Yung, H. I., & Pass F. (2015). Effects of Cueing by a Pedagogical Agent in an Instructional Animation: A Cognitive Load Approach. Educational Technology & Society, 18 (3), 153–160.

153 ISSN 1436-4522 (online) and 1176-3647 (print). This article of the Journal of Educational Technology & Society is available under Creative Commons CC-BY-ND-NC 3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Journal Editors at ets-editors@ifets.info.

Effects of Cueing by a Pedagogical Agent in an Instructional Animation: A Cognitive Load Approach

Hsin I. Yung1* and Fred Paas2

1Graduate Institute of Technological & Vocational Education, National Taipei University of Technology, Taiwan // 2Institute of Psychology, Erasmus University Rotterdam, Rotterdam, The Netherlands // hcy103@mail.ntut.edu.tw //

paas@fsw.eur.nl *Corresponding author

(Submitted May 19, 2014; Revised November 27, 2014; Accepted December 12, 2014)

ABSTRACT

This study investigated the effects of a pedagogical agent that cued relevant information in a story-based instructional animation on the cardiovascular system. Based on cognitive load theory, it was expected that the experimental condition with the pedagogical agent would facilitate students to distinguish between relevant and irrelevant information, and therefore lead to higher learning outcomes and lower cognitive load than the control condition without a pedagogical agent. Based on 133 seventh-grade students as participants, the results confirmed the hypotheses, indicating that the pedagogical agent had a more positive effect on learning outcomes and resulted in a more favorable relationship between learning outcomes and cognitive load. The results are discussed in terms of theoretical and practical implications for designing instructional animation.

Keywords

Pedagogical agent, Cueing, Cognitive load Introduction In recent years, numerous studies have explored the effects of instructional animations on learning (for reviews see, Höffler & Leutner, 2007; Tversky, Morrison, & Bétrancourt, 2002). Animation is assumed to be particularly effective in the learning of complex dynamic systems, such as can be found in the domains of science and biology. Animations provide immersive external representations and are assumed to be effective for portraying visual changes of concepts, presenting implicit knowledge, and facilitating comprehension and learning (Scaife & Rogers, 1996; Tversky et al., 2002). However, animations can only be effective for learning if they are designed in such a way that they engage learners in processing its relevant parts and understanding the relations between those parts. Many animations contain both relevant and irrelevant parts, which may lead to learners spending part of their limited cognitive resources on processing irrelevant parts, leaving less cognitive capacity to process the relevant parts (Ayres & Paas, 2007a, 2007b). Tversky et al., (2002) have argued that animations may fail to improve learning because they are too complex or too fast to be accurately perceived. In addition, learners’ attention is automatically allocated to perceptually salient elements in an animation, for example, elements with a sudden onset/offset, elements that move or elements that change in color. Because these elements are not necessarily the most relevant elements in the animation, attentional resources can be wasted (Ayres & Paas, 2007a, 2007b; Tversky et al., 2002). Under these circumstances, researchers are challenged to find instructional techniques that support learners in focusing their cognitive resources on aspects of the animation that are relevant to the learning goal. Ayres and Paas (2007a, 2007b) have argued that animations are often less effective than static pictures, because of their intrinsic transient nature and poor design features, which impose high extraneous cognitive loads. One design feature that imposes a high extraneous cognitive load is related to the split-attention effect, which materializes when learners have to mentally integrate two physically separated sources of information, such as a diagram and text, before the learning task can be understood (e.g., Ayres & Sweller, 2005; Chandler & Sweller, 1992; Mayer & Moreno, 2003). However, the main reason for high extraneous load in animations (e.g., Ayres & Paas, 2007a, 2007b; Hegarty, 2004; Lewalter, 2003) is that learners are required to process current information, remember this information when it disappears and mentally integrate it with newly appearing information, and, when available, with information previously stored in long-term memory. Consequently, a substantial part of the limited working memory resources is focused on dealing with the demands of the presentational format, rather than on learning. Static visualizations do not have these problems, because information is continuously available, which prevents

