Data mining and learning analytics : applications in educational research /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: ElAtia, Samira, 1973-, Ipperciel, Donald, 1967-, ZaŁiane, Osmar
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc., [2017]
Temas:
Education > Research > Statistical methods.
Educational statistics > Data processing.
Data mining.
Data Mining
Statistique de l'Đeducation > Informatique.
Exploration de donnĐees (Informatique)
EDUCATION > Research.
COMPUTERS > Databases > Data Mining.
Data mining
Education > Research > Statistical methods
Educational statistics > Data processing
Learning analytics.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • TITLE PAGE ; COPYRIGHT PAGE ; CONTENTS; NOTES ON CONTRIBUTORS; INTRODUCTION: EDUCATION AT COMPUTATIONAL CROSSROADS; PART I AT THE INTERSECTION OF TWO FIELDS: EDM ; CHAPTER 1 EDUCATIONAL PROCESS MINING: A TUTORIAL AND CASEÈSTUDY USING MOODLE DATA SETS; 1.1 BACKGROUND; 1.2 DATA DESCRIPTION ANDÈPREPARATION; 1.2.1 Preprocessing Log Data; 1.2.2 Clustering Approach forÈGrouping Log Data; 1.3 WORKING WITHÈProM; 1.3.1 Discovered Models; 1.3.2 Analysis ofẗheÈModels' Performance; 1.4 CONCLUSION; ACKNOWLEdGMENTS; REFERENCES; CHAPTER 2 ON BIG DATA ANDÈTEXT MINING INÈTHEḦUMANITIES; 2.1 BUSA ANDÈTHEÈDIGITAL TEXT2.2 THESAURUS LINGUAE GRAECAE ANDÈTHEÏBYCUS COMPUTER ASÏNFRASTRUCTURE; 2.2.1 Complete Data Sets; 2.3 COOKING WITHÈSTATISTICS; 2.4 CONCLUSIONS; REFERENCES.
  • CHAPTER 3 FINDING PREDICTORS INḦIGHER EDUCATION; 3.1 CONTRASTING TRADITIONAL AND COMPUTATIONAL METHODS; 3.2 PREDICTORS ANDÈDATA EXPLORATION; 3.3 DATA MINING APPLICATION: ANËXAMPLE; 3.4 CONCLUSIONS; REFERENCES; CHAPTER 4 EDUCATIONAL DATA MINING: AÈMOOC EXPERIENCE; 4.1 BIG DATA INËDUCATION: THEÈCOURSE; 4.1.1 Iteration 1: Coursera; 4.1.2 Iteration 2: edX; 4.2 COGNITIVE TUTOR AUTHORING TOOLS; 4.3 BAZAAR; 4.4 WALKTHROUGH4.4.1 Course Content; 4.4.2 Research onÈBDEMOOC; 4.5 CONCLUSION; ACKNOWLEDGMENTS; REFERENCES.
  • CHAPTER 5 DATA MINING AND ACTION RESEARCH; 5.1 PROCESS; 5.2 DESIGN METHODOLOGY; 5.3 ANALYSIS ANDÏNTERPRETATION OFÈDATA; 5.3.1 Quantitative Data Analysis andÏnterpretation; 5.3.2 Qualitative Data Analysis andÏnterpretation; 5.4 CHALLENGES; 5.5 ETHICS; 5.6 ROLE OFÄDMINISTRATION INÈTHEÈDATA COLLECTION PROCESS; 5.7 CONCLUSION; REFERENCES; PART II PEDAGOGICAL APPLICATIONS OF EDM ; CHAPTER 6 DESIGN OFÄNÄDAPTIVE LEARNING SYSTEM ANDËDUCATIONAL DATAÈMINING; 6.1 DIMENSIONALITIES OFÈTHEÜSER MODEL INÄLS6.2 COLLECTING DATA FORÄLS; 6.3 DATA MINING INÄLS; 6.3.1 Data Mining forÜser Modeling; 6.3.2 Data Mining forÈKnowledge Discovery; 6.4 ALS MODEL ANDÈFUNCTION ANALYZING; 6.4.1 Introduction ofÈModule Functions; 6.4.2 Analyzing theẄorkflow; 6.5 FUTURE WORKS; 6.6 CONCLUSIONS; ACKNOWLEDGMENT; REFERENCES.
  • CHAPTER 7 THE "GEOMETRY" OF NAŁIVEÈBAYES: TEACHING PROBABILITIES BY "DRAWING"ÈTHEM; 7.1 INTRODUCTION; 7.1.1 Main Contribution; 7.1.2 Related Works; 7.2 THE GEOMETRY OFÈNB CLASSIFICATION; 7.2.1 Mathematical Notation; 7.2.2 Bayesian Decision Theory; 7.3 TWO-DIMENSIONAL PROBABILITIES7.3.1 Working withÈLikelihoods andÈPriors Only; 7.3.2 De-normalizing Probabilities ; 7.3.3 NB Approach; 7.3.4 Bernoulli NaŁive Bayes; 7.4 A NEW DECISION LINE: FAR FROMÈTHEÖRIGIN; 7.4.1 De-normalization Makes (Some) Problems Linearly Separable ; 7.5 LIKELIHOOD SPACES, WHEN LOGARITHMS MAKE AÈDIFFERENCE (OR AÈSUM); 7.5.1 De-normalization Makes (Some) Problems Linearly Separable ; 7.5.2 A New Decision inÈLikelihood Spaces; 7.5.3 A Real Case Scenario: Text Categorization; 7.6 FINAL REMARKS; REFERENCES; CHAPTER 8 EXAMINING THEÈLEARNING NETWORKS OFÄÈMOOC; 8.1 REVIEW OFÈLITERATURE.

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