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Eitel J. M. Lauría
Researcher at Marist College
Publications - 25
Citations - 855
Eitel J. M. Lauría is an academic researcher from Marist College. The author has contributed to research in topics: Learning analytics & Bayesian network. The author has an hindex of 10, co-authored 23 publications receiving 763 citations.
Papers
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Journal ArticleDOI
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative
TL;DR: The process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at-risk students are depicted.
Book
Introduction to Information Quality
TL;DR: The purpose of this book is to alert IT-MIS-Business professionals to the pervasiveness and criticality of data quality problems and to arm the students with approaches and the commitment to overcome these problems.
Journal ArticleDOI
A Bayesian belief network for IT implementation decision support
TL;DR: This paper demonstrates how to create a BBN from real-world data on Information Technology implementations and displays the resulting BBN and describes how it can be incorporated into a DSS to support "what-if' analyses about Information Technology Implementations.
Journal ArticleDOI
A methodology for developing Bayesian networks: An application to information technology (IT) implementation
TL;DR: This article presents a methodology for building an information technology (IT) implementation BN from client–server survey data and demonstrates how to use the BN to predict the attainment of IT benefits, given specific implementation characteristics and activities.
Proceedings ArticleDOI
Mining academic data to improve college student retention: an open source perspective
Eitel J. M. Lauría,Joshua D. Baron,Mallika Devireddy,Venniraiselvi Sundararaju,Sandeep M. Jayaprakash +4 more
TL;DR: Preliminary results on initial model development using several data mining algorithms for classification are presented, and a methodological framework to develop models that can be used to perform inferential queries on student performance using open source course management system data and student academic records is laid out.