Mood-affect congruency. Exploring the relation between learners’ mood and the affective charge of educational videos
Introduction
School teachers use educational videos as a welcome change in daily teaching. According to Hoogerheide, Loyens, and van Gog (2016), a wide range of students watch instructional videos for formal and informal learning purposes on diverse online platforms. These videos are consulted in order to get deeper understandings in relevant learning topics or even replace classical face to face learning activities. Especially in the World Wide Web, numerous video lectures (Traphagan, Kucsera, & Kishi, 2010), knowledge clips (Day, 2008), and demonstration videos (Ayres, Marcus, Chan, & Qian, 2009) are available and used frequently. Instructional videos are also becoming more popular in school (Spires, Hervey, Morris, & Stelpflug, 2012). Educational videos offer numerous design possibilities in order to create an interesting and appealing learning environment (e.g., Papa et al., 2000) and are an ideal multimedia applications to deliver emotions and information (Chen & Sun, 2012). Especially, educational videos which induce positive emotions are found to significantly influence learning-relevant cognitive mechanisms like memory, attention and perception (Izard, 1993, 2007; Lewis, 2005; Lewis, Haviland-Jones, & Barett, 2010). In addition to the affective influence due to the emotional charge of an educational video, learner always have a basic mood when entering the learning situation. This mood influences learning processes as well (Brose, Schmiedek, Lovden, & Lindenberger, 2012; Um, Plass, Hayward, & Homer, 2012). The current study aimed to investigate the impact of emotional charge of educational videos on cognitive and learning processes. Furthermore, an interaction between the current mood of the learner and the affective charge of the video will be examined.
Two major frameworks were developed in order to describe learning processes with various multimedia environments. The Cognitive Theory of Multimedia Learning (CTML; Mayer, 2014) describes how information is selected, organized and integrated. Information get processed via a verbal and a pictorial channel and are organized into coherent models within the working memory. Afterwards both models have to be integrated and transferred to the long-term memory. However, knowledge transfer to the long-term memory is mediated by the amount of domain-specific prior knowledge (Kalyuga, Ayres, Chandler, & Sweller, 2003). According to the Cognitive Load Theory (CLT; Kalyuga & Singh, 2016), two cognitive processes can be identified during learning: (1) Intrinsic cognitive load (ICL) is defined as the processing of the relevant information which is affected by the element-interactivity and prior knowledge, (2) extraneous cognitive load (ECL) is defined as processing of learning-irrelevant information which are caused by the design of the learning environment.
However, affective variables are not included in the CLT and CTML. These variables have proven influences in learning with media (e.g., Plass & Kaplan, 2016). Learning with media is always associated with an affective quality. Affective charge of a learning environment evokes an emotional episode within the learner which can be categorized in terms of valence (positive vs. negative) and arousal (activating vs. deactivating; Plass & Kaplan, 2016). Watching educational videos can cause such an emotional episode (Becher, 1999). Cognitive processes can be enhanced and learning processes are fostered especially by evoking an emotional episode with positive valence (emotion-as-facilitator hypothesis; Park, Knörzer, Plass, & Brünken, 2015). In contrast, emotions can also lead to distraction and inhibit learning processes (emotion-as-suppressor hypothesis; Park et al., 2015). Therefore, extended frameworks, like the Cognitive-Affective Theory of Learning with Media (CATLM; Moreno & Mayer, 2007) and the Integrated Model of Cognitive-Affective Learning with Media (ICALM; Plass & Kaplan, 2016) were developed in order to implement affective and motivational influences in learning with multimedia. Both theories intertwine cognitive and affective processes at various stages of the learning process. Information is rather integrated as an affective-cognitive mental model in contrast to a purely cognitive mental model.
When arguing about effects of affect during learning in terms of cognitive processes and learning success, two perspectives have to be taken into account. First, the current mood of the learner may influence the learning process. Second, the learning environment consists of its own emotional charge which may influence learning as well. Therefore, mood and emotions must be distinguished from another by definition. Mood can be defined as long-lasting affective states which are rather diffuse and often labeled as “good”, “neutral”, or “bad” (Ekkekakis, 2012). In comparison with concrete emotions, mood can be described as enduring and of lower intensity (Frijda, 2009). Emotional episodes are of stronger intensity and shorter duration as moods (Shuman & Scherer, 2014).
