Examining Sequential, Temporal, and Cross-Channel Interactions of Cognitive, Affective, and Metacognitive States on Learning Outcomes

• In Event: Feature Engineering for Learning Analytics in Higher Education Research on Motivation and Self-Regulation

Abstract

Game-based learning (GBL) research shows learners maintain a high level of enjoyment and motivation during learning of complex topics compared to traditional methods (Taub et al., 2020; Plass et al., 2015; Qian et al., 2016). GBL features are designed to balance emotional engagement while fostering knowledge acquisition. Thus, when learners engage in GBL, they must regulate different learning processes, including cognition, affect, metacognition, and motivation (CAMM), to ensure their learning efficiency. Self-regulated learning (SRL) requires learners to actively and accurately monitor and control their learning processes and behaviors in learning situations (Winne et al., 2014). Yet, studies show that learners are not effective self-regulators. A contemporary model of SRL called MASRL (Efklides et al., 2011) explains that affect and motivation interact dynamically with metacognition and cognition, which drives effective regulation. However, little research has evaluated the dynamics and interactions among SRL CAMM during GBL and its influence on learning. This research explores the sequential and temporal dynamics of cognitive, affective, and metacognitive states during GBL, utilizing think- and emote-aloud protocols. We also evaluate cross-channel interactions between CAM states and a continuous affect signal (via facial recognition).

Authors

• Elizabeth B. Cloude, Michigan State University

• Jingwei Wei, Tampere University

• Bianca Esmanhoto, Tampere University

• Muhterem Dindar, University of Oulu

• Manuel Ninaus, University of Graz

• Kristian Kiili, Tampere University of Technology