Individualized help for at-risk students using model-agnostic and counterfactual explanations
Although using machine learning for predicting which students are at risk of failing a course
is indeed valuable, how can we identify which characteristics of individual students …
is indeed valuable, how can we identify which characteristics of individual students …
Predicting new student performances and identifying important attributes of admission data using machine learning techniques with hyperparameter tuning
Recently, many global universities have faced high student failure and early dropout rates
reflecting on the quality of education. To tackle this problem, forecasting student success as …
reflecting on the quality of education. To tackle this problem, forecasting student success as …
[PDF][PDF] XLA: Explainable Learning Analytics
In the last decade, we are witnessing a widespread adoption of artificial intelligence in a
wide range of application domains. Learning analytics is no exception. Artificial Intelligence …
wide range of application domains. Learning analytics is no exception. Artificial Intelligence …
Using Machine Learning to Predict Student Success in Undergraduate Engineering Programs
BP DeJong, E Karadogan - 2024 IEEE 3rd International …, 2024 - ieeexplore.ieee.org
Undergraduate engineering programs are typically considered some of the most
challenging as their curricula require students to have an aptitude for math, science, and …
challenging as their curricula require students to have an aptitude for math, science, and …
Understanding Student Success Prediction Using SHapley Additive exPlanations
B Ujkani, D Minkovska, O Nakov - 2023 International Scientific …, 2023 - ieeexplore.ieee.org
Predicting student success is an important task in educational institutions, as it allows for
targeted interventions and support systems to enhance educational outcomes. This paper …
targeted interventions and support systems to enhance educational outcomes. This paper …
Predicting engineering students' academic performance using ensemble classifiers-a preliminary finding/A'zraa Afhzan Ab Rahim and Norlida Buniyamin
N Buniyamin - Journal of Electrical and Electronic Systems …, 2022 - ir.uitm.edu.my
Current literature review indicates a void of an accurate predictive tool to assist educators
and administrators in analyzing and monitoring student performance in Malaysia …
and administrators in analyzing and monitoring student performance in Malaysia …
[图书][B] The Relationship Between Multifaceted Motivational Factors and Academic Achievement
S Beasley - 2020 - search.proquest.com
Abstract The United States has yet to reach the White House's 2020 goal of attaining the top
international ranking in college degree attainment among young adults. Researchers have …
international ranking in college degree attainment among young adults. Researchers have …
[PDF][PDF] Explainable Learning Analytics: challenges and opportunities
In the last decade, we are witnessing a widespread adoption of artificial intelligence in a
wide range of application domains. Learning analytics is no exception. Artificial Intelligence …
wide range of application domains. Learning analytics is no exception. Artificial Intelligence …
[图书][B] Student Attrition at CSULB: Interpretable Classification with Imbalanced Datasets
J Braswell - 2021 - search.proquest.com
Over the past five years retention rates at Cal State Long Beach have been dropping. For an
institution that has made commitments to raise graduation rates by 2025 this is a serious …
institution that has made commitments to raise graduation rates by 2025 this is a serious …
[PDF][PDF] Characterizing At-Risk Students and measuring treatment effects using machine learning methods
BI Smith - 2020 - wiredspace.wits.ac.za
Predicting and characterizing students that are at risk of failing is important because it would
allow At-Risk students to be identified and recommended to various approaches that could …
allow At-Risk students to be identified and recommended to various approaches that could …