Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

The rapid growth of technology has meant that computer learning has increasingly integrated artificial intelligence techniques in order to develop more personalized educational systems. These systems are known as Intelligent Tutoring systems (ITS). MetaTutorES (Cerezo, Esteban, et al., 2020; Cerezo, Fernández, et al., 2020), a Spanish adaptation of MetaTutor (Azevedo, 2009) is an ITS designed to detect, model, trace, and foster students' self-regulated learning while learning various science topics (e.g., by modeling and scaffolding metacognitive monitoring, facilitating the use of effective learning strategies, and setting and coordinating relevant learning goals). The system uses human-like avatar technology that allows pedagogical agents to track student behavior and provide interaction on this basis. Tracking students' behavior is also a powerful research tool used to collect data on students' cognitive, metacognitive, affective, and motivational processes deployed during learning (Azevedo et al., 2011; Greene & Azevedo, 2010; Harley et al., 2014). These different data sources can be fused and mined to to reveal learning-related information such as student performance.