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learners from having to hold the information in their working memory. This also explains why some instructional manipulations that reduce transient information in animations and the associated demands on working memory, such as some forms of user-interactivity (for an overview see, Wouters, Paas, & van Merriënboer, 2008), segmentation (for an overview see, Spanjers, van Gog, & van Merriënboer, 2010), attention cueing/cueing (for an overview see, de Koning et al., 2009), and adding a human movement component (Paas & Sweller, 2012; Wong et al., 2009) improve the effectiveness of animations. Literature review The pedagogical agent with cueing One way to provide instructional support in computer-based learning environments is by using an animated pedagogical agent (Atkinson, 2002; Baylor, 2009; Moreno, Mayer, Spires, & Lester, 2001). Pedagogical agents are human-like onscreen characters that provide hints and feedback as well as direct learners’ attention by using gesture, gaze, speech, or combinations of those modalities. In recent years, researchers have been focusing on the internal and external properties of pedagogical agents (Mayer, 2005; Moreno, 2007; Wouters et al., 2008). The internal properties of pedagogical agents relate to instructional strategies that promote learners’ cognitive engagement and reduce extraneous cognitive load, including providing verbal explanations, feedback or guidance. The external properties of pedagogical agents relate to their social and motivational features to encourage the learning process (Mayer & DaPra, 2012; Wang et al., 2008). The research on pedagogical agents has revealed a general positive effect on affective and cognitive variables. For example Moreno et al. (2001) compared a discovery-based learning environment containing an animated pedagogical agent (i.e., Herman the Bug) to a text-based version of the same environment and found higher levels of transfer performance, motivation and interest for the pedagogical agent condition. Atkinson (2002) found positive effects on transfer performance of a pedagogical agent that was incorporated in a computer-based learning environment designed to teach learners how to solve word problems. He concluded that an animated pedagogical agent can help optimize learning from examples. Lusk and Atkinson (2007) compared different levels of embodiment of a pedagogical agent in a worked example based learning environment on how to solve multi-step proportional word problems. They showed that the learning environment with the embodied agent resulted in better transfer performance than a voice-only condition without an agent. One way of directing learners’ attention that has been found effective for learning from animations is cueing. Cognitive load theory provides a strong framework for the effect of attention cueing in terms of learners’ limited cognitive resources (Sweller, 2010; Sweller, van Merriënboer, & Paas, 1998). Ayres and Paas (2007a) have proposed that attention cueing can be an effective method to reduce extraneous cognitive load by focusing learners’ attention to essential information in the learning task. According to Lowe and Boucheix (2011) cues can be categorized as internal or external. Whereas internal cues are embedded within the text or graphic, such as color, external cues, such as a verbal statement, are separated from the targeted information. Internal cues have greater effects to raise its visual contrast and attract learners’ attention (Boucheix & Lowe, 2010). Several studies that have been conducted in the context of cognitive load theory, have shown positive effects of cues that focus learners’ attention on the information that is relevant at a certain point in time on learning from animations (e.g., Amadieu, Mariné, & Laimay, 2011; Boucheix & Lowe, 2010; Mayer & Moreno, 2003). Cueing has been designed in a variety of visual and verbal forms including color coding (e.g., Kalyuga, Chandler, & Sweller, 1999) and picture labeling (e.g., Florax & Ploetzner, 2010) to guide learners in discriminating between relevant and irrelevant information. De Koning, Tabbers, Rikers, and Paas (2009) have classified three functions for attention cueing in learning. Firstly, a selective function to support learners in discriminating between relevant and irrelevant information. Secondly, an organizational function to support learners in indentifying structure and organization in the learning materials. Thirdly, an integrative function is to emphasize relations between information elements in the learning materials. Previous research on the use of pedagogical agents in multimedia learning has focused on user experience (Dehn & van Mulken, 2000), motivational effects (Baylor, 2009) and embodiment effects (Mayer & DaPra, 2012; Moreno, Mayer, Spires, & Lester, 2001). Only a few studies on pedagogical agents have focused on the pedagogical agent’s support of learner’s cognitive functions in learning. In addition, previous research has only considered affective measures or learning performance tests to measure the effects of the pedagogical agent. In this study we investigated