The current mood may deplete resources in working memory (Brose et al., 2012; Mitchell & Phillips, 2007). Negative and positive mood prime learning-irrelevant thoughts. Therefore, working memory capacity is limited because of the split of attention between the learning task and mood processing (Brand, Reimer, & Opwis, 2007; Seibert & Ellis, 1991). Certainly, a positive mood may enhance motivation and engagement towards the learning task (Isen & Reeve, 2005). In particular, intrinsic motivation is enhanced by positive mood (e.g., Um et al., 2012). Additionally, positive mood is associated with a higher attention span and a global processing of information (Fredrickson & Branigan, 2005), and increased creativity and a higher usage of problem solving skills (Isen, 1999) as well as enhanced learning outcomes (Liew & Su-Mae, 2016). However, negative mood may also increase motivation and effort (Forgas, 2013). Forgas pointed out that a negative mood leads to a more systematic and analytical processing of information and thus, may foster learning.
In addition to the mood of the learner, the learning environment consists of its own emotional charge. Positive or negative emotions can be induced via various techniques (e.g., decorative elements; Schneider, Nebel, Beege, & Rey, 2018). Positive emotional states were shown to foster learning outcomes in contrast to neutral (e.g., Park et al., 2015) and negative states (e.g., Heidig, Müller, & Reichelt, 2015). Positive emotions direct the attention towards the learning material, while more cognitive resources can be used in order to process relevant information (Huk & Ludwigs, 2009). Creative thinking is enhanced in emotionally positive learning environments in contrast to negative learning environments and therefore, learning might be fostered (Nadler, Rabi, & Minda, 2010). Furthermore, emotions are strongly connected to motivational variables (Heidig et al., 2015). A positive emotional charge of a learning environment led to an increased intrinsic motivation and therefore, learning was fostered. In summary, there are several evidences that a positive mood of the learner as well as a positive emotional charge of the learning environment is beneficial for learning in comparison with a neutral mood or a learning environment which does not induce emotional episodes.
The mood of the learner and the emotional charge of the learning environment were rarely investigated separately. Nevertheless, there might be interaction effects between these two constructs. A thoroughly investigated effect which combines the effects of mood of the learner and the emotional charge of the learning material is the so called mood congruency effect (e.g., Bower, 1981; Kim & Pekrun, 2014; Schwarz, 2000). This effect implies that learning with congruent mood of the learner and emotional charge of the learning material lead to a deeper processing during learning process and a better recall of information (Bower, 1981). For example, Bower, Monteiro, and Gilligan (1978) investigated the mood congruency effect by inducing emotions by two word lists at different times either with positive or negative emotions. Words with an emotional content which matched with the emotional state of the learner during the recall were significantly recalled more often. The mood congruency effect is often explained by considering associative networks. According to the CLT, memory is constructed by concepts and schemata (Kalyuga & Singh, 2016). As mentioned, emotions are inextricably linked to events of daily life and therefore, the objective knowledge is not stored free from emotions in the long-term memory. If a mood is experienced, mood-related concepts are more easily available in the working memory (Levine & Pizarro, 2004). In consequence, storage and recall in a material-congruent affect facilitates the access to information because affect-congruent stimuli receive a stronger activation than incongruent stimuli (Fiedler, Nickel, Asbeck, & Pagel, 2003). Especially, a positive emotional charge of the learning material in combination with a positive mood of the learner is conducive for learning (Mayer, Gayle, Meehan, & Haarman, 1989), whereas arousal of the learner or the learning material (which can be defined as activation; Plass & Kaplan, 2016) does not seem to influence the mood congruency effect (Mayer et al., 1989; Varner & Ellis, 1998).
