal Modeling to Understand the Relationship between Student Attitudes, Affect and Outcomes (Dovan Rai, Joseph Beck and Ivon Arroyo); (74) Determining Review Coverage by Extracting Topic Sentences Using A Graph-Based Clustering Approach (Lakshmi Ramachandran, Balaraman Ravindran and Edward Gehringer); (75) Affective State Detection in Educational Systems through Mining Multimodal Data Sources (Sergio Salmeron-Majadas, Olga C. Santos and Jesus G. Boticario); (76) Exploring the Relationship between Course Structure and eText Usage in Blended and Open Online Courses (Daniel T. Seaton, Yoav Bergner and David E. Pritchard); (77) Data Preprocessing Using a Priori Knowledge (Jean Simon); (78) Discovering the Relationship between Student Effort and Ability for Predicting the Performance of Technology-Assisted Learning in a Mathematics After-School Program (Jun Xie, Xudong Huang, Henry Hua, Jin Wang, Quan Tang, Scotty Craig, Arthur Graesser, King-Ip Lin and Xiangen Hu); (79) Using Item Response Theory to Rene Knowledge Tracing (Yanbo Xu and Jack Mostow); (80) Estimating the Benefits of Student Model Improvements on a Substantive Scale (Michael Yudelson and Kenneth Koedinger); (81) A Dynamic Group Composition Method to Refine Collaborative Learning Group Formation (Zhilin Zheng); (82) Educational Data Mining: Illuminating Student Learning Pathways in an Online Fraction Game (Ani Aghababyan, Taylor Martin, Nicole Forsgren Velasquez and Philip Janisiewicz); (83) Automatic Gaze-Based Detection of Mind Wandering during Reading (Sidney D'Mello, Jonathan Cobian and Matthew Hunter); (84) DARE: Deep Anaphora Resolution in Dialogue based Intelligent Tutoring Systems (Nobal B. Niraula, Vasile Rus and Dan Stefanescu); (85) Are You Committed? Investigating Interactions among Reading Commitment, Natural Language Input, and Students Learning Outcomes (Laura K. Varner, G. Tanner Jackson, Erica L. Snow and Danielle S. McNamara); (86) Using Multi-level Models to Assess Data From an Intelligent Tutoring System (Jennifer Weston and Danielle S. McNamara); (87) Evaluation of Automatically Generated Hint Feedback (Michael Eagle and Tiffany Barnes); (88) Analysing Engineering Expertise of High School Students Using Eye Tracking and Multimodal Learning Analytics (July Gomes, Mohamed Yassine, Marcelo Worsley and Paulo Blikstein); (89) Investigating the Efficacy of Algorithmic Student Modelling in Predicting Students at Risk of Failing in Tertiary Education (Geraldine Gray, Colm McGuinness and Philip Owende); (90) BOTS: Harnessing Player Data and Player Effort to Create and Evaluate Levels in a Serious Game (Andrew Hicks); (91) Helping Students Manage Personalized Learning Scenarios (Paul Salvador Inventado, Roberto Legaspi and Masayuki Numao); (92) Determining Problem Selection for a Logic Proof Tutor (Behrooz Mostafavi and Tiffany Barnes); (93) Demonstration of a Moodle Student Monitoring Web Application (Angela Bovo, Stephane Sanchez, Olivier Heguy and Yves Duthen); (94) Students Activity Visualization Tool (Marius Stefan Chiritoiu, Cristian Mihaescu and Dumitru Dan Burdescu); (95) FlexCCT: Software for Maximum Likelihood Cultural Consensus Theory (Stephen France, Mahyar Vaghe and William Batchelder); (96) Visual Exploration of Interactions and Performance with LeMo. (Agathe Merceron, Sebastian Schwarzrock, Margarita Elkina, Andreas Pursian, Liane Beuster, Albrecht Fortenbacher, Leonard Kappe and Boris Wenzla); (97) Project CASSI: A Social-Graph Based Tool for Classroom Behavior Analysis and Optimization (Robert Olson, Zachary Daily, John Malayny and Robert Szkutak); (98) A Moodle Block for Selecting, Visualizing and Mining Students' Usage Data (Cristobal Romero, Cristobal Castro and Sebastian Ventura); (99) SEMILAR: A Semantic Similarity Toolkit for Assessing Students' Natural Language Inputs (Vasile Rus, Rajendra Banjade, Mihai Lintean, Nobal Niraula and Dan Stefanescu); (100) Gathering Emotional Data from Multiple Sources (Sergio Salmeron-Majadas, Olga C. Santos, Jesus G. Boticario, Raul Cabestrero, Pilar Quiros and Mar Saneiro); and (101) A Tool for Speech Act Classification Using Interactive Machine Learning (Borhan Samei, Fazel Keshtkar and Arthur C. Graesser). Individual presentations contain references. [For "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)", see ED537074.].
