Educational Data Mining

TRAMA

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 knowledgeand find targets for future work in the field of educational data mining.

SOMMARIO

Part I: Profile1 Which Contribution Does EDM Provide to Computer Based Learning Environments?    Nabila Bousbia, Idriss Belamri2 A Survey on Pre-processing Educational Data    Cristóbal Romero, José Raúl Romero, Sebastián Ventura3 How Educational Data Mining Empowers Government Policies to Re-form Education: The Mexican Case Study    Alejandro Peña-Ayala, Leonor Cárdenas Part II: Student Modeling4 Modeling Student Performance in Higher Education Using Data Mining    Huseyin Guruler, Ayhan Istanbullu5 Using Data Mining Techniques to Detect the Personality of Players in an Educational Game    Fazel Keshtkar, Candice Burkett, Haiying Li, Arthur C. Graesser6 Students’ Performance Prediction using Multi-Channel Decision Fusion    H. Moradi, S. Abbas Moradi, L. Kashani7 Predicting Student Performance from Combined Data Sources    Annika Wolff, Zdenek Zdrahal, Drahomira Herrmannova, Petr Knoth8 Predicting Learner Answers Correctness Through Eye Movements With Random Forest    Alper Bayazit, Petek Askar, Erdal Cosgun Part III: Assessment9 Mining Domain Knowledge for CoherenceAssessment of Students Proposal Drafts    Samuel González López, Aurelio López-López10 Adaptive Testing in Programming Courses Based on Educational Data Mining Techniques     Vladimir Ivancevic, Marko Kneževic, Bojan Pušic, Ivan Lukovic11 Plan Recognition and Visualization in Exploratory Learning Environments      Ofra Amir, Kobi Gal, David Yaron, Michael Karabinos, Robert Bel-ford12 Dependency of Test Items from Students' Response Data      Xiaoxun Sun Part IV : Trends13 Mining Texts, Learner Productions and Strategies with ReaderBench      Mihai Dascalu, Philippe Dessus, Maryse Bianco, Stefan Trausan-Matu, Aurélie Nardy14 Maximizing the Value of Student Ratings Through Data Mining      Kathryn Gates, Dawn Wilkins, Sumali Conlon, Susan Mossing, Mau-rice Eftink15 Data Mining and Social Network Analysis in the Educational Field: An Application for Non-expert Users      Diego García-Saiz, Camilo Palazuelos, Marta Zorrilla16 Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective      Reihaneh Rabbany, Samira ElAtia, Mansoureh Takaffoli, Osmar R. Zaïane

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783319027371
• Collana: Studies in Computational Intelligence
• Dimensioni: 235 x 155 mm
• Formato: Copertina rigida
• Illustration Notes: XVIII, 468 p. 139 illus.
• Pagine Arabe: 468
• Pagine Romane: xviii