Publication Date
8-2025
Document Type
Research Project
Publication Title
Gavilan College's 2025 Poster Symposium
Abstract
Multiple factors influence student performance, including demographics, institutional support, study habits, and prior academic results. While educational data mining (EDM) research has primarily examined online learning, traditional classroom settings remain understudied. Early performance indicators strongly predict final outcomes, making timely intervention crucial. However, current models predict more acurately during courses than before they begin, revealing a need for better pre-course forecasting.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science; Mathematics and Statistics
Recommended Citation
Mingmar Sherpa and Maryam Khazaei Pool. "Data Analysis & ML: Analyzing Student Performance Data" Gavilan College's 2025 Poster Symposium (2025).
