Leveraging Hybrid Deep Q-Learning for Early Identification of At-Risk Students

Student performance prediction is employed to predict the learning performance to identify at-risk students. However, prediction models should also consider external factors along with learning activities, such as course duration. The student’s performance gets affected, which leads to a high decreasing rate and meets the risk of failing to complete the course on time. To overcome these challenges, this work proposed a Sea Lion Search Optimization (SLnSO) based on the Deep Q network (DQN) for predicting at-risk students. Here, the input data is taken from the dataset and forwarded to the data transformation phase, which is performed by Yeo-Johnson (YJ) transformation. Then, in the feature selection stage, the most relevant features are selected using the Damerau-Levenshtein technique. Then, Data Augmentation (DA) is performed to increase the dimension of the features, which is followed by the Deep Q Network (DQN) that is utilized for predicting the students at risk. Finally, by implementing the proposed SLnSO, the predicted results will be executed by DQN. The SLnSO-DQN is the combination of both Sea Lion Optimization (SLnO) and Squirrel Search Algorithm (SSA). The outcomes of the proposed model SLnSO-DQN attain significant performance that is based on various parameters, such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root MSE (RMSE), and also obtained better values of 0.327, 0.265, and 0.514, respectively.