Methods for analyzing and leveraging online learning data:
"This book helps lay the groundwork for how to use the large amounts of available data from the myriad online learning systems. Its approach of looking at online learning data will advance the field of online teaching, learning, and curriculum design"--Provided by publisher.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
[2019]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "This book helps lay the groundwork for how to use the large amounts of available data from the myriad online learning systems. Its approach of looking at online learning data will advance the field of online teaching, learning, and curriculum design"--Provided by publisher. |
| Beschreibung: | 31 PDFs (xviii, 436 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781522575290 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
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| 505 | 0 | |a Section 1. Understanding the states of online learning systems. Chapter 1. Reading data possibilities from an LMS data portal data dictionary ; Chapter 2. Five academic years of activated third-party and custom-coded applications on an LMS instance -- Section 2. Teaching and learning design. Chapter 3. Exploring the common structures and sequences of real-world online learning modules ; Chapter 4. "Conceptual reverse engineering" of online learning objects and sequences for practical applications ; Chapter 5. Improving teaching and learning from high-level and close-in features of assignments and assessments in an LMS instance ; Chapter 6. Creating and analyzing induced decision trees from online learning data ; Chapter 7. Using social image sets to explore virtual embodiment in second life as indicators of formal, nonformal, and informal learning -- Section 3. On open learning online. Chapter 8. Peripheral vision: engaging multimodal social media datasets to differentiate MOOC platforms by course offerings and user bases ; Chapter 9. Datafication of the "e-learning faculty modules" for next steps ; Chapter 10. Visual senses of "online learning" and "instructional design": social imagery as online learning data ; Chapter 11. Using article networks on Wikipedia to explore public understandings of academic domains and address observed gaps -- Section 4. Profiling learners, profiling teachers. Chapter 12. Computational text analysis of the C2C digital magazine: using distant reading to understand the authors, organizational interests, and related professionals -- Section 5. Measuring time. Chapter 13. Highlights from extracted eras of a live LMS instance ; Chapter 14. Basic time-to-event analyses of online educational data -- Section 6. Improving instrumentation. Chapter 15. An exploratory factor analysis of an open-access virtual "privilege walk" instrument ; Chapter 16. Assessing practical accessibility in online courses based on local conditions. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "This book helps lay the groundwork for how to use the large amounts of available data from the myriad online learning systems. Its approach of looking at online learning data will advance the field of online teaching, learning, and curriculum design"--Provided by publisher. | |
| 530 | |a Also available in print. | ||
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| 650 | 0 | |a Internet in education |x Management. | |
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| contents | Section 1. Understanding the states of online learning systems. Chapter 1. Reading data possibilities from an LMS data portal data dictionary ; Chapter 2. Five academic years of activated third-party and custom-coded applications on an LMS instance -- Section 2. Teaching and learning design. Chapter 3. Exploring the common structures and sequences of real-world online learning modules ; Chapter 4. "Conceptual reverse engineering" of online learning objects and sequences for practical applications ; Chapter 5. Improving teaching and learning from high-level and close-in features of assignments and assessments in an LMS instance ; Chapter 6. Creating and analyzing induced decision trees from online learning data ; Chapter 7. Using social image sets to explore virtual embodiment in second life as indicators of formal, nonformal, and informal learning -- Section 3. On open learning online. Chapter 8. Peripheral vision: engaging multimodal social media datasets to differentiate MOOC platforms by course offerings and user bases ; Chapter 9. Datafication of the "e-learning faculty modules" for next steps ; Chapter 10. Visual senses of "online learning" and "instructional design": social imagery as online learning data ; Chapter 11. Using article networks on Wikipedia to explore public understandings of academic domains and address observed gaps -- Section 4. Profiling learners, profiling teachers. Chapter 12. Computational text analysis of the C2C digital magazine: using distant reading to understand the authors, organizational interests, and related professionals -- Section 5. Measuring time. Chapter 13. Highlights from extracted eras of a live LMS instance ; Chapter 14. Basic time-to-event analyses of online educational data -- Section 6. Improving instrumentation. Chapter 15. An exploratory factor analysis of an open-access virtual "privilege walk" instrument ; Chapter 16. Assessing practical accessibility in online courses based on local conditions. |
