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Educational Data Mining And Its Uses To Predict The Most Prosperous Learning Environment
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Book Synopsis EDUCATIONAL DATA MINING AND ITS USES TO PREDICT THE MOST PROSPEROUS LEARNING ENVIRONMENT. by : Lewis Whitley
Download or read book EDUCATIONAL DATA MINING AND ITS USES TO PREDICT THE MOST PROSPEROUS LEARNING ENVIRONMENT. written by Lewis Whitley and published by . This book was released on 2018 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of technology and data analysis within the classroom has been a resourceful tool in order to collect, study, and compare a student's level of success. With the large amount of regularly collected data from student behaviors, and course structure there is more than enough resources in order to find student success with data analysis. A method of data analysis within a learning environment is called Educational Data Mining (EDM), which has proven to be an emerging trend when it involves the development of exploration techniques and the analysis of educational data. EDM has been able to contribute to the understanding of student behavior, as well as factors that influence both student actions and their success. The study of student success within EDM has focused on student learning patterns, student to teacher culture, and teaching techniques. In this research we will look at uses of technology and data mining in an EDM setting and compare the success of findings. Using past experience of other research we will determine which method would be best in order to look at a learning environment, and try to find which factors will affect a student's academic performance.
Book Synopsis Data Mining and Learning Analytics by : Samira ElAtia
Download or read book Data Mining and Learning Analytics written by Samira ElAtia and published by John Wiley & Sons. This book was released on 2016-09-20 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Educational Data Mining by : Alejandro Peña-Ayala
Download or read book Educational Data Mining written by Alejandro Peña-Ayala and published by Springer. This book was released on 2013-11-08 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Educational Data Mining by : Cristobal Romero
Download or read book Handbook of Educational Data Mining written by Cristobal Romero and published by CRC Press. This book was released on 2010-10-25 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook 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. With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It helps education experts understand what types of questions EDM can address and helps data miners understand what types of questions are important to educational design and educational decision making.
Book Synopsis Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities by : Bhatt, Chintan
Download or read book Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities written by Bhatt, Chintan and published by IGI Global. This book was released on 2019-08-02 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Mining and Learning Analytics by : Samira ElAtia
Download or read book Data Mining and Learning Analytics written by Samira ElAtia and published by John Wiley & Sons. This book was released on 2016-09-26 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Educational Data Mining with R and Rattle by : R.S. Kamath
Download or read book Educational Data Mining with R and Rattle written by R.S. Kamath and published by CRC Press. This book was released on 2022-09-01 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. EDM is a promising discipline which has an imperative impact on predicting students' academic performance. It includes the transformation of existing, and the innovation of new approaches derived from multidisciplinary spheres of influence such as statistics, machine learning, psychometrics, scientific computing etc.An archetype that is covered in this book is that of learning by example. The intention is that reader will easily be able to replicate the given examples and then adapt them to suit their own needs of teaching-learning. The content of the book is based on the research work undertaken by the authors on the theme "Mining of Educational Data for the Analysis and Prediction of Students' Academic Performance". The basic know-how presented in this book can be treated as guide for educational data mining implementation using R and Rattle open source data mining tools. .Technical topics discussed in the book include:• Emerging Research Directions in Educational Data Mining• Design Aspects and Developmental Framework of the System• Model Development - Building Classifiers• Educational Data Analysis: Clustering Approach
Book Synopsis Educational Data Science by : Alejandro Peña-Ayala
Download or read book Educational Data Science written by Alejandro Peña-Ayala and published by Springer Nature. This book was released on 2023 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well-sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning!
Book Synopsis Advancing the Power of Learning Analytics and Big Data in Education by : Azevedo, Ana
Download or read book Advancing the Power of Learning Analytics and Big Data in Education written by Azevedo, Ana and published by IGI Global. This book was released on 2021-03-19 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: The term learning analytics is used in the context of the use of analytics in e-learning environments. Learning analytics is used to improve quality. It uses data about students and their activities to provide better understanding and to improve student learning. The use of learning management systems, where the activity of the students can be easily accessed, potentiated the use of learning analytics to understand their route during the learning process, help students be aware of their progress, and detect situations where students can give up the course before its completion, which is a growing problem in e-learning environments. Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors. This book is ideal for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in the use of big data and learning analytics for improved student success and educational environments.
