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Early Identification of Students at ...

So, Roger Sheng.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Early Identification of Students at Academic Risk Based on Learning Management System Log Data./
Author:	So, Roger Sheng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	81 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
Contained By:	Dissertations Abstracts International85-09A.
Subject:	Higher education administration. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30693620
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. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 81 p.

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

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

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.

ISBN: 9798381745009Subjects--Topical Terms:

2122863
Higher education administration.
Subjects--Index Terms:

Canvas

Early Identification of Students at Academic Risk Based on Learning Management System Log Data.
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245 1 0 $a Early Identification of Students at Academic Risk Based on Learning Management System Log Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 81 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
500 $a Advisor: Campbell, James R.
502 $a Thesis (Ed.D.)--St. John's University (New York), 2024.
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.
590 $a School code: 0192.
650 4 $a Higher education administration. $3 2122863
650 4 $a Educational technology. $3 517670
650 4 $a Educational evaluation. $3 526425
650 4 $a Educational leadership. $3 529436
653 $a Canvas
653 $a Learning Management Systems
653 $a Retention
653 $a Student engagement
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690 $a 0710
690 $a 0443
690 $a 0449
710 2 $a St. John's University (New York). $b SOE Department of Administrative and Instructional Leadership. $3 3557433
773 0 $t Dissertations Abstracts International $g 85-09A.
790 $a 0192
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792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30693620