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| spelling | Hai-Jew, Shalin, author. Methods for analyzing and leveraging online learning data by Shalin Hai-Jew. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, [2019] 31 PDFs (xviii, 436 pages) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Section 1. Understanding the states of online learning systems. Chapter 1. Reading data possibilities from an LMS data portal data dictionary ; Chapter 2. Five academic years of activated third-party and custom-coded applications on an LMS instance -- Section 2. Teaching and learning design. Chapter 3. Exploring the common structures and sequences of real-world online learning modules ; Chapter 4. "Conceptual reverse engineering" of online learning objects and sequences for practical applications ; Chapter 5. Improving teaching and learning from high-level and close-in features of assignments and assessments in an LMS instance ; Chapter 6. Creating and analyzing induced decision trees from online learning data ; Chapter 7. Using social image sets to explore virtual embodiment in second life as indicators of formal, nonformal, and informal learning -- Section 3. On open learning online. Chapter 8. Peripheral vision: engaging multimodal social media datasets to differentiate MOOC platforms by course offerings and user bases ; Chapter 9. Datafication of the "e-learning faculty modules" for next steps ; Chapter 10. Visual senses of "online learning" and "instructional design": social imagery as online learning data ; Chapter 11. Using article networks on Wikipedia to explore public understandings of academic domains and address observed gaps -- Section 4. Profiling learners, profiling teachers. Chapter 12. Computational text analysis of the C2C digital magazine: using distant reading to understand the authors, organizational interests, and related professionals -- Section 5. Measuring time. Chapter 13. Highlights from extracted eras of a live LMS instance ; Chapter 14. Basic time-to-event analyses of online educational data -- Section 6. Improving instrumentation. Chapter 15. An exploratory factor analysis of an open-access virtual "privilege walk" instrument ; Chapter 16. Assessing practical accessibility in online courses based on local conditions. Restricted to subscribers or individual electronic text purchasers. "This book helps lay the groundwork for how to use the large amounts of available data from the myriad online learning systems. Its approach of looking at online learning data will advance the field of online teaching, learning, and curriculum design"--Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 10/18/2018). Educational statistics Data processing. Internet in education Management. IGI Global, publisher. (Original) (DLC)2018029251 Print version: 1522575286 9781522575283 (DLC) 2018029251 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7528-3 Volltext |
| spellingShingle | Hai-Jew, Shalin Methods for analyzing and leveraging online learning data Section 1. Understanding the states of online learning systems. Chapter 1. Reading data possibilities from an LMS data portal data dictionary ; Chapter 2. Five academic years of activated third-party and custom-coded applications on an LMS instance -- Section 2. Teaching and learning design. Chapter 3. Exploring the common structures and sequences of real-world online learning modules ; Chapter 4. "Conceptual reverse engineering" of online learning objects and sequences for practical applications ; Chapter 5. Improving teaching and learning from high-level and close-in features of assignments and assessments in an LMS instance ; Chapter 6. Creating and analyzing induced decision trees from online learning data ; Chapter 7. Using social image sets to explore virtual embodiment in second life as indicators of formal, nonformal, and informal learning -- Section 3. On open learning online. Chapter 8. Peripheral vision: engaging multimodal social media datasets to differentiate MOOC platforms by course offerings and user bases ; Chapter 9. Datafication of the "e-learning faculty modules" for next steps ; Chapter 10. Visual senses of "online learning" and "instructional design": social imagery as online learning data ; Chapter 11. Using article networks on Wikipedia to explore public understandings of academic domains and address observed gaps -- Section 4. Profiling learners, profiling teachers. Chapter 12. Computational text analysis of the C2C digital magazine: using distant reading to understand the authors, organizational interests, and related professionals -- Section 5. Measuring time. Chapter 13. Highlights from extracted eras of a live LMS instance ; Chapter 14. Basic time-to-event analyses of online educational data -- Section 6. Improving instrumentation. Chapter 15. An exploratory factor analysis of an open-access virtual "privilege walk" instrument ; Chapter 16. Assessing practical accessibility in online courses based on local conditions. Educational statistics Data processing. Internet in education Management. |
| title | Methods for analyzing and leveraging online learning data |
| title_auth | Methods for analyzing and leveraging online learning data |
| title_exact_search | Methods for analyzing and leveraging online learning data |
| title_full | Methods for analyzing and leveraging online learning data by Shalin Hai-Jew. |
| title_fullStr | Methods for analyzing and leveraging online learning data by Shalin Hai-Jew. |
| title_full_unstemmed | Methods for analyzing and leveraging online learning data by Shalin Hai-Jew. |
| title_short | Methods for analyzing and leveraging online learning data |
| title_sort | methods for analyzing and leveraging online learning data |
| topic | Educational statistics Data processing. Internet in education Management. |
| topic_facet | Educational statistics Data processing. Internet in education Management. |
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