Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/64387

Predicting Students’ College Drop Out and Departure Decisions by Analyzing their Campus-Based Social Network Text Messages

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Title:Predicting Students’ College Drop Out and Departure Decisions by Analyzing their Campus-Based Social Network Text Messages
Authors:Allen, Rebecca
Nakonechnyi, Alex
Seidmann, Abraham
Roberts, Jacqueline
Keywords:Analyzing the Impact of Digitization on Business Operations
sentiment analysis
social networks
universities
Date Issued:07 Jan 2020
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.
Pages/Duration:7 pages
URI:http://hdl.handle.net/10125/64387
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.645
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Analyzing the Impact of Digitization on Business Operations


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