An ensembling model for early identification of at‐risk students in higher education

2021 ◽  
Author(s):  
Anika Gupta ◽  
Deepak Garg ◽  
Parteek Kumar
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Early Identification ◽  
At Risk Students

Redesigning Prediction Algorithms for At-Risk Students in Higher Education

2019 ◽  
pp. 232-250 ◽  
Author(s):  
Dennis Foung
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Logistic Regression ◽  
Academic Writing ◽  
Industry 4.0 ◽  
Classification Tree ◽  
At Risk Students ◽  
Higher Education Administrators ◽  
Writing Courses ◽  
Education Administrators

Use of algorithms and data mining approaches are not new to Industry 4.0. However, these may not be common for students and educators in higher education. This chapter compares various classification techniques: classification tree, logistic regression, and artificial neural networks (ANN). The comparison focuses on each method's accuracy, algorithm, and practicality in higher education. This study made use of a dataset from two academic writing courses in a university in Hong Kong with more than 5,000 records. Results suggest that classification trees and logistic regression can be easily used in the higher education context, but ANN may not be applicable in higher educational settings. The research team suggests that higher education administrators take this research forward and design platforms to realize these classification algorithms to predict at-risk students.

scholarly journals An Early Warning System to Detect At-Risk Students in Online Higher Education

2020 ◽  
Vol 10 (13) ◽  
pp. 4427 ◽  
Author(s):  
David Bañeres ◽  
M. Elena Rodríguez ◽  
Ana Elena Guerrero-Roldán ◽  
Abdulkadir Karadeniz
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Predictive Model ◽  
Early Warning ◽  
Early Warning System ◽  
Time Lag ◽  
At Risk Students ◽  
Warning System ◽  
Online Higher Education ◽  
Depth Analysis

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

scholarly journals Exploring the “At-Risk” Student Label Through the Perspectives of Higher Education Professionals

2020 ◽  
Author(s):  
Nick Dix ◽  
Andrew Lail ◽  
Matt Birnbaum ◽  
Joseph Paris
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Undergraduate Students ◽  
Thematic Analysis ◽  
At Risk Students ◽  
Institutions Of Higher Education ◽  
Education Professionals ◽  
Higher Education Professionals ◽  
Risk Definition ◽  
At Risk Student

Institutions of higher education often use the term “at-risk” to label undergraduate students who have a higher likelihood of not persisting. However, it is not clear how the use of this label impacts the perspectives of the higher education professionals who serve and support these students. Our qualitative study explores the descriptions and understandings of higher education professionals who serve and support at-risk students. We use thematic analysis (Braun & Clark, 2006) to interpret our data and develop our themes. These themes include conflicting views of the “at-risk” definition, attempts to normalize at-risk, fostering relationships, and “at-promise.”

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