Abstract
This study applied Berlyne’s theory of aesthetic preference to these websites: a theory that suggests that people prefer a medium level of stimuli to a low or high level of stimuli. A total of 150 children between 10 to12 years old were involved.
The results showed that overall the children preferred websites that had a medium level of visual complexity to those that had a high or low level of visual complexity. Thus the results supported Berlyne’s theory.
Author Keywords
Learning motivation; visual aesthetic; VisAWI; IMMS; visual complexity; website; children.
ACM Classification Keywords
HCI, Design, Experimentation, Human Factors
Introduction
Recent years have seen an increasing number of children’s educational institutions introducing e-learning websites. In order for these websites to be useful they need to be informative; they also need to be usable and attractive. However, while much research has been conducted into the usability of websites (e.g., [1]) little has been carried out into their visual appeal. Research that has been undertaken includes studies into overall impressions [2], the importance of aesthetics with respect to the layout of the website design [3], and the effect of colour on emotions [4]. Research has also been conducted into users’ preferences with regard to perceived visual complexity in websites; however, most of this work has involved adults [5]. As such, little is known of children’s appreciation of visual aesthetics with respect to perceived visual complexity in websites and whether varying levels of visual complexity affect their learning motivation.
Given the various aspects of visual aesthetic influence, the perceptions of websites, and the dearth of research conducted in this area [6], the goal of the current study is to provide a better understanding of the relations between visual complexity, visual aesthetics and learning motivation, and their effect on children’s websites. In particular, it addresses the following question: what levels of visual complexity (high, medium, and low) can enhance children’s appreciation of visual aesthetics, and their learning motivation on learning websites? The findings are expected to be applicable in a wide range of situations in which researchers, practitioners and children’s educators’ needs to design better websites to motivate children’s learning.
STUDY BACKGROUND
Visual complexity and visual aesthetics
Aesthetics have been found to play an important role in web design, product design and learning environments. Tractinsky (2013) defined the term ‘visual aesthetics’ as ‘an artistically beautiful or pleasing appearance on the visual senses’ [6]. There have been various approaches to the study of aesthetics in HCI and these have resulted in a number of theories [7], one of which was Berlyne’s psychobiological theory of aesthetics [8]. This theory proposed that aesthetic preference is related to the arousal potential to stimulus arousal potential and that medium levels of arousal are preferred to low and high levels. Berlyne argued that arousal potential was a function of a number of different ‘collative variables’ one of which was perceived visual complexity.
Since Berlyne formulated his theory, a number of researchers have explored perceived visual complexity in relation to aesthetic preference and websites. In an experiment that involved websites taken from domains such as museums and educational establishments, Wang and Bowerman (2012) found that children preferred websites that had a medium level of perceived visual complexity to websites that had either a low or high level of visual complexity [9]. Accordingly, their results supported Berlyne’s theory. Geissler, Zinkhan and Watson (2006) also tested Berlyne’s theory. In their study 360 students evaluated home pages that had been specifically created using Netscape Navigator Composer to have different levels of visual complexity. The researchers discovered that the students responded more favourably to the home pages that had been created with a medium level of perceived visual complexity than to those that had been created with a high or low level of perceived complexity [10]. In other words, the researchers found that Berlyne’s theory was supported.
However, it should be noted that not all researchers have found that the findings of their experiments support Berlyne’s theory. Tuch, Bargas-Avila, Opwis and Wilhelm (2009) conducted an experiment that involved participants evaluating home pages. Participants were shown a number of home pages of varying levels of perceived visual complexity on a computer screen and, amongst other things, were asked to rate each one for the pleasure it elicited. The researchers found a negative linear relationship between perceived visual complexity and pleasure. Thus, the data did not support Berlyne’s theory [11].
The above paragraphs illustrate that there is a lack of consistency in the literature with respect to results that relate to visual complexity: some experiments support Berlyne’s theory while others do not. This suggests that more research is needed in this area. Therefore, we hypothesize that:
H1: Different levels of visual complexity in websites will impact on children’s appreciation of the visual aesthetics.
H2: Children will be influenced by different factors of visual aesthetic with regard to websites.
