In education, quality is the focus; improving the quality of education and teaching has always been one of the goals pursued by educators. Machine learning and data mining techniques can reveal data-related laws and extract valuable information and data to solve problems in various fields. This paper proposes a model to predict the National Assessment of Educational Progress (NAEP) exam scores using LightGBM, a kind of GBDT (gradient boosting decision tree) that owns optimal performance in the industry field. It performs a comparison-based experiment using the same metrics and the same dataset. The lower the Root Mean Square Error (RMSE), the better performance that the model will gain. Accordingly, the LightGBM model has the best performance, with 0.544 and 9.344 lower than SVM and Linear Regression, respectively.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.