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Applying data science and learning analytics throughout a learner's lifespan

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying data science and learning analytics throughout a learner's lifespan/ Goran Trajkovski, Marylee Demeter, Heather Hayes editors.
other author:	Hayes, Heather.
Published:	Hershey, Pennsylvania :IGI Global, : 2022.,
Description:	1 online resource (314 p.)
[NT 15003449]:	Section 1. Perspectives on data. Chapter 1. Mind the gap: from typical LMS traces to learning to learn journeys ; Chapter 2. Relationships between out-of-school-time lessons and academic performance among adolescents in four high-performing education systems ; Chapter 3. An assessment of self-service business intelligence tools for students: the impact of cognitive needs and innovative cognitive styles -- Section 2. Applications. Chapter 4. Univariate and multivariatefiltering techniques for feature selection and their applications in field of machine learning ; Chapter 5. Scalable personalization for student success: a framework for using machine learning methods in self-directed online courses ; Chapter 6. User sentiment analysis and review rating prediction for the blended learning platform app ; Chapter 7. Analyzing and predicting learner sentiment toward specialty schools using machine learning techniques -- Section 3. Advancing theprofession. Chapter 8. Working towards a data science associates degree program: impacts, challenges, and future directions ; Chapter 9. A storied approach to learning data analytics in a graduate data analytics program ; Chapter 10. Identifying structure in program-level competencies and skills: dimensionality analysis of performance assessment scores from multiple courses in an IT program -- Section 4. Considerations. Chapter 11. Don't me: a study of the perception of twitter users of educational offerings ; Chapter 12. The secret lives of eportfolios: text network analysis and the future of algorithmic hiring ; Chapter 13. Do we need a digital data exorcism?: end of life considerations of data mining educational content.
Subject:	Education - Data processing. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-9644-9
ISBN:	9781799896463 (electronic bk.)

Applying data science and learning analytics throughout a learner's lifespan
Applying data science and learning analytics throughout a learner's lifespan[electronic resource] /Goran Trajkovski, Marylee Demeter, Heather Hayes editors. - Hershey, Pennsylvania :IGI Global,2022. - 1 online resource (314 p.)

Includes bibliographical references and index.

Section 1. Perspectives on data. Chapter 1. Mind the gap: from typical LMS traces to learning to learn journeys ; Chapter 2. Relationships between out-of-school-time lessons and academic performance among adolescents in four high-performing education systems ; Chapter 3. An assessment of self-service business intelligence tools for students: the impact of cognitive needs and innovative cognitive styles -- Section 2. Applications. Chapter 4. Univariate and multivariatefiltering techniques for feature selection and their applications in field of machine learning ; Chapter 5. Scalable personalization for student success: a framework for using machine learning methods in self-directed online courses ; Chapter 6. User sentiment analysis and review rating prediction for the blended learning platform app ; Chapter 7. Analyzing and predicting learner sentiment toward specialty schools using machine learning techniques -- Section 3. Advancing theprofession. Chapter 8. Working towards a data science associates degree program: impacts, challenges, and future directions ; Chapter 9. A storied approach to learning data analytics in a graduate data analytics program ; Chapter 10. Identifying structure in program-level competencies and skills: dimensionality analysis of performance assessment scores from multiple courses in an IT program -- Section 4. Considerations. Chapter 11. Don't me: a study of the perception of twitter users of educational offerings ; Chapter 12. The secret lives of eportfolios: text network analysis and the future of algorithmic hiring ; Chapter 13. Do we need a digital data exorcism?: end of life considerations of data mining educational content.

"This publication examines novel and emerging applications of data science and sister disciplines in gaining insights from data to inform interventions into the learners' journey and interactions with an academic or training institution, covering topics such as building models of learners for success, using data to inform courseware and assessmentware development."

ISBN: 9781799896463 (electronic bk.)Subjects--Topical Terms:

524970
Education
--Data processing.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: LB1028.43 / .A63 2022eb

Dewey Class. No.: 371.334

Applying data science and learning analytics throughout a learner's lifespan
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245 0 0 $a Applying data science and learning analytics throughout a learner's lifespan $h [electronic resource] / $c Goran Trajkovski, Marylee Demeter, Heather Hayes editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2022.
300 $a 1 online resource (314 p.)
504 $a Includes bibliographical references and index.
505 0 $a Section 1. Perspectives on data. Chapter 1. Mind the gap: from typical LMS traces to learning to learn journeys ; Chapter 2. Relationships between out-of-school-time lessons and academic performance among adolescents in four high-performing education systems ; Chapter 3. An assessment of self-service business intelligence tools for students: the impact of cognitive needs and innovative cognitive styles -- Section 2. Applications. Chapter 4. Univariate and multivariatefiltering techniques for feature selection and their applications in field of machine learning ; Chapter 5. Scalable personalization for student success: a framework for using machine learning methods in self-directed online courses ; Chapter 6. User sentiment analysis and review rating prediction for the blended learning platform app ; Chapter 7. Analyzing and predicting learner sentiment toward specialty schools using machine learning techniques -- Section 3. Advancing theprofession. Chapter 8. Working towards a data science associates degree program: impacts, challenges, and future directions ; Chapter 9. A storied approach to learning data analytics in a graduate data analytics program ; Chapter 10. Identifying structure in program-level competencies and skills: dimensionality analysis of performance assessment scores from multiple courses in an IT program -- Section 4. Considerations. Chapter 11. Don't me: a study of the perception of twitter users of educational offerings ; Chapter 12. The secret lives of eportfolios: text network analysis and the future of algorithmic hiring ; Chapter 13. Do we need a digital data exorcism?: end of life considerations of data mining educational content.
520 3 $a "This publication examines novel and emerging applications of data science and sister disciplines in gaining insights from data to inform interventions into the learners' journey and interactions with an academic or training institution, covering topics such as building models of learners for success, using data to inform courseware and assessmentware development."
650 0 $a Education $x Data processing. $3 524970
650 0 $a Data mining. $3 562972
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Hayes, Heather. $3 3675246
700 1 $a Demeter, Marylee. $3 3675247
700 1 $a Trajkovski, Goran. $3 3675248
710 2 $a IGI Global. $3 1361470
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-9644-9