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the effects of cueing by a pedagogical agent on learning performance, cognitive load, and a composite measure of instructional efficiency. Research questions and hypotheses In this study students studied an animation about the blood circulation in the human heart with or without a pedagogical agent. It was investigated whether students’ learning from an animation with a pedagogical agent that supports the learner’s distinction between relevant and irrelevant aspects through cueing is more efficient than learning from an animation without a pedagogical agent. It was argued that the pedagogical agent with cueing would facilitate learners to identify relevant information, which would reduce extraneous cognitive load and leave more working memory resources to construct a cognitive schema (i.e., intrinsic cognitive load). Therefore, it was hypothesized that the participants in the experimental condition with the pedagogical agent would show a more favorable relationship between cognitive load during the learning phase and performance during the testing phase (i.e., higher instructional efficiency: Paas & van Merriënboer, 1993) than the participants in the condition without the pedagogical agent. Method Participants and design Participants were 133 seventh grade students (67 boys, 66 girls) with an average age of 12 years from a large junior high school at Taipei, Taiwan. All participants were volunteers. Participants were randomly assigned to two experimental conditions, in such a way participants with pedagogical agent condition, participants without pedagogical agent condition. The dependent variables were comprehension test performance, cognitive load and instructional efficiency. The experimental group received animation with the pedagogical agent while the control group received the same animation without the pedagogical agent. Materials The animation of the cardiovascular system was developed by a team consisting of an animation expert, an instructional designer, and a content expert. The content of the animation on the cardiovascular system was segmented into 23 separate screens and was adapted from a middle school biology textbook, which was written into a story titled “the adventure of the red blood cell.” It was expected that the use of a story format would stimulate students’ engagement with the materials. The animation story was based on three characters: red blood cell, white blood cell and the pedagogical agent. The pedagogical agent was developed using Maya and Adobe after effect (AE) software and was designed in the form of an instructor. The pedagogical agent was capable of exhibiting gestures such as waiving and pointing words in order to focus learners’ attention to important concepts. The story line of the animation was written in collaboration with two biology teachers of the school in which the experiment took place. The animation portrayed the process of the heart expanding when it fills with blood, the circulation of the blood through the heart, and the contraction of the heart. The story began with the red blood cell starting a journey and making friends with a white blood cell. While they traveled around the four chambers of the heart, the pedagogical agent joined the two characters, and told them important concepts of the heart and guided them to find the right path to finish their journey. The pedagogical agent, which was positioned to the right of the “adventure of the red blood cell” animation, directed the learner’s attention by deictic gestures, such as moving his hand for highlighting relevant information (see Figure 1a, 1b), and providing information, such as telling the red blood cell how to distinguish the difference between atrium and ventricle. It was expected that the visual cueing could guide learners’ visual attention to actively engage in knowledge construction and decrease the possibility that learners pay attention to irrelevant elements. During the adventure, the pedagogical agent acted like an instructor and explained fundamental concepts in the animation. The attention cueing strategy was expected to support learners to select essential information. Our main assumption was that applying the pedagogical agent with a red and a white blood cell in a story format would enhance learners’ understanding of the dynamic heart circulation, rather than just stimulate them to memorize concepts. It should be noted that the

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instructional content of the animation and the presented fundamental concepts were identical in the experimental and control condition. The only aspect that differed was the presence of the pedagogical agent in the experimental condition.

Figure 1a. Example of the pedagogical agent with explanations

Figure 1b. Example of explaining the main function of the red blood cell

Instruments Comprehension test The comprehension test evaluated participants’ understanding of the heart blood circulation. It consisted of 10 multiple-choice questions and asked participants to indicate the correct answer within four options. The questions were developed based on important concepts of the heart circulation system and process of the pulmonary circulation. An example question of the comprehension test, “please indicate whether the following option has the correct path of pulmonary circulation?”

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Participants could receive one point for each correct answer. For each correct answer, the participants received one point, otherwise they received zero points. A maximum of 10 points could be obtained on the comprehension test. Cronbach’s alpha for the comprehension test was 0.61. All comprehension questions were evaluated by three biology instructors with 10 years of teaching experience. Cognitive load measures The present study tested the effects of a pedagogical agent with cueing on cognitive load. Measurement of cognitive load was consisted two types of subjective measures: perceived task difficulty and perceived amount of invested mental effort (Paas, 1992). The five-point Likert scales that were used for measuring perceived task difficulty and effort ranged from very low to very high was adapted from Paas (1992). Using self-ratings of mental effort and perceived difficulty would provide a reliable and valid indication of cognitive load. The perceived task difficulty scale had a Cronbach’s alpha of .81. The perceived mental effort scale had a Cronbach’s alpha of .80. According to Paas and van Merriënboer (1993, 1994), the perceived amount of invested mental effort and the perceived task difficulty can be considered as indices of cognitive load. Although the individual measures of cognitive load, such as the perceived amount of invested mental effort, can be considered important to determine the power of different instructional conditions. Paas and van Merriënboer (1993) have argued that a meaningful interpretation of a certain level of cognitive load can only be given in the context of its associated performance level and vice versa. They developed a computational approach to combine measures of mental effort with measures of the associated primary task performance to compare the instructional efficiency of instructional conditions. Since then, a whole range of studies has successfully applied this method or an alternative method combining effort and performance (for an overview see, Paas et al., 2003). In this study, we used ratings of task difficulty as a measure of cognitive load and ratings of mental effort in combination with performance measures to calculate instructional efficiency. Procedure Participants were randomly assigned to either experimental condition (n = 64) or control condition (n = 69). The experiment was run in a group session with 35 participants in the schools’ computer classrooms. Each student worked individually at a computer. At the start of the experimental session, participants were provided with headphones and asked not to talk to the other participants. Subsequently each participant had about 5 minutes to work on the tutorial before the start of the learning phase, which had a maximum duration of 40 minutes. They were instructed to watch the animation carefully in order to comprehend the material. After the experiment, participants were asked to rate the perceived cognitive load on a rating scale. Subsequently, they received the comprehension test without having access to the animation. The whole experiment lasted approximately 45-50 minutes. Results Analyses of variance (ANOVAs) were performed for the mean scores of each dependent variable. Table 1 shows the mean scores and corresponding standard deviations of the comprehension test scores, cognitive load, and the instructional efficiency scores for students in the experimental and control condition.

