Al-Gahmi, A. (2024, June), On the Portability and Robustness of Early Student Performance Predictions  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47811

\bibitem{asee_peer_47811}
  Al-Gahmi, A. (2024, June), \emph{On the Portability and Robustness of Early Student Performance Predictions}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  https://peer.asee.org/47811

Abdulmalek Al-Gahmi.  "On the Portability and Robustness of Early Student Performance Predictions".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/47811 Internet.  30 Jul, 2024

\bibitem{asee_peer_47811}
  Abdulmalek Al-Gahmi.
  "On the Portability and Robustness of Early Student Performance Predictions".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/47811 Internet.  30 Jul, 2024

@INPROCEEDINGS{asee_peer_47811
author = "Abdulmalek Al-Gahmi"
title = "On the Portability and Robustness of Early Student Performance Predictions"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/47811}
}

TY  - CPAPER
AB  - The early prediction of student performance has long been a significant area of interest within the educational research community. Numerous studies have sought to identify students who struggle early in their courses, with the goal of providing timely interventions. This early detection of at-risk students is vital for fostering and promoting student success, which is critical to the missions of higher-education institutions and their long-term goals of improving student graduation and retention rates.

This paper builds on the predictive models from a previous paper, focused on making early course-based predictions of student performance based on data collected from a Learning Management System (LMS). It describes these models, augments them with new features, and investigates the portability and robustness of their predictions— aspects not addressed in the previous paper. It frames the early prediction of student performance as a binary classification problem, distinguishing between two student groups: at-risk students who are failing their courses and require early identification for timely assistance, and those who are performing satisfactorily. Additionally, this paper employs Explainable AI (XAI) methods to gain insight into how the new models generate their predictions and to identify the key data features that contribute to these predictions.

This paper relies on data gathered from the Canvas Learning Management System through RESTful and GraphQL APIs. The data pertain to 274 lower-division Computer Science courses, encompassing 2,656 students and 37 instructors. These courses are delivered in various formats (face-to-face, online, and virtual), spanning a three-year period at a public four-year university.

AU  - Abdulmalek Al-Gahmi
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - On the Portability and Robustness of Early Student Performance Predictions
UR  - https://peer.asee.org/47811
ER  -