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Data mining and learning analytics : applications in educational research /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2017]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Data mining and learning analytics : |b applications in educational research / |c edited by Samira ElAtia, Donald Ipperciel, Osmar R. Zaiane. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2017] | |
| 300 | |a 1 online resource (314 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Education |x Research |x Statistical methods. | |
| 650 | 0 | |a Educational statistics |x Data processing. | |
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Statistique de l'Đeducation |x Informatique. | |
| 650 | 6 | |a Exploration de donnĐees (Informatique) | |
| 650 | 7 | |a EDUCATION |x Research. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Databases |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Education |x Research |x Statistical methods |2 fast | |
| 650 | 7 | |a Educational statistics |x Data processing |2 fast | |
| 653 | |a Learning analytics. | ||
| 700 | 1 | |a ElAtia, Samira, |d 1973- |1 https://id.oclc.org/worldcat/entity/E39PCjFdcy7c4ghVq9wyMwHWcP | |
| 700 | 1 | |a Ipperciel, Donald, |d 1967- |1 https://id.oclc.org/worldcat/entity/E39PBJvHbJ8JhTHRkKq99HjKVC | |
| 700 | 1 | |a ZaŁiane, Osmar. | |
| 758 | |i has work: |a Data mining and learning analytics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFR6wJbJRG3mkMmhg89TBP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a ElAtia, Samira. |t Data Mining and Learning Analytics : Applications in Educational Research. |d Somerset : Wiley, Ã2016 |z 9781118998236 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4675499 |z Texto completo |
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