Data mining and learning analytics : applications in educational research /
| Корпоративные авторы: | , |
|---|---|
| Другие авторы: | , , , , |
| Формат: | |
| Язык: | English |
| Опубликовано: |
Somerset :
Wiley,
2016
Hoboken, New Jersey : 2016 Somerset : 2016 |
| Серии: | Wiley series on methods and applications in data mining
|
| Предметы: |
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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 |
| 260 | |a Somerset : |b Wiley, |c 2016 | ||
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c 2016 | |
| 264 | 1 | |a Somerset : |b Wiley, |c 2016 | |
| 264 | 4 | |c ©2016 | |
| 300 | |a 1 online resource (314 pages) : |b illustrations, tables | ||
| 300 | |a 1 online resource (314 pages) | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Wiley Series on Methods and Applications in Data Mining | |
| 500 | |a 8.1 REVIEW OF&#x;LITERATURE | ||
| 500 | |a 8.1 REVIEW OF&LITERATURE | ||
| 504 | |a Includes bibliographical references at the end od each chapters and index | ||
| 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 CASE&#x;STUDY USING MOODLE DATA SETS; 1.1 BACKGROUND; 1.2 DATA DESCRIPTION AND&#x;PREPARATION; 1.2.1 Preprocessing Log Data; 1.2.2 Clustering Approach for&#x;Grouping Log Data; 1.3 WORKING WITH&#x;ProM; 1.3.1 Discovered Models; 1.3.2 Analysis of&#x;the&#x;Models' Performance; 1.4 CONCLUSION; ACKNOWLEdGMENTS; REFERENCES; CHAPTER 2 ON BIG DATA AND&#x;TEXT MINING IN&#x;THE&#x;HUMANITIES | |
| 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 CASE&STUDY USING MOODLE DATA SETS; 1.1 BACKGROUND; 1.2 DATA DESCRIPTION AND&PREPARATION; 1.2.1 Preprocessing Log Data; 1.2.2 Clustering Approach for&Grouping Log Data; 1.3 WORKING WITH&ProM; 1.3.1 Discovered Models; 1.3.2 Analysis of&the&Models' Performance; 1.4 CONCLUSION; ACKNOWLEdGMENTS; REFERENCES; CHAPTER 2 ON BIG DATA AND&TEXT MINING IN&THE&HUMANITIES | |
| 505 | 8 | |a 2.1 BUSA AND&#x;THE&#x;DIGITAL TEXT2.2 THESAURUS LINGUAE GRAECAE AND&#x;THE&#x;IBYCUS COMPUTER AS&#x;INFRASTRUCTURE; 2.2.1 Complete Data Sets; 2.3 COOKING WITH&#x;STATISTICS; 2.4 CONCLUSIONS; REFERENCES; CHAPTER 3 FINDING PREDICTORS IN&#x;HIGHER EDUCATION; 3.1 CONTRASTING TRADITIONAL AND COMPUTATIONAL METHODS; 3.2 PREDICTORS AND&#x;DATA EXPLORATION; 3.3 DATA MINING APPLICATION: AN&#x;EXAMPLE; 3.4 CONCLUSIONS; REFERENCES; CHAPTER 4 EDUCATIONAL DATA MINING: A&#x;MOOC EXPERIENCE; 4.1 BIG DATA IN&#x;EDUCATION: THE&#x;COURSE; 4.1.1 Iteration 1: Coursera; 4.1.2 Iteration 2: edX; 4.2 COGNITIVE TUTOR AUTHORING TOOLS; 4.3 BAZAAR | |
| 505 | 8 | |a 2.1 BUSA AND&THE&DIGITAL TEXT2.2 THESAURUS LINGUAE GRAECAE AND&THE&IBYCUS COMPUTER AS&INFRASTRUCTURE; 2.2.1 Complete Data Sets; 2.3 COOKING WITH&STATISTICS; 2.4 CONCLUSIONS; REFERENCES; CHAPTER 3 FINDING PREDICTORS IN&HIGHER EDUCATION; 3.1 CONTRASTING TRADITIONAL AND COMPUTATIONAL METHODS; 3.2 PREDICTORS AND&DATA EXPLORATION; 3.3 DATA MINING APPLICATION: AN&EXAMPLE; 3.4 CONCLUSIONS; REFERENCES; CHAPTER 4 EDUCATIONAL DATA MINING: A&MOOC EXPERIENCE; 4.1 BIG DATA IN&EDUCATION: THE&COURSE; 4.1.1 Iteration 1: Coursera; 4.1.2 Iteration 2: edX; 4.2 COGNITIVE TUTOR AUTHORING TOOLS; 4.3 BAZAAR | |
