ORCID
https://orcid.org/0009-0006-3686-2229
Date of Award
2024
Document Type
Dissertation
Degree Name
Education (Ed.D.)
Department
Administrative and Instructional Leadership
First Advisor
James R Campbell
Second Advisor
Anthony J Annunziato
Third Advisor
Richard F Bernato
Abstract
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.
Recommended Citation
So, Roger Sheng, "EARLY IDENTIFICATION OF STUDENTS AT ACADEMIC RISK BASED ON LEARNING MANAGEMENT SYSTEM LOG DATA" (2024). Theses and Dissertations. 704.
https://scholar.stjohns.edu/theses_dissertations/704