Learning motivation, aesthetics and visual complexity
Motivation is recognised as being an important component of learning. Pintrich (2003) explains that the word ‘motivation’ comes from the Latin word ‘movere’ meaning ‘to move’. Hence, motivation can be explained as that which moves (that is, incites) people to behave in a certain way [12]. In psychology, a distinction is made between intrinsic and extrinsic motivation. Intrinsic motivation is that which drives people to do something because it is inherently satisfying; whereas extrinsic motivation is that which drives people to do something because it leads to a separable outcome, such as a work promotion, money or grades [13].
Ryan and Deci (2000) stated that intrinsic motivation exists between people and activities[13]. People were intrinsically motivated when the activities were interesting, because they gained the satisfactions from intrinsically motivated task engagement. Thus the reward was in the activity itself. Researchers investigated what task characteristics made an activity interesting. Renninger, Hidi and Krapp (1992) suggested that competence and control, interest and intrinsic motivation were the key ways to motivate students to learn. Research on interestingness has shown that making learning tasks and materials interesting results in higher levels of cognitive engagement and higher levels of achievement [14]. Making learning materials aesthetically appealing was assumed to be one way to make them interesting. More support was provided by an empirical study by Zain, Tey and Goy (2007) which suggested that aesthetics impact upon children’s learning motivation; they also found that children are more motivated by web-pages with good aesthetics than web-pages with poor aesthetics [17]. Thus, we hypothesize that:
H3: There is a relationship between children’s visual aesthetic appreciation of a website and their learning motivation.
A considerable body of evidence shows that there is a relationship between the visual complexity of an object and an individual’s affective aspects such as visual aesthetics. Among these relations with visual aesthetics are the numbers of elements in art pictures [18], the layout of websites [11], and the visual complexity of virtual actors [19]. Thus, following on from the previous discussion, learning materials with appropriate levels of visual complexity are likely to enhance a person’s intrinsic learning motivation. Chang, Lin and Lee (2005) provided research evidence to support this idea. They conducted an experiment to explore the different levels of visual complexity and learning motivation in relation to English learning for young children, and their results showed that an image with a higher level of visual complexity enhanced their learning motivation [20]. Thus, a hypothesis was formed as follows:
H4: Different levels of visual complexity in websites will have an impact upon children’s learning motivation.
Method
Participants
A total of 150 children participated in the experiment (68 boys and 82 girls). They had a mean age of 12.3 years (SD=.39). They all attended the same school in Taichung, Taiwan. All the children used computers regularly both at home and at school. They all had computer lessons at least twice a week, played computer games and used the Internet. After completing the experiment, each participant received a small toy for his/her participation.
Experimental design
The experiment employed a single factor design. The independent variable was visual complexity, had three levels: high, medium and low. There were two dependent variables, which were visual aesthetics and learning motivation. Visual aesthetics had four factors: simplicity, diversity, colourfulness and craftsmanship. Learning motivation had three factors: attention, relevance and confidence.
Materials
The experiment involved each child examining a set of three web-pages with the same level of visual complexity, designed by the experimenter. Children were randomly assigned to one of those three levels of visual complexity. In total three sets were created: a set with a high level of visual complexity, a set with a medium level of visual complexity and a set with a low level of visual complexity. In addition to these three sets a further set was created to help the children familiarise themselves with the experimental procedure and their equipment prior to the experiment commencing. In this paper, this familiarisation exercise has been called the ‘practice trial’.
The three sets of web-pages that were used in the experiment were on the topic of nutrition. The words in each set of web-pages were identical and were adapted from a text book. While the three web-pages in each set could have been constructed as one long web-page.
The three different levels of complexity were created with reference to the work of Geissler, Zinkhan and Watson (2006) [10]. The researchers found that the more images, visible links and Top Left Corners (TLCs) that a web-page contained, the more visually complex people perceived it to be. The visible links when clicked took the children to The Concise Chinese Dictionary online. It should be noted that Geissler et al. found that the number of words a web-page contained also affected its visual complexity. However, as the experimenter wanted all the children to read the same information, the number of words in each set was the same.
Below each web-page was a number of simple multiple choice questions. These were included to ensure that each child read the text.