| 505 | 8 | |a 4.4 WALKTHROUGH4.4.1 Course Content; 4.4.2 Research on&#x;BDEMOOC; 4.5 CONCLUSION; ACKNOWLEDGMENTS; REFERENCES; CHAPTER 5 DATA MINING AND ACTION RESEARCH; 5.1 PROCESS; 5.2 DESIGN METHODOLOGY; 5.3 ANALYSIS AND&#x;INTERPRETATION OF&#x;DATA; 5.3.1 Quantitative Data Analysis and&#x;Interpretation; 5.3.2 Qualitative Data Analysis and&#x;Interpretation; 5.4 CHALLENGES; 5.5 ETHICS; 5.6 ROLE OF&#x;ADMINISTRATION IN&#x;THE&#x;DATA COLLECTION PROCESS; 5.7 CONCLUSION; REFERENCES; PART II PEDAGOGICAL APPLICATIONS OF EDM ; CHAPTER 6 DESIGN OF&#x;AN&#x;ADAPTIVE LEARNING SYSTEM AND&#x;EDUCATIONAL DATA&#x;MINING | |
| 505 | 8 | |a 4.4 WALKTHROUGH4.4.1 Course Content; 4.4.2 Research on&BDEMOOC; 4.5 CONCLUSION; ACKNOWLEDGMENTS; REFERENCES; CHAPTER 5 DATA MINING AND ACTION RESEARCH; 5.1 PROCESS; 5.2 DESIGN METHODOLOGY; 5.3 ANALYSIS AND&INTERPRETATION OF&DATA; 5.3.1 Quantitative Data Analysis and&Interpretation; 5.3.2 Qualitative Data Analysis and&Interpretation; 5.4 CHALLENGES; 5.5 ETHICS; 5.6 ROLE OF&ADMINISTRATION IN&THE&DATA COLLECTION PROCESS; 5.7 CONCLUSION; REFERENCES; PART II PEDAGOGICAL APPLICATIONS OF EDM ; CHAPTER 6 DESIGN OF&AN&ADAPTIVE LEARNING SYSTEM AND&EDUCATIONAL DATA&MINING | |
| 505 | 8 | |a 6.1 DIMENSIONALITIES OF&#x;THE&#x;USER MODEL IN&#x;ALS6.2 COLLECTING DATA FOR&#x;ALS; 6.3 DATA MINING IN&#x;ALS; 6.3.1 Data Mining for&#x;User Modeling; 6.3.2 Data Mining for&#x;Knowledge Discovery; 6.4 ALS MODEL AND&#x;FUNCTION ANALYZING; 6.4.1 Introduction of&#x;Module Functions; 6.4.2 Analyzing the&#x;Workflow; 6.5 FUTURE WORKS; 6.6 CONCLUSIONS; ACKNOWLEDGMENT; REFERENCES; CHAPTER 7 THE "GEOMETRY" OF NAÏVE&#x;BAYES: TEACHING PROBABILITIES BY "DRAWING"&#x;THEM; 7.1 INTRODUCTION; 7.1.1 Main Contribution; 7.1.2 Related Works; 7.2 THE GEOMETRY OF&#x;NB CLASSIFICATION; 7.2.1 Mathematical Notation; 7.2.2 Bayesian Decision Theory | |
| 505 | 8 | |a 6.1 DIMENSIONALITIES OF&THE&USER MODEL IN&ALS6.2 COLLECTING DATA FOR&ALS; 6.3 DATA MINING IN&ALS; 6.3.1 Data Mining for&User Modeling; 6.3.2 Data Mining for&Knowledge Discovery; 6.4 ALS MODEL AND&FUNCTION ANALYZING; 6.4.1 Introduction of&Module Functions; 6.4.2 Analyzing the&Workflow; 6.5 FUTURE WORKS; 6.6 CONCLUSIONS; ACKNOWLEDGMENT; REFERENCES; CHAPTER 7 THE "GEOMETRY" OF NAÏVE&BAYES: TEACHING PROBABILITIES BY "DRAWING"&THEM; 7.1 INTRODUCTION; 7.1.1 Main Contribution; 7.1.2 Related Works; 7.2 THE GEOMETRY OF&NB CLASSIFICATION; 7.2.1 Mathematical Notation; 7.2.2 Bayesian Decision Theory | |
