Prediction of Course Grades in Computer Science Higher Education Program via a Combination of Loss Functions in LSTM Model

Abstract

In the realm of education, the timely identification of potential challenges, such as learning difficulties leading to dropout risks, and the facilitation of personalized learning, emphasizes the crucial importance of early grade prediction. This study seeks to connect predictive modeling with educational outcomes, particularly focusing on addressing these challenges in computer science higher education programs. To address these issues, nonlinear dynamic systems, notably Recurrent Neural Networks (RNNs), have demonstrated efficacy in unraveling the intricate relationships within student learning traces, surpassing the constraints of traditional time series methods. However, the challenge of vanishing gradient issues hampers RNNs, leading to a significant decrease in gradient values during weight matrix multiplication. To solve this challenge, we introduce an innovative loss function, the MSECosine loss function crafted by seamlessly combining two established loss functions: Mean Square Error (MSE) and LogCosh. In assessing the performance of this novel loss function, we employed two self-collected datasets comprising learning management system (LMS) and assessment records from a higher education computer science program. These datasets serve as the testing ground for four deep time series models: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and CNN-LSTM. Employing 29 meticulously designed feature sets representing combination of demography, learning activities and assessment, LSTM emerges as the preeminent model which is consistent with our expectation that RNN is the best suited approach. Building on this groundwork, we solve the vanishing gradient issue and boost the LSTM model’s performance by integrating the proposed MSECosine loss function, resulting in an enhanced model termed eLSTM. Experimental results underscore the noteworthy achievements of the eLSTM model, emphasizing an accuracy of 0.6191% and a substantially reduced error rate of 0.1738. The proposed MSECosine loss function performance in addressing the vanishing gradient issue yields two times better than compared to standard loss functions. These outcomes surpass those of alternative approaches, highlighting the instrumental role of the MSECosine loss function in refining eLSTM for more accurate predictions in course grade prediction, as well as the feature set that captures early grade prediction.

Keywords

• Learning analytics
• hybrid objective functions
• deep time series neural network
• LSTM model optimization