Table 1. Means and standard deviations of comprehension performance and cognitive load for students in the experimental and control condition

Experimental group (N = 64) Control group (N = 69) M SD M SD

Comprehension 6.67 1.28 6.01 1.20 Cognitive load 3.02 0.94 3.03 0.88 Instructional efficiency 0.24 1.20 -0.24 0.94

An ANOVA for comprehension test performance revealed a significant effect of the pedagogical agent, F(1, 129) = 8.772, p = .004, In line with our hypothesis, the pedagogical agent with cueing condition had a positive effect on

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learning. However, the ANOVA for the mean ratings of task difficulty and mental effort did not reveal a significant difference between the conditions, F(1, 129) = 0.665, p = .416. Instructional efficiency Paas and van Merriënboer (1993) developed instructional efficiency formula by combining mental effort and learning performance. Instructional efficiency was calculated using learning performance and mental effort. By standardizing participants’ mental effort and learning performance scores to z-scores, this formula can reveal important information on the efficiency of instructional conditions that is not necessarily reflected by each of those measures alone. Consistent with the original method, we used ratings of mental effort in combination with performance measures to calculate instructional efficiency.

The result showed that there was a main effect of the pedagogical agent on instructional efficiency, F(1, 129) = 5.53, p = .020. It indicated that the participants in the pedagogical agent conditions exhibited a more favorable relationship between the amount of mental effort invested in the learning phase and the performance in the test phase. Conclusion and discussion This study investigated the effect of a pedagogical agent with cueing on students’ learning performance, cognitive load, and instructional efficiency. It was hypothesized that directing learners’ attention to the relevant aspects of the task by the pedagogical agent would decrease extraneous cognitive load, improve learning, and lead to a more favorable relationship between learning effort and test performance (i.e., instructional efficiency). The results indicated that cueing by the pedagogical agent had a positive effect on learning performance and instructional efficiency. No significant differences in cognitive load were found. This finding might have been caused by the fact that only the total cognitive load was measured, which makes it impossible to draw conclusions on the different types of cognitive load, i.e., intrinsic, extraneous and germane load that underlie the overall load (Paas et al., 2003). More specifically, based on the main effects found for pedagogical agent it can be assumed that the cognitive capacity that was freed by the pedagogical agent (by decreasing extraneous load) was used for activities that fostered learning (i.e., imposed a germane load) reduction in extraneous load. Unfortunately, there are currently no methods available for measuring the specific types of load that constitute total load (but see, Leppink, Paas, van der Vleuten, van Gog, & van Merriënboer, 2013). The results regarding learning and learning efficiency indicated that the cueing of the pedagogical agent was strong enough to direct learners’ attention in such a way that they could acquire an adequate mental representation the blood circulation in the human heart. In general, learning from instructional animation is a challenge for learners because they need to extract relevant information from transient information to construct adequate mental representations. Using cueing is considered critical in the instructional design of animation to guide the learners’ attention and enhance their cognitive processing of the crucial elements in animation (De Koning et al., 2009). According to Fischer and Schwan (2010), if cueing only underlines specific parts of an animation and uncued parts of the animation are still required for integrating the conceptual structure of the materials, students’ learning improvement may be limited. Because, the blood circulation in the human heart is a dynamic system, which consists of a range of factors with causal relationships, the cueing of the pedagogical agent in the current study might not have been optimal. Future research is needed to investigate which aspects of the dynamic system can best be cued. It is clear that the positive effects of the pedagogical agent on comprehension test performance and instructional efficiency cannot be generalized based on the results of this study. In the present study a very specific story-based format of a pedagogical agent was used. Although, such a format might be attractive for the young population that was used in this study, it is not clear whether adolescents and adults would show the same effects. In addition, it is not clear whether other formats of a cueing pedagogical agent would lead to similar results in a similar population. It is also not clear whether this specific type of pedagogical agent would work in other domains. Therefore, future

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research is needed to investigate the effectiveness of different story-based, cueing pedagogical agents in different populations and in different domains. In the present study, we only focused on the attention guiding function of cueing. Therefore, it would be interesting for future studies to explore other functions of cueing, such as the organizational function, and their effects on cognitive load and learning. From a practical point of view, the study highlighted the positive effect of the pedagogical agent with cueing as an effective instructional strategy for reducing the complexity of animation. Implementing the cueing effect in the instructional design for learning from animation of the complex dynamic system may support learners in discriminating between relevant and irrelevant information in order to construct a mental representation of the system. Although the study is not unique in terms of investigating effects of cueing, it presents a new approach to the instructional design of animation in biology domain, based on the theoretical framework of cognitive load. In conclusion, using cueing with the pedagogical support can be an effective strategy to enhance learning from animation. References

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