ERIC Number: ED634741
Record Type: Non-Journal
Publication Date: 2023
Pages: 108
Abstractor: As Provided
ISBN: 979-8-3795-8748-2
ISSN: N/A
EISSN: N/A
The Relationship of Instructor-Type and Intervention in a First-Year Seminar Using a Closed-Loop Analytics Model
Hahn, Lisa M.
ProQuest LLC, Ed.D. Dissertation, Northern Arizona University
In the post-COVID-19 higher education landscape, administrators must question the legacy of their programs and methods to rise up and meet not only the technological integrations that are a part of educational infrastructures but the demands of the profession moving it beyond the twenty-first century. Since the twentieth-century, first-year seminars have been an essential and fundamental retention tool for incoming students into higher education institutions. However, with a lack of operational definitions, program uniformity, and contemporary research indicating their usefulness for today's students, research is needed to navigate technologically enhanced pedagogies and the design principles that can and should be replicated. This study examined the data from a closed-loop learning analytics model in a first-year seminar to identify the relationship between instructor-type and the timing of intervention when predicting for the real-time student success indicator of the course grade. Results from this study have both practical and research implications that indicate further efforts are needed to understand what elements of this traditional model should be moved into the classrooms serving students today versus those that have not been previously highlighted as essential for success. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]
Descriptors: Intervention, Grades (Scholastic), First Year Seminars, Teacher Characteristics, Learning Analytics, Prediction, Success
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Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A