A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

• Main Authors: Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.
• Other Authors: 58061521900
• Format: Article
• Published: International Association of Online Engineering 2023

One of the key applications of Learning Analytics is offering an opportunity to the institutions to track the students' academic activities and provide them with real-time adaptive consultations regarding the students' academic progression. However, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. Machine learning algorithm's performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of significant attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with an exhausting list of attributes used in developing student performance prediction models. We propose a conceptual framework which identifies the nature of attributes and classify them as either latent or dynamic. The latent attributes may appear significant, but the student is not able to control these attributes, on the other hand, the student has command to restrain the dynamic attributes. The framework presents an opportunity to the researchers to pick constructive attributes for model development. We apply artificial neural network, a supervised learner, over a dataset to compare the performance of prediction models with distinct classes of attributes. It confirms the significance of dynamic attributes for student performance prediction models. � 2021. All Rights Reserved.