Leveraging ensemble learning for stealth assessment model with game-based learning environment

A distinguishing feature of intelligent game-based learning environment is its capacity for assisting stealth assessment. Stealth assessment gathers data regarding student competency in an invisible way and enables drawing valid inferences with respect to student knowledge. Stealth assessment might radically extend the impact and scope of learning analytics. Stealth assessment describes the unobtrusive assessment of learner by using emergent data from the digital traces in electronic learning environment via machine learning technology. This study presents a new stealth assessment model using ensemble learning for inferring the student competency in a game-based learning (GBL) environments, named ELSAM-GBL technique. To perform automated and accurate stealth assessment, this study focuses on the design of ensemble learning model by the incorporation of three DL models, namely gated recurrent unit (GRU), sparse auto encoder (SAE), and vanilla recurrent neural network (RNN). At the same time, the hyperparameter tuning of the DL models takes place using the atomic orbital search (AOS) optimization algorithm, which helps in improving the ensemble learning process. To demonstrate the enhanced stealth assessment performance of the ELSAM-GBL technique, a comprehensive experimental analysis is conducted. The comparative study shows the enhanced performance of the presented ELSAM-GBL technique over other DL models in terms of different metrics.