The aim of this research is to develop light ontology of educational data mining (OntoEDM) and cased based reasoning recommender system (CBR-RS) to improve selection of educational data mining (EDM) techniques for prediction accuracy.
Category: Data mining
The aim of this research is to develop light ontology of educational data mining (OntoEDM) and cased based reasoning recommender system (CBR-RS) to improve selection of educational data mining (EDM) techniques for prediction accuracy.
Author: International Working Group on Educational Data MiningPublish On: 2010
The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus.
Author: International Working Group on Educational Data Mining
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  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.
33. de Baker, R.S.J., Yacef, K.: The state of educational data mining in 2009: a
review and future vision. J. Educ. Data Min. 1(1), 1–15 (2009) Peña-Ayala, A.,
Domínguez, R., Medel, J.: Educational data mining: a sample of review and study
Author: Mamdouh Alenezi
The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and machine learning researchers in Saudi Arabia, Gulf Cooperation Council (GCC) countries and the Middle East region. This book gathers the proceedings of the SDMA 2018. All papers were peer-reviewed based on a strict policy concerning the originality, significance to the area, scientific vigor and quality of the contribution, and address the following research areas.• Applications: Applications of data mining in domains including databases, social networks, web, bioinformatics, finance, healthcare, and security.• Algorithms: Data mining and machine learning foundations, algorithms, models, and theory.• Text Mining: Semantic analysis and mining text in Arabic, semi-structured, streaming, multimedia data.• Framework: Data mining frameworks, platforms and systems implementation.• Visualizations: Data visualization and modeling.
This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, ...
Author: Soni Sweta
Category: Technology & Engineering
This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system. Learning is a continuous process since human evolution. In fact, it is related to life and innovations. The basic objective of learning to grow, aspire and develop ease of life remains the same despite changes in the learning methodologies. Introduction of computers empowered us to attain new zenith in knowledge domain, developed pragmatic approach to solve life’s problem and helped us to decipher different hidden patterns of data to get new ideas. Of late, computers are predominantly used in education. Its process has been changed from offline to online in view of enhancing the ease of learning. With the advent of information technology, e-learning has taken centre stage in educational domain. In e-learning context, developing adaptive e-learning system is buzzword among contemporary research scholars in the area of Educational Data Mining (EDM). Enabling personalized systems is meant for improvement in learning experience for learners as per their choices made or auto-detected needs. It helps in enhancing their performance in terms of knowledge, skills, aptitudes and preferences. It also enables speeding up the learning process qualitatively and quantitatively. These objectives are met only by the Personalized Adaptive E-learning Systems in this regard. Many noble frameworks were conceptualized, designed and developed to infer learning style preferences, and accordingly, learning materials were delivered adaptively to the learners. Designing frameworks help to measure learners’ preferences minutely and provide adaptive learning materials to them in a way most appropriately.
The state of educational data mining in 2009: A review and future visions. journal
ofEducational Data Mining, 1(1), 3—17. Barth, R. S. (1974). Open education and
the American school. New York: Schocken Books. Battelle for Kids. (2009).
Author: Philip J. Piety
Publisher: Teachers College Press
For better or worse, many educational decisions that were once a private matter of teachers or administrators are now based on information technology. To be successful in this era, educators need to know how to use data successfully for their purposes and to understand the social forces at work. In this book, the author draws on his unique background in education policy and information systems to provide valuable insights into the education data movement. Using narratives of practice, the text discusses many current topics including value added modeling for teacher evaluation, big data and analytics, longitudinal data systems, open educational resources, and new designs for teaching.
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) ...
