Feature Engineering on LMS Data to Optimize Student Performance Prediction

Keith Hubbard; Sheilla Amponsah

doi:10.1007/978-3-031-99879-9_8

Feature Engineering on LMS Data to Optimize Student Performance Prediction

Mathematics and Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	New Frontiers in Data Science - 4th Southwest Data Science Conference, SDSC 2025, Revised Selected Papers
Editors	Henry Han, James Stamey
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	125-142
Number of pages	18
ISBN (Print)	9783031998782
DOIs	https://doi.org/10.1007/978-3-031-99879-9_8
State	Published - 2026
Event	4th Southwest Data Science Conference, SDSC 2025 - Waco, United States Duration: Mar 21 2025 → Mar 22 2025

Publication series

Name	Communications in Computer and Information Science
Volume	2556 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	4th Southwest Data Science Conference, SDSC 2025
Country/Territory	United States
City	Waco
Period	3/21/25 → 3/22/25

ASJC Scopus Subject Areas

General Computer Science
General Mathematics

Keywords

College Student Success
Learning Management System Data
LMS Feature Engineering
Predictive Modeling

Access to Document

10.1007/978-3-031-99879-9_8

Cite this

Hubbard, K., & Amponsah, S. (2026). Feature Engineering on LMS Data to Optimize Student Performance Prediction. In H. Han, & J. Stamey (Eds.), New Frontiers in Data Science - 4th Southwest Data Science Conference, SDSC 2025, Revised Selected Papers (pp. 125-142). (Communications in Computer and Information Science; Vol. 2556 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-99879-9_8

Feature Engineering on LMS Data to Optimize Student Performance Prediction. / Hubbard, Keith; Amponsah, Sheilla.
New Frontiers in Data Science - 4th Southwest Data Science Conference, SDSC 2025, Revised Selected Papers. ed. / Henry Han; James Stamey. Springer Science and Business Media Deutschland GmbH, 2026. p. 125-142 (Communications in Computer and Information Science; Vol. 2556 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hubbard, K & Amponsah, S 2026, Feature Engineering on LMS Data to Optimize Student Performance Prediction. in H Han & J Stamey (eds), New Frontiers in Data Science - 4th Southwest Data Science Conference, SDSC 2025, Revised Selected Papers. Communications in Computer and Information Science, vol. 2556 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 125-142, 4th Southwest Data Science Conference, SDSC 2025, Waco, United States, 3/21/25. https://doi.org/10.1007/978-3-031-99879-9_8

Hubbard K, Amponsah S. Feature Engineering on LMS Data to Optimize Student Performance Prediction. In Han H, Stamey J, editors, New Frontiers in Data Science - 4th Southwest Data Science Conference, SDSC 2025, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2026. p. 125-142. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-99879-9_8

Hubbard, Keith ; Amponsah, Sheilla. / Feature Engineering on LMS Data to Optimize Student Performance Prediction. New Frontiers in Data Science - 4th Southwest Data Science Conference, SDSC 2025, Revised Selected Papers. editor / Henry Han ; James Stamey. Springer Science and Business Media Deutschland GmbH, 2026. pp. 125-142 (Communications in Computer and Information Science).

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Feature Engineering on LMS Data to Optimize Student Performance Prediction

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this