Procedure
The study was carried out in a computer lab with groups of 15 participants in 10 sessions. Each child had his/her own computer and worked alone. To ensure that everybody was able to use his/her equipment properly and knew what he/she would be doing, each session started with a practice trial. The trial involved each child viewing a set of two web-pages and answering two multiple-choice questions about each one. The questions, which were straightforward (for example, ‘What kind of vitamin can keep our bones healthy and strong?’), were written on-screen below each web-page and required the mouse to be clicked to answer them. When the experimenter was content that everybody could use his/her equipment satisfactorily, the experiment started. The experiment involved each child viewing a set of three web-pages designed by the experimenter and answering the questions below them; all the web-pages seen by each child were of the same level of visual complexity (high, medium or low). The children were told that they could take as long as they wanted to answer the questions and that they could move from one web-page to the next using the ‘Arrow’ button and should click the ‘Done’ button when finished. Each set of three web-pages (the high level of visual complexity set, the medium level of visual complexity set, and the low level of visual complexity set) was viewed by fifteen 4th grade children and fifteen 6th grade children.
Once everybody had completed the activity a questionnaire was distributed. Each question was explained and the children completed the questionnaire independently.
The questionnaire
In the experiment a questionnaire was used to collect information from participants. The questionnaire, which was written in Chinese, had three parts. The first part asked children to rate the visual aesthetics of the web-pages they viewed using 7 point Likert scales. The scales ranged from 1 (strongly agree) to 7 (strongly disagree). It used 16 questions (which were modified slightly so the children could understand them more easily) taken from the questionnaire called the Visual Aesthetics of Website Inventory (VisAWI) constructed by Moshagen and Thielsch (2010) [21]. The VisAWI was used to assess the perception of visual aesthetics, which had four different factors: simplicity, diversity, colourfulness and craftsmanship.
The second part of the questionnaire measured learning motivation based on the Instructional Material Motivational Survey (IMMS), which was developed by Huang et al. (2006) in a study that looked at motivation in computer-based learning[22]. It also employed 7- points Likert scales to capture participants’ levels of agreement with statements about the web-pages’ learning motivation. Three different factors were explored: attention, confidence, and relevance.
The final part of the questionnaire collected demographic information. It asked children to state their age, gender, reasons for using computers and the amount of time they used a computer each week.
Instrument reliability
In order to ensure construct validity, the items in the visual aesthetic questionnaire (VisAWI) were reduced from the initial 18 items to 16 due to a low loading, one item from the simplicity factors and one item from the craftsman factors. In addition, the learning motivation questionnaire (IMMS) was reduced from the initial 20 items to 14 items. All of the satisfaction factors (six items) were dropped because they exhibited low item loading. The attention factors retained six items, relevance factors retained three items and confidence factors retained five items. There were thirty items used in this experiment.
Construct reliability was assessed using Cronbach’s ” -value for both visual aesthetic and learning motivation questionnaires. After the removal of invalid items, the Cronbach’s ” for visual aesthetics indicated that the scales used for simplicity scored 0.81, for diversity 0.77, for colourfulness 0.78, and for craftsmanship 0.74. The reliability of learning motivation for attention was 0.86, for relevance 0.81, and for confidence 0.86. Nunnally (1978) recommends that the Cronbach’s ” reliability of the scale should be greater than 0.7 for items to be used together as a construct[23]. The constructs of both questionnaires passed the construct reliability test in this study.
RESULTS
A one-way ANOVA was carried out to evaluate the effect of visual complexity in terms of differences in mean scores, and Scheff” post-hoc comparison was used to test for differences between the various levels of visual complexity. The mean and standard deviation for the rating of visual aesthetic factors and learning motivation were illustrated in Table1.
Table1’Descriptive statistics for visual aesthetic factors, learning motivation, and visual complexity (n=150)
M SD M SD
Overall aesthetic Overall motivation
High 4.31 .49 High 4.26 .33
Medium 4.37 .65 Medium 4.30 .33
Low 3.93 .56 Low 4.01 .27
Simplicity Attention
High 4.28 .49 High 4.31 .60
Medium 4.51 .65 Medium 4.28 .73
.73
Low 4.20 .56 Low 4.05
Diversity Relevance
High 4.28 .67 High 4.18 .55
Medium 4.36 .62 Medium 4.35 1.37
Low 3.92 .44 Low 3.99 .67
Colourful Confidence
High 4.29 .76 High 4.38 1.09
Medium 4.21 .54 Medium 4.32 1.32
Low 3.91 .54 Low 4.04 .61
Craftsman
High 4.28 .66
Medium
Low 4.20 .70
4.07 .44
Simplicity
The results showed that a main effect for the different levels of perceived visual complexity was significant, F (2, 144) =5.219, p=.006, ”2=.068. The simplicity factor ratings for the different levels of visual complexity were the medium level of perceived visual complexity, followed by the high level of perceived visual complexity, and last, the low level of perceived visual complexity.
