大葉大學圖書館 |

Methods for analyzing and leveraging online learning data[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	371.33/44678
書名/作者:	Methods for analyzing and leveraging online learning data/ by Shalin Hai-Jew.
作者:	Hai-Jew, Shalin,
出版者:	Hershey, Pennsylvania : : IGI Global,, [2019]
面頁冊數:	1 online resource (xviii, 436 p.)
標題:	Educational statistics - Data processing.
標題:	Internet in education - Management.
ISBN:	9781522575290 (ebk.)
ISBN:	9781522575283 (hbk.)
書目註:	Includes bibliographical references and index.
內容註:	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.
摘要、提要註:	"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.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7528-3

Methods for analyzing and leveraging online learning data[electronic resource] /
Hai-Jew, Shalin,

Methods for analyzing and leveraging online learning data[electronic resource] /by Shalin Hai-Jew. - Hershey, Pennsylvania :IGI Global,[2019] - 1 online resource (xviii, 436 p.)

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.

ISBN: 9781522575290 (ebk.)Subjects--Topical Terms:

691838
Educational statistics
--Data processing.

LC Class. No.: LB1028.43 / .H25 2019e

Dewey Class. No.: 371.33/44678

Methods for analyzing and leveraging online learning data[electronic resource] /
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020 $a 9781522575283 (hbk.)
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245 1 0 $a Methods for analyzing and leveraging online learning data $h [electronic resource] / $c by Shalin Hai-Jew.
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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.
650 0 $a Educational statistics $x Data processing. $3 691838
650 0 $a Internet in education $x Management. $3 739743
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7528-3