Book Synopsis Data Mining in E-learning by : Cristobal Romero
Download or read book Data Mining in E-learning written by Cristobal Romero and published by WIT Press. This book was released on 2006 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of e-learning systems, particularly, web-based education systems, has increased exponentially in recent years. Following this line, one of the most promising areas is the application of knowledge extraction. As one of the first of its kind, this book presents an introduction to e-learning systems, data mining concepts and the interaction between both areas.
Book Synopsis Linking Competence to Opportunities to Learn by : Xiufeng Liu
Download or read book Linking Competence to Opportunities to Learn written by Xiufeng Liu and published by Springer Science & Business Media. This book was released on 2009-05-01 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many people, a high standard for student learning is desirable. This is what underlies current standard-based science education reforms around the world. As someone who was born and brought up in a less-privileged home and educated in a resource-limited school environment in a developing country, I always had to study hard to meet various standards from elementary to high school to univ- sity. My first book in English published over 10 years ago (Liu, X. [1996]. Mathematics and Science Curriculum Change in the People’s Republic of China. Lewiston, NY: The Edwin Mellen Press) provided me an opportunity to examine standards (i. e. , Chinese national science teaching syllabi) from a historical and political point of view. I argued that standards are developed for particular poli- cal agendas in order to maintain the privileged position of certain groups (i. e. , urban residents) in a society at expenses of others (i. e. , rural residents). Thus, underneath standards is systematic discrimination and injustice. Since then, I have had opportunities to study the issue of standards in much more breadth and depth. This book, Linking Competence to Opportunities to Learn: Models of Competence and data mining, provides me an opportunity to examine standards from a different perspective: opportunity to learn.
Download or read book Web Data Mining written by Bing Liu and published by Springer Science & Business Media. This book was released on 2011-06-25 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Book Synopsis AI and education by : Miao, Fengchun
Download or read book AI and education written by Miao, Fengchun and published by UNESCO Publishing. This book was released on 2021-04-08 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]
Book Synopsis Applications of Big Data Analytics by : Mohammed M. Alani
Download or read book Applications of Big Data Analytics written by Mohammed M. Alani and published by Springer. This book was released on 2019-02-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.
Book Synopsis Data Analytics in e-Learning: Approaches and Applications by : Marian Cristian Mihăescu
Download or read book Data Analytics in e-Learning: Approaches and Applications written by Marian Cristian Mihăescu and published by Springer Nature. This book was released on 2022-03-22 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
Book Synopsis Emergence and Innovation in Digital Learning by : George Veletsianos
Download or read book Emergence and Innovation in Digital Learning written by George Veletsianos and published by Athabasca University Press. This book was released on 2016-06-01 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational systems worldwide are facing an enormous shift as a result of sociocultural, political, economic, and technological changes. The technologies and practices that have developed over the last decade have been heralded as opportunities to transform both online and traditional education systems. While proponents of these new ideas often postulate that they have the potential to address the educational problems facing both students and institutions and that they could provide an opportunity to rethink the ways that education is organized and enacted, there is little evidence of emerging technologies and practices in use in online education. Because researchers and practitioners interested in these possibilities often reside in various disciplines and academic departments the sharing and dissemination of their work across often rigid boundaries is a formidable task. Contributors to Emergence and Innovation in Digital Learning include individuals who are shaping the future of online learning with their innovative applications and investigations on the impact of issues such as openness, analytics, MOOCs, and social media. Building on work first published in Emerging Technologies in Distance Education, the contributors to this collection harness the dispersed knowledge in online education to provide a one-stop locale for work on emergent approaches in the field. Their conclusions will influence the adoption and success of these approaches to education and will enable researchers and practitioners to conceptualize, critique, and enhance their understanding of the foundations and applications of new technologies.
Book Synopsis Proceedings of the International Conference on Educational Data Mining(Edm) (5Th, Chania, Greece, June 19-21, 2012). by : International Educational Data Mining Society
Download or read book Proceedings of the International Conference on Educational Data Mining(Edm) (5Th, Chania, Greece, June 19-21, 2012). written by International Educational Data Mining Society and published by . This book was released on 2012 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.].
