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Early Identification of Students at Academic Risk Based on Learning Management System Log Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Early Identification of Students at Academic Risk Based on Learning Management System Log Data./
Author:	So, Roger Sheng.
Description:	1 online resource (81 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
Contained By:	Dissertations Abstracts International85-09A.
Subject:	Higher education administration. -
Online resource:	click for full text (PQDT)
ISBN:	9798381745009

Early Identification of Students at Academic Risk Based on Learning Management System Log Data.
So, Roger Sheng.

Early Identification of Students at Academic Risk Based on Learning Management System Log Data. - 1 online resource (81 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: A.

Thesis (Ed.D.)--St. John's University (New York), 2024.

Includes bibliographical references

Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify students who are at academic risk. The scope of this study is to retrospectively analyze first-year student activity for the Spring 2022 semester for early warning signs worthy of intervention. A student risk assessment will be determined by reviewing student LMS activity, compared with peers, during the semester.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381745009Subjects--Topical Terms:

1148709
Higher education administration.
Subjects--Index Terms:

CanvasIndex Terms--Genre/Form:

554714
Electronic books.

Early Identification of Students at Academic Risk Based on Learning Management System Log Data.
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502 $a Thesis (Ed.D.)--St. John's University (New York), 2024.
504 $a Includes bibliographical references
520 $a Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify students who are at academic risk. The scope of this study is to retrospectively analyze first-year student activity for the Spring 2022 semester for early warning signs worthy of intervention. A student risk assessment will be determined by reviewing student LMS activity, compared with peers, during the semester.
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538 $a Mode of access: World Wide Web
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650 4 $a Educational evaluation. $3 555501
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653 $a Learning Management Systems
653 $a Retention
653 $a Student engagement
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