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Putting the Pieces Together : = Using Learning Analytics to Inform Learning Theory, Design, Activities, and Outcomes in Higher Education.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Putting the Pieces Together :/
其他題名:	Using Learning Analytics to Inform Learning Theory, Design, Activities, and Outcomes in Higher Education.
作者:	Goodman, Amy Graham.
面頁冊數:	1 online resource (106 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
Contained By:	Dissertations Abstracts International84-05A.
標題:	Technology education. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30183036click for full text (PQDT)
ISBN:	9798352931653

Putting the Pieces Together : = Using Learning Analytics to Inform Learning Theory, Design, Activities, and Outcomes in Higher Education.
Goodman, Amy Graham.

Putting the Pieces Together :Using Learning Analytics to Inform Learning Theory, Design, Activities, and Outcomes in Higher Education. - 1 online resource (106 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: A.

Thesis (Ph.D.)--University of North Texas, 2021.

Includes bibliographical references

The goal of learning analytics is to optimize learning and the environments in which it occurs. Since 2011, when learning analytics was defined as a separate and distinct area of academic inquiry, the literature has identified a need for research that presents evidence of effective learning analytics, as well as, learning analytics research that is conducted in conjunction with learning theory. This study uses Efklides' metacognitive and affective model of self-regulated learning (MASRL) to define cognitive, metacognitive, and affective variables that can explain students' learning outcomes in hybrid/online sections of Calculus I in the 2020-21 academic year. Cognitive variables were measured according to the cognitive operational framework for analytics (COPA). Metacognitive variables were defined according to the ways in which students interacted with the course content in the learning management system (LMS) and supplemental instruction, and affective variables were measured by ways students gave evidence of their affective states, such as in discussion board posts. All variables were compared across the course learning design, activities, and outcomes. Binary logistic regression revealed five significant variables: two cognitive, one metacognitive, and two affective. Thus, this study provided a learning analytics, evidence-based link between self-regulated learning theory and learning design, activities, and outcomes. In addition, implications for students, instructors, and learning theory were explored, as well as, the qualifications of this study as evidence of effective learning analytics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352931653Subjects--Topical Terms:

3423978
Technology education.
Subjects--Index Terms:

Learning analyticsIndex Terms--Genre/Form:

542853
Electronic books.

Putting the Pieces Together : = Using Learning Analytics to Inform Learning Theory, Design, Activities, and Outcomes in Higher Education.
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