Visual aesthetic factors
Diversity
The results showed that a significant main effect for the different levels of perceived visual complexity, F (2, 144) =6.557, p=.002, ”2=.083, The diversity factor ratings for the different levels of visual complexity were the medium level of perceived visual complexity, followed by the high level of perceived visual complexity, and last, the low level of perceived visual complexity.
Colourfulness and craftsmanship
Analysis of variance showed that the different levels of perceived visual complexity were significant, F (2, 144) =6.284, p=.002, ”2=.080. The colourfulness factor ratings for the different levels of visual complexity were the high level of perceived visual complexity, followed by the medium level of perceived visual complexity, and last, the low level of perceived visual complexity.
No different levels of visual complexity were identified for craftsmanship factors.
Learning motivation
Attention
There was a significant difference between different levels of visual complexity and attention factors, F (2, 144) =4.162, p=.019, ”2=.104. The attention factor ratings for the different levels of visual complexity were the high level of perceived visual complexity, followed by the medium level of perceived visual complexity, and last, the low level of perceived visual complexity.
Relevance and Confidence
No effect of different levels of visual complexity was found for relevance factor and confidence factor ratings.
Correlation between visual aesthetics and learning motivation
Pearson’s correlation analysis was used to identify the correlation between visual aesthetic ratings and learning motivation. The results showed a positive correlation between visual aesthetic ratings and learning motivation, r=.217, p=.008. There was also a significant correlation between diversity factors and confidence factors, r=.211, p=.010.
DISCUSSION
Some other work has already been done on the question of which visual aesthetics characteristics affect evaluations of website design [6]. In this study we examined some of those characteristics, and explored the visual aesthetics taking into account four factors: simplicity, diversity, colourfulness and craftsmanship. According to Moshagen and Thielsch (2010) [24], Simplicity refers to the traditional interpretation of aesthetic factors, communicating a sense of well organised layout and good proportions, which are highly correlated with usability. The other aesthetic factor was diversity, which associated with artistic point of view, and represented creativity, fascination and original design. Clearly, the websites with diversity related visual aesthetics interfaces may have produced positive reactions in users, and given the interface a sense of novelty. However, when a website is designed without simplicity aesthetic factors in mind, users cannot find information easily, and this leads to basic usability problems. Thus, some general advice can be given. They should balance their websites, incorporating creativity and originality, but at the same time keeping the layout well organised and well proportioned.
The relationship between a user’s appreciation of a website’s visual aesthetics and his/her learning motivation was also investigated in this study; the data indicated that visual aesthetics play an important role in learning motivation (H3). The finding that visual aesthetics and learning motivation are correlated provides statistical support for guidelines for learning with multimedia, which were previously based on a theoretical perspective[15]. This result also supported those of an empirical study by Zain, Tey and Goy (2007) which suggested that aesthetic websites can increase children’s learning motivation[17].
Conclusion
This study contributes to the limited body of work that relates to the designing of websites that enhance children’s learning motivation and enrich their appreciation of the visual aesthetic. The findings of this experiment should be useful to practical educators, instructional designers and web designers who wish to create better learning websites for children.
ACKNOWLEDGMENT
This experiment was kindly supported by a grant from the Ministry of Science and Technology in Taiwan. (contract number: MOST105-2410-H-415-023).
REFERENCE
[1] Hart, T.A., Chaparro, B.S., & Halcomb, C.G. (2008). Evaluating websites for older adults: adherence to ‘senior-friendly’ guidelines and end-user performance. Behaviour & Information Technology, 27(3), 191-199.
[2] Schenkman, B., & J”nsson, F. (2000). Aesthetics and preferences of web pages. Behaviour & Information Technology, 19, 367-377.