Author: Samira ElAtia
Publisher: John Wiley & Sons
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems.
Author: Mykola Pechenizkiy
Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for generating recommendations and advice to students, for improving management of learning objects, etc. However, most of the traditional data mining techniques focus on data dependencies or simple patterns and do not provide a visual representation of the complete educational (assessment) process ready to be analyzed. To allow for these types of analysis (in which the process plays the central role), a new line of data-mining research, called "process mining", has been initiated. Process mining focuses on the development of a set of intelligent tools and techniques aimed at extracting process-related knowledge from event logs recorded by an information system. In this paper we demonstrate the applicability of process mining, and the ProM framework in particular, to educational data mining context. We analyze assessment data from recently organized online multiple choice tests and demonstrate the use of process discovery, conformance checking and performance analysis techniques. (Contains 6 figures and 4 footnotes.) [Funding was provided by the Dutch Science Foundation (NWO). For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.].
An educational dashboard is a display which visualizes the results of educational data mining in a useful way. Educational data mining and visualization
techniques allow teachers and students to monitor and reflect on their online
Author: Guang Chen
Category: Social Science
This book provides an archival forum for researchers, academics, practitioners and industry professionals interested and/or engaged in the reform of the ways of teaching and learning through advancing current learning environments towards smart learning environments. The contributions of this book are submitted to the International Conference on Smart Learning Environments (ICSLE 2014). The focus of this proceeding is on the interplay of pedagogy, technology and their fusion towards the advancement of smart learning environments. Various components of this interplay include but are not limited to: Pedagogy- learning paradigms, assessment paradigms, social factors, policy; Technology- emerging technologies, innovative uses of mature technologies, adoption, usability, standards and emerging/new technological paradigms (open educational resources, cloud computing, etc.)
Keywords: Educational data mining, Classification, Bayesian network,
Assessment, Predictive model. 1 Introduction The advent of information
technology in various fields has lead the large volumes of data storage in various
formats like ...
Author: P. Venkata Krishna
This 2-Volume-Set, CCIS 0269-CCIS 0270, constitutes the refereed proceedings of the International Conference on Global Trends in Computing and Communication (CCIS 0269) and the International Conference on Global Trends in Information Systems and Software Applications (CCIS 0270), ObCom 2011, held in Vellore, India, in December 2011. The 173 full papers presented together with a keynote paper and invited papers were carefully reviewed and selected from 842 submissions. The conference addresses issues associated with computing, communication and information. Its aim is to increase exponentially the participants' awareness of the current and future direction in the domains and to create a platform between researchers, leading industry developers and end users to interrelate.
In order to achieve a decisional database, many steps need to be taken which are explained in this thesis. This work investigates the efficiency, scalability, maintenance and interoperability of data mining techniques.
Author: Saurabh Pal
Publisher: GRIN Verlag
Doctoral Thesis / Dissertation from the year 2014 in the subject Computer Science - General, , course: DOCTOR OF PHILOSOPHY, language: English, abstract: The primary objective of this research is to develop a process to accurately predict useful data from the huge amount of available data using data mining techniques. Data Mining is the process of finding treads, patterns and correlations between fields in large RDBMS. It permits users to analyse and study data from multiple dimensions and approaches, classify it, and summarize identified data relationships. Our focus in this thesis is to use education data mining procedures to understand higher education system data better which can help in improving efficiency and effectiveness of education. In order to achieve a decisional database, many steps need to be taken which are explained in this thesis. This work investigates the efficiency, scalability, maintenance and interoperability of data mining techniques. In this research work, data-results obtained through different data mining techniques have been compiled and analysed using variety of business intelligence tools to predict useful data. An effort has also been made to identify ways to implement this useful data efficiently in daily decision process in the field of higher education in India. Mining in educational environment is called Educational Data Mining. Han and Kamber describes data mining software that allow the users to analyze data from different dimensions, categorize it and Summarize the relationships which are identified during the mining process. New methods can be used to discover knowledge from educational databases. Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Information and communication technology puts its leg into the education sector to capture and compile low cost information. Now a day a new research community, educational data mining (EDM), is growing which is intersection of data mining and pedagogy. First chapter of the thesis elaborates the knowledge data discovery process, data mining concept, history and application of data mining in various industries.