Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data

Abstract

The ability to predict students' academic performance is critical for any educational institution that aims to improve their students' learning process and achievement. Although students' performance prediction problem is studied widely, it still represents a challenge and complex issue for educational institutions due to the different features that affect students learning process and achievement in courses. Moreover, the utilization of web-based learning systems in education provides opportunities to study how students learning and what learning behavior leading them to success. The main objective of this research was to investigate the impact of assessment grades and online activity data in the Learning Management System (LMS) on students' academic performance. Based on one of the commonly used data mining techniques for prediction, called classification. Five classification algorithms were applied that decision tree, random forest, sequential minimal optimization, multilayer perceptron, and logistic regression. Experimental results revealed that assessment grades are the most important features affecting students' academic performance. Moreover, prediction models that included assessment grades alone or in combination with activity data perform better than models based on activity data alone. Also, random forest algorithm performs well for predicting student a cademic performance, followed by decision tree.

Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

“…Alhassan et al [15] proposed a study of analyzing student learning behaviors and predicting their academic performance in web-based learning management systems (LMS). The study observed the student learning patterns on the online study platform using five machine learning classifiers: J48 of decision tree, random forest (RF), the logistic regression (LR), sequential minimal optimization (SMO), and multilayer perceptron (MLP).…”

“…Accuracy and root mean square error are the two commonly used metrics evaluating predction models. www.ijacsa.thesai.org  Accuracy (ACC): ACC is a common model evaluation metric used to evlauce the performance of prediction model by computing the percentage of coreectly prediction [15]. It is calculated as in (1).…”

“…Correctly predicted values ACC = Total values (1)  Root Mean Square Error (RMSE): RMSE is a standard evaluation metric used to evaluate prediction error by computing the error or difference between actual output and predicted output [15]. It can be calculated using (2).…”

“…Various factors lead to have higher dimensions. Hence, many studies have focused on determining the related factors that affect student performance and predict their academic progress by using FS methods and applying predictive models of EDM [11][12][13][14][15][16]. In educational research, FS methods aim at determining important factors that are in need and sufficient to report the academic performance; we call it as dominant factors.…”

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

“…Phase 2 involved two parts. The first part was the building, training, and testing of ensemble-based models by using Bagging (BAG), Random SubSpace (RNDS), MultiClass Classifier (MCC), and Rotation of Forest (ROF) Algorithms [15]. The second part was the building, training and testing of the base learner or classification-based models using Naïve Bayes (NB), MultiLayer Perceptron (MLP), k-Nearest Neighbour (KNN), and Decision Table (DT) algorithms.…”