| 000 | 03661cam a22004218i 4500 | ||
|---|---|---|---|
| 999 |
_c14505 _d14505 |
||
| 001 | 20455663 | ||
| 005 | 20191001083542.0 | ||
| 008 | 180417s2018 nyu b 000 0 eng | ||
| 010 | _a 2018013484 | ||
| 020 | _a978-1-138-30217-4 (Paperback) | ||
| 020 | _z9781351400534 (Web PDF) | ||
| 020 | _z9781351400527 (ePub) | ||
| 020 | _z9781351400510 (Mobi/Kindle) | ||
| 040 |
_aDLC _beng _cDLC _erda |
||
| 042 | _apcc | ||
| 050 | 0 | 0 |
_aLB2331.72 _b.L42 2018 |
| 082 | 0 | 0 |
_aDC 378 _22019 _bL479 |
| 245 | 0 | 0 |
_aLearning analytics in higher education : _bcurrent innovations, future potential, and practical applications / _cby Jaime Lester, Carrie Klein, Aditya Johri and Huzefa Rangwala. |
| 260 |
_aNew York, NY : _bRoutledge, _c©2019 |
||
| 263 | _a1808 | ||
| 300 |
_axv, 199 pages : _billustrations ; _c23 cm |
||
| 504 | _aIncludes bibliographical references. | ||
| 505 | 0 | _aChapter 1. Absorptive capacity and routines: Understanding barriers to learning analytics adoption in higher education / Aditya Johri -- Chapter 2. Analytics in the field: Why locally grown continuous improvement systems are essential for effective data driven decision-making / Matthew T. Hora -- Chapter 3. Big data, small data, and data shepherds / Jennifer DeBoer and Lori Breslow -- Chapter 4. Evaluating scholarly teaching: A model and call for an evidence-based approach / Daniel L. Reinholz, Joel C. Corbo, Daniel J. Bernstein, and Noah D. Finkelstein -- Chapter 5. Discipline-focused learning analytics approaches with users instead of for users / David B. Knight, Cory Brozina, Timothy J. Kinoshita, Brian J. Novoselich, Glenda D. Young, and Jacob R. Grohs -- Chapter 6. Student consent in learning analytics: The devil in the details? / Paul Prinsloo and Sharon Slade -- Chapter 7. Using learning analytics to improve student learning outcomes assessment in higher education: Potential, constraint, & possibility / Carrie Klein and Richard M. Hess -- Chapter 8. Data, data everywhere: Implications and considerations / Matthew D. Pistilli. | |
| 520 | _aLearning analytics in higher education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators | ||
| 650 | 0 |
_aAcademic achievement _xEvaluation. _940091 |
|
| 650 | 0 |
_aEducation, Higher _xEvaluation. _940092 |
|
| 650 | 0 |
_aEducational evaluation _xData processing. _940093 |
|
| 650 | 0 |
_aData mining. _940094 |
|
| 650 | 0 |
_aUniversities and colleges _xResearch. _940095 |
|
| 700 | 1 |
_aLester, Jaime, _eeditor. _940096 |
|
| 700 | 1 |
_aKlein, Carrie, editor _940097 |
|
| 700 | 1 |
_aJohri, Aditya, 1976- editor _940098 |
|
| 700 | 1 |
_aRangwala, Huzefa, editor _940099 |
|
| 365 |
_b4,138.00 _cPhp |
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| 906 |
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| 942 |
_2ddc _cDC |
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