Performance Prediction for Students: A Multi-Strategy Approach

Abstract

This paper presents a study on Predicting Student Performance (PSP) in academic systems. In order to solve the task, we have proposed and investigated different strategies. Specifically, we consider this task as a regression problem and a rating prediction problem in recommender systems. To improve the performance of the former, we proposed the use of additional features based on course-related skills. Moreover, to effectively utilize the outputs of these two strategies, we also proposed a combination of the two methods to enhance the prediction performance. We evaluated the proposed methods on a dataset which was built using the mark data of students in information technology at Vietnam National University, Hanoi (VNU). The experimental results have demonstrated that unlike the PSP in e-Learning systems, the regression-based approach should give better performance than the recommender system-based approach. The integration of the proposed features also helps to enhance the performance of the regression-based systems. Overall, the proposed hybrid method achieved the best RMSE score of 1.668. These promising results are expected to provide students early feedbacks about their (predicted) performance on their future courses, and therefore saving times of students and their tutors in determining which courses are appropriate for students’ ability.

The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity

“…Tran, Dang, Dinh, and Phan [22] presented a study on Predicting Student Performance (PSP) in academic systems using regression and classification methods in order to predict student performance on their courses and also help to predict which courses suited their abilities or preferences. Similarly, Pereira and Zambrano [23] used the classification technique based on decision tree to build a model for profiling student dropout pattern.…”

Optimize the Combination of Categorical Variable Encoding and Deep Learning Technique for the Problem of Prediction of Vietnamese Student Academic Performance

“…Utilization of deep learning techniques and machine learning techniques for SAPP problem has been concerned in many researches [4,5,13,14]. Using these techniques can improve the prediction quality [5,13,14] and opens many applications in reality [4]. In which, the application of deep learning techniques is a research direction that has received great attention in recent times [5].…”

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

“…Machine learning have been applied in several sectors and have achieved good results. For example financial market (Nti et al, 2019a;2020a-c), education (Adejo and Connolly, 2018;Nti and Quarcoo, 2019;Tran et al, 2017), energy (Huber et al, 2018; 2020d-e), health (Futoma et al, 2020;Ngiam and Khor, 2019;Wiens et al, 2019), agriculture (Airlangga and Liu, 2019) and more. There are numerous machine-learning algorithms, however, we present two of these algorithms that are normally used in this field.…”