| 505 | 8 | |a 7.3 TWO-DIMENSIONAL PROBABILITIES7.3.1 Working with&#x;Likelihoods and&#x;Priors Only; 7.3.2 De-normalizing Probabilities ; 7.3.3 NB Approach; 7.3.4 Bernoulli Naïve Bayes; 7.4 A NEW DECISION LINE: FAR FROM&#x;THE&#x;ORIGIN; 7.4.1 De-normalization Makes (Some) Problems Linearly Separable ; 7.5 LIKELIHOOD SPACES, WHEN LOGARITHMS MAKE A&#x;DIFFERENCE (OR A&#x;SUM); 7.5.1 De-normalization Makes (Some) Problems Linearly Separable ; 7.5.2 A New Decision in&#x;Likelihood Spaces; 7.5.3 A Real Case Scenario: Text Categorization; 7.6 FINAL REMARKS; REFERENCES; CHAPTER 8 EXAMINING THE&#x;LEARNING NETWORKS OF&#x;A&#x;MOOC | |
| 505 | 8 | |a 7.3 TWO-DIMENSIONAL PROBABILITIES7.3.1 Working with&Likelihoods and&Priors Only; 7.3.2 De-normalizing Probabilities ; 7.3.3 NB Approach; 7.3.4 Bernoulli Naïve Bayes; 7.4 A NEW DECISION LINE: FAR FROM&THE&ORIGIN; 7.4.1 De-normalization Makes (Some) Problems Linearly Separable ; 7.5 LIKELIHOOD SPACES, WHEN LOGARITHMS MAKE A&DIFFERENCE (OR A&SUM); 7.5.1 De-normalization Makes (Some) Problems Linearly Separable ; 7.5.2 A New Decision in&Likelihood Spaces; 7.5.3 A Real Case Scenario: Text Categorization; 7.6 FINAL REMARKS; REFERENCES; CHAPTER 8 EXAMINING THE&LEARNING NETWORKS OF&A&MOOC | |
| 506 | |a Access restricted by licensing agreement | ||
| 506 | |a Restricted for use by site license | ||
| 533 | |a Electronic reproduction |b Hoboken, N.J. |n Available via World Wide Web. | ||
| 588 | |a Description based on print version record | ||
| 588 | |a Description based upon print version of record | ||
| 590 | |a Access is available to the Yale community | ||
| 650 | 0 | |a Data mining | |
| 650 | 0 | |a Education |x Research |x Statistical methods | |
| 650 | 0 | |a Educational statistics |x Data processing | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Education |x Research |2 fast | |
| 650 | 7 | |a Education |x Research |x Statistical methods |2 fast | |
| 650 | 7 | |a Educational statistics |2 fast | |
| 650 | 7 | |a Educational statistics |x Data processing |2 fast | |
| 700 | 1 | |a ElAtia, Samira, |d 1973- |1 http://viaf.org/viaf/119146094336100332516 | |
| 700 | 1 | |a ElAtia, Samira, |d 1973- |e editor | |
| 700 | 1 | |a ElAtia, Samira, |d 1973- | |
| 700 | 1 | |a Ipperciel, Donald, |d 1967- |1 http://viaf.org/viaf/74014042 | |
| 700 | 1 | |a Ipperciel, Donald, |d 1967- |e editor | |
| 700 | 1 | |a Ipperciel, Donald, |d 1967- | |
| 700 | 1 | |a Zaiane, Osmar R., |d 1965- |e editor | |
| 700 | 1 | |a Zaïane, Osmar | |
| 700 | 1 | |a Za�ane, Osmar |d 1965- | |
| 710 | 2 | |a ProQuest (Firm) | |
| 710 | 2 | |a Wiley InterScience (Online service) | |
| 776 | 0 | 8 | |i Print version: |a ElAtia, Samira |t Data Mining and Learning Analytics : Applications in Educational Research |d Somerset : Wiley,c2016 |z 9781118998236 |
| 776 | 0 | 8 | |i Print version: |t Data mining and learning analytics : applications in educational research |d Hoboken, New Jersey : Wiley, c2016 |h xxviii, 283 pages |k Wiley series on methods and applications in data mining. |z 9781118998236 |w 2016016549 |
| 797 | 2 | |a ProQuest (Firm) | |
| 830 | 0 | |a Wiley series on methods and applications in data mining | |
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