[3] Tuch, A. N., Bargas-Avila, J. A., & Opwis, K. (2010). Symmetry and aesthetics in website design: It’s a man’s business. Computers in Human Behavior, 26, 1831-1837.
[4] Cyr, D., Head, M., & Larios, H. (2010). Colour appeal in website design within and a cross cultures: a multi-method evaluation. International Journal of Human-Computer Studies, 68,1-21.
[5] Michailidou, E., Harper, S., & Bechhofer, S. (2008).Visual complexity and aesthetic perception of web pages. In SIGDOC ’08: Proceedings of the 26th annual ACM international conference on Design of communication, (pp. 215-224). USA: New York.
[6] Tractinsky, N. (2013). Visual Aesthetics. In: Soegaard, Mads and Dam, Rikke Friis (eds.), The Encyclopedia of Human-Computer Interaction, 2nd Ed. Aarhus, Denmark: The Interaction Design Foundation.
[7] Udsen, L. E., & J”rgensen, A. H. (2005). The aesthetic turn: unravelling recent aesthetic approaches to human-computer interaction. Digital Creativity,16(4), 205-216.
[8] Berlyne, D. E. (1971). Aesthetics and psychobiology. New York: Appleton century crofts publishing.
[9] Wang, H. F., & Bowerman, C. J. (2012). The Impact of Perceived Visual Complexity on Children’s Websites in Relation to Classical and Expressive Aesthetics. In: IADIS International Conference IADIS Interfaces and Human Computer Interaction 2012. (Blashki, P. K., ed.), 269-273, July 21-23. Lisbon, Inderscience Publishers.
[10]Geissler, G. L., Zinkhan, M. Z., & Watson, R. T. (2006). The influence of home page complexity on consumer attention, attitudes, and purchase intent. Journal of Advertising, 35(2), 69-80.
[11] Tuch, A.N., Bargas-Avila, J. A., Opwis, K., & Wilhelm, F. H. (2009).Visual complexity of websites: Effects on users’ experience, physiology, performance, and memory. International Journal of Human-Computer Studies, 67(9), 703-715.
[12] Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667’686.
[13] Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
[14] Renninger, K. A., Hidi, S., & Krapp, A. (1992). The role of interest in learning and development. Hillsdale, NJ: Erlbaum.
[15]Deubel, P. (2003). An investigation of behaviorist and cognitive approaches to instructional multimedia design. Journal of Educational Multimedia and Hypermedia,12(1), 63-90.
[16] Vilamnil-Casanova, J., & Molina, L. (1996). An interactive guide to multimedia. In: Que Education and Training (pp. 124-129).
[17] Zain, J.M., Tey, M., & Goh, Y. (2007). Does aesthetics of web page interface matters to Mandarin learning? International Journal of Computer Science and Network Security, 7(8), 43-51.
[18] Roberts, M. N. (2007). Complexity and aesthetic preference for diverse visual stimuli, PhD thesis, Departament de Psicologia, Universitat de les Illes Balears.
[19] Kartiko, I., Kavakli, M., & Cheng, K. (2010). Learning science in a virtual reality application: the impacts of animated-virtual actors’ visual complexity. Computers & Education, 55, 881-891.
[20] Chang, Y-M., Lin, C-Y., & Lee, Y-K. (2005). The preferences of young children for images used in dynamic graphical interfaces in computer-assisted English vocabulary learning. Displays, 26, 147-152.
[21] Moshagen, M., & Thielsch, M. T. (2010). Facets of visual aesthetics. International Journal of Human-Computer Studies, 68 (10), 689-709.
[22] Huang, W-H, Huang,W-Y, Diefes-Dux, H., & Imbrie, P. K. (2006). A preliminary validation of Attention, Relevance, Confidence and Satisfaction model-based Instructional Material Motivational Survey in a computer-based tutorial setting. British Journal of Educational Technology, 37 (2), 243-259.
[23] Nunnally, J.C. (1978). Psychometric Theory. (2nd ed.). New York: McGraw Hill.
[24] Tracinsky, N., Cokhavi, A., Kirschenbaum, M., & Sharfi, T. (2006). Evaluating the consistency of immediate aesthetic perceptions of web pages. International Journal of Human-Computer Studies, 64, 1071-1083.
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