The Effects of Positive and Negative Mood on Cognition and Motivation in Multimedia Learning Environment

Introduction In a typical e-learning and computer-based learning environment, learners are confronted with information presented in the format of text, narration, animation, and graphics. Hence, learners engage in the cognitive process known as multimedia learning (Mayer, 2008). The cognitive process of multimedia learning describes an information processing in which learners select relevant visual and verbal materials, organize these visual and verbal mental representations in coherent structures in working memory, and integrate the visual and the verbal mental representations with one another and with prior knowledge (Mayer, 2008). Complementing this process model, the cognitive load theory (CLT) provides a capacity model of multimedia learning (Kalyuga, 2011). The CLT model highlights the inherent limitation of working memory and describes three types of cognitive load: intrinsic, germane, and extraneous. Intrinsic cognitive load is the inherent level of difficulty associated with a specific instructional topic. Germane load defines the amount of mental effort invested by the learner in comprehending the materials, whereas extraneous load describes processing demands of information that are not directly related to the learning task (Kalyuga, 2011). Multimedia learning design has been primarily based on cognitive models such as dual-coding theory and cognitive load theory (Mayer & Moreno, 2003). However, Moreno's (2006) Cognitive-Affective Theory of Learning with Media extended the framework by integrating emotional and motivational factors in multimedia learning design. According to this framework, the process of multimedia learning is mediated by emotional factors that influence learner's willingness to invest mental effort to the learning process (Park et al., 2015). In addition, the framework assumes that multimedia learning process is mediated by learner's metacognition such as cognitive styles and abilities (Moreno & Duran, 2004; Park et al., 2015). Hence, a learner's working memory, motivation (i.e., germane load), and cognitive style is affected by his or her emotional states. Recent studies have attempted to validate the importance of emotional factors in the design of multimedia learning environments. In a seminal work by Um et al. (2012), it was demonstrated that learners who were induced with positive mood outperformed learners who were induced with neutral mood in comprehension and transfer tests. The researchers also showed that positive emotion can be transferred to learners through interface designs, such as anthropomorphism and bright colors. In the attempt to replicate these findings, Plass et al. (2014) tested the effects of color and anthropomorphism on learners' emotion, motivational and cognitive outcomes. It was shown that positive emotional design in multimedia learning environments enhanced positive emotion, motivation, and comprehension. Moreover, positive emotional design in a multimedia interface significantly reduced learners' perception of task difficulty. However, the study failed to replicate the facilitating effect of positive emotional design on transfer scores. Mayer and Estrella (2014) tested the effects of positive emotional design in multimedia learning environments. Mirroring earlier findings (Plass et al., 2014; Um et al., 2012;), results from their experiments showed that positive emotional design via anthropomorphism and appealing colors enhanced retention scores. In an eye tracking experiment, Park et al. (2015) revealed that learners who were induced with positive mood had higher learning outcomes in comprehension and transfer tests and showed longer fixation durations on the relevant information of the multimedia learning environment than learners who were induced with neutral mood. Their findings empirically indicate that emotional state influences cognitive and attentional processing in multimedia learning. Moods in multimedia learning environment There are different constructs when defining "emotion" (Ekkekakis, 2012; Russell, 2003). …