The current study aims to investigate two knowledge gaps which result from the reviewed literature: (1) Learning effects in the field of emotional design were almost exclusively determined on pictures and websites (e.g., Heidig et al., 2015; Schneider, Nebel, & Rey, 2016). In contrast to this research history, Chen and Sun (2012) pointed out that videos are an ideal multimedia application to deliver emotions and information. Therefore, the current study used educational videos to implement a positive emotional charge (which is henceforth called positive charge) and a neutral emotional charge (which is henceforth is called neutral charge) in order to investigate if previous design recommendations and effects can be transferred to audio-visual media. (2) The mood congruency effect is dealing with learning processes but was mostly examined in the context of clinical psychology (e.g., Clark & Teasdale, 1982; Teasdale & Taylor, 1981). Furthermore, the mood congruency effect describes the emotional fit between the learning and recall situation and thus, indicates that an emotional congruence is crucial for learning. The current study extends this effect by investigating the emotional fit and interaction between the mood of the learner when entering a learning situation and the emotional charge of the learning material.
Learners got a mood induction (positive vs. neutral) at the beginning of the investigation and then received an educational video with congruent or incongruent emotional charge (positive vs. neutral). According to the reviewed literature, especially a positive mood of the learner is beneficial for learning in comparison to neutral mood (e.g., Fredrickson & Branigan, 2005). Still, empirical results showed that a positive mood can be harmful for learning as well (e.g., Brand et al., 2007). In order to consider both effect directions, two contrasting hypotheses are postulated:
H1a: Learners with a positive mood will achieve higher learning outcomes than learners with a neutral mood.
H1b: Learners with a positive mood will achieve lower learning outcomes than learners with a neutral mood.
In particular, recent research results pointed out that a positive charge of a learning environment is beneficial for learning in contrast to a control group (Schneider et al., 2016; 2018). Therefore, a positively charged educational video should be beneficial for learning. A hypothesis was formulated in order to check if existing results regarding the emotional design of learning environments are transferable to audiovisual media:
H2: Learners watching an educational video with a positive charge will achieve higher learning scores than learners watching the educational video with a neutral charge.
The mood congruency effect indicates that emotional congruence during learning elicits a positive impact on learning outcomes (e.g., Fiedler et al., 2003). The current study broadens this effect in order to investigate not only the emotional congruence between learning and recall but also the emotional congruence between the general mood of the learner and the emotional charge of the learning environment.
H3: Learners in the conditions with congruence between their mood and the emotional charge of the video will achieve higher learning scores than learners in the conditions without congruence between their mood and the emotional charge of the video.
Furthermore, research has shown that cognitive processes are influenced by emotional design factors and the mood of the learner (e.g., Isen & Reeve, 2005). Finally, the detailed effect of mood induction before the learning situation and the emotional design factors in educational videos on the emotional response of the learner should be explored. Therefore, these variables will be additionally examined in the current experiment.
Section snippets
Participants & design
Overall, 165 students in grade ten (N = 78), eleven (N = 49) and twelve (N = 35) from a secondary school in Thuringia (Germany) participated in this experiment. Because of incomplete data records, three students had to be excluded from statistical analyses. The remaining 162 participants (58% female; age: M = 16.49, SD = 0.96) were randomly assigned to one cell of a two (mood induction: positive vs. neutral) × two (emotional charge of the videos: positive vs. neutral) factorial between-subjects
Results
In order to investigate differences between the experimental groups, multivariate analyses of covariance (MANCOVAs) and univariate analyses of covariance (ANCOVAs) were conducted. For all analyses the experimental factors mood induction (positive vs. neutral) and the charge of the video (positive vs. neutral) were used as independent variables. Since the variable “grade” significantly differed among the experimental groups, this variable was used as covariate in all analyses. Test assumptions
Discussion
Results demonstrated that the induced mood of the students did not influence learning outcomes according to the conducted ANOVAs. Indeed, a path analysis revealed that a positive mood affects the learning scales rather negatively. Therefore, H1a and could not be confirmed and a small evidence for H1b was found. Furthermore, students in the condition with the positive video outperformed students in the condition with the educational video with neutral charge regarding retention performance. This
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