Predicting Students’ College Drop Out and Departure Decisions by Analyzing their Campus-Based Social Network Text Messages
Files
Date
2020-01-07
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Undergraduate student retention is a key concern in the US higher education system. Having a scientific method for predicting undergraduate student departure would enable institutions to deploy targeted interventions with the goal of retaining a particular student who is at risk of dropping out. We explore the use of Latent Dirichlet Allocation (LDA), Systemic Functional Linguistics (SFL), and new techniques for Social Network Analytics addressing student communications within a novel campus-based closed social networking platform. Our research results indicate that students who were ultimately retained sent three times as many messages than those who were not, and analyzing the patterns of use of the closed social network in an academic setting reliably predicts undergraduate student dropouts and leads to a more effective deployment of retention resources over time.
Description
Keywords
Analyzing the Impact of Digitization on Business Operations, sentiment analysis, social networks, universities
Citation
Extent
7 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 53rd Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.