Revolutionizing animation: unleashing the power of artificial intelligence for cutting-edge visual effects in films

  • Application of soft computing
  • Published: 16 December 2023
  • Volume 28 , pages 749–763, ( 2024 )

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research paper about animation

  • Vundela Sivakrishna Reddy 1 ,
  • M. Kathiravan 1 &
  • Velagalapalli Lokeswara Reddy 2  

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Integrating artificial intelligence (AI) technology with the cinema and television sectors has resulted in significant transformations in the programming and production of television shows and the emergence of a novel cohort of AI-driven media. The ubiquity of AI-enabled technology enhances film and television production quality. Conversely, there has been a notable expansion in the animation sector in recent years, characterized by a growing number of film productions annually. Finding the user’s preferred animated films within the large array of information about animated movies has emerged as a notable obstacle. This article examines the sophisticated visual effects of computer vision in animated films, focusing on using artificial intelligence and machine learning technologies. This article proposes a critical perspective on fostering the advancement of cinematic visual effects through strategic means, utilizing computer vision and machine learning technology as fundamental tools for investigating novel methodologies and frameworks for achieving visual effects. This article explores new methodologies and methods for creating visual effects in moving images, using the film industry’s digitalization, intelligent advancement, and enhancement as a starting point. This article examines the application of convolutional neural algorithms in analyzing the visual effects of the Hollywood anime film “Coco.” The study’s findings indicate that the test set’s accuracy remained relatively constant at approximately 59% even after determining the model’s parameters. This outcome significantly enhances film productions’ audiovisual quality and creative standards while fostering healthy and sustainable growth in the film industry.

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Reddy, V.S., Kathiravan, M. & Reddy, V.L. Revolutionizing animation: unleashing the power of artificial intelligence for cutting-edge visual effects in films. Soft Comput 28 , 749–763 (2024).

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Title: animate anyone: consistent and controllable image-to-video synthesis for character animation.

Abstract: Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.

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Influence of Artificial Intelligence Technology on Animation Creation

Qingke Liu 1 and Hui Peng 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1881 , The 2nd International Conference on Computing and Data Science (CONF-CDS) 2021 28-30 January 2021, Stanford, United States Citation Qingke Liu and Hui Peng 2021 J. Phys.: Conf. Ser. 1881 032076 DOI 10.1088/1742-6596/1881/3/032076

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The advent of the era of artificial intelligence has brought unprecedentedly technological changes and breakthroughs to animation creation, which makes animation art creation gradually move towards the field of intelligence, and avoid the cumbersome and intensive work mode, thereby making the creation focus on the creative innovation. This paper deeply discusses the relationship between artificial intelligence and animation creation, and clarifies the advantages of artificial intelligence in improving the efficiency of animation creation. At the same time, it also analyses that the root of animation creation in this period is still human nature, so the creator should create animation based on human nature itself.

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In This Article Expand or collapse the "in this article" section Animation and the Animated Film

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Animation and the Animated Film by Paul Wells LAST REVIEWED: 19 December 2012 LAST MODIFIED: 19 December 2012 DOI: 10.1093/obo/9780199791286-0076

For many years, animation received minimal recognition as a significant form of cinematic and artistic expression. A seemingly irrevocable process of marginalization and dismissal has been arrested, however, by the enduring presence of animation festivals worldwide, the rise of animation studies in the Academy of Motion Picture Arts and Sciences, and the exponential rise in animation production in all sectors of media, culture, and the arts. Animation is now at the heart of cinema, from the traditional neoclassicism of Pixar Animation to high-end effects movies such as Avatar (2009); indeed, some argue that all cinema is a form of animation. Animation has been most associated, of course, with the American animated cartoon. Following the pioneering work of J. Stuart Blackton and Winsor McCay, Walt Disney developed the form through his “ Silly Symphonies ,” and Snow White and the Seven Dwarfs (1937), the first full-length, Technicolor, sound-synchronized, marquee-headlining, animated feature. Narrative, esthetic, and cultural challenges to Disney’s emergent classical style followed in cartoons by the Fleischer, Warner Brothers, MGM, and UPA studios, soon inventive models of comic mayhem and social satire. A different kind of experimental tradition emerged in Europe through auteurs such as Émile Cohl, Lotte Reiniger, Oskar Fischinger, Ladislaw Starewich, and Norman McLaren, employing different techniques and materials, but it also informed the productions of studios such as those in Zagreb and Prague, or Halas and Batchelor and W. E. Larkins in the United Kingdom. Animation afforded practitioners the opportunity to develop distinctive approaches, exploring color, shape, form, and motion for its own sake, or advancing fresh approaches to narrative and sociocultural representation. Strong indigenous traditions of animation were present, too, in Japan, Russia, and China, and almost uniformly across eastern and western Europe. Pixar Animation and Dreamworks have moved animation into the forefront of mainstream feature film entertainment, with high-quality franchises such as Toy Story (1995–2010) and Shrek (2001–2010), but independent works such as Persepolis (2007) and Waltz with Bashir (2008) have also achieved breakthrough crossover success. Animé—especially the films of Hayao Miyazaki—enjoys international appeal. Most major blockbuster movies employ spectacular animated visual effects, and The Simpsons , South Park , and Family Guy remain important staples of the TV schedules. The whole history of global animation practice is coming under fresh scrutiny. This developing literature properly reflects the different strands of activity and thinking about animation as a process, an art, a craft, a representational idiom, and a site addressing ideas and issues, most specifically memory and emotion.

The following texts offer broad overviews of animation in a variety of ways, partly operating as quasi-histories, partly as introductory, informative, and often richly illustrated works, and partly as commentaries on production that suggest other kinds of more developed reading and investigation. McCall 1998 is a useful catalogue; Beck 1994 collects the fifty greatest cartoons; Beckerman 2003 offers a view of animation as a craft, fully extended in Furniss 2008 ; Faber and Walters 2003 provides an update of the experimental tradition championed in Russett and Starr 1988 ; Kanfer 1997 looks at animation through the filter of business and industry; and Wiedemann 2004 collates the imagery of global animation as art.

Beck, Jerry. The 50 Greatest Cartoons: As Selected by 1,000 Animation Professionals . Atlanta: Turner, 1994.

Beck, one of the most knowledgeable figures about American animated cartoons, and convener of the “Cartoon Brew” website, ballots animation-invested experts and practitioners to name the fifty greatest cartoons. The criteria by which cartoons are evaluated are “originality,” “artistry,” “animation,” “music,” “humor,” “personality,” and “concept.” Number one is Chuck Jones’s What’s Opera, Doc? (1957).

Beckerman, Howard. Animation: The Whole Story . Rev. ed. New York: Allworth, 2003.

A practitioner perspective on the development of the form, offering a view of the history of animation in relation to its technical and craft orientation before offering insights on drawing, creating characters, visual storytelling, direction, and traditional approaches to 2D animation.

Faber, Liz, and Helen Walters. Animation Unlimited: Innovative Short Films since 1940 . London: Laurence King, 2003.

An invaluable compendium of innovative independent animated shorts made since 1940, with short introductory pieces and high-quality illustrative images. The book includes a DVD of examples based on the thematic categories of form, sound, words, and character, and embraces the anticipated canon of experimental filmmakers, and less celebrated figures including John Stehura, Jules Engel, Karl Sims, and Stan Vanderbeek.

Furniss, Maureen. The Animation Bible . New York: Abrams, 2008.

Maureen Furniss, a leading figure in animation studies, has produced some key texts in the research, scholarship, and pedagogy of animation. The Animation Bible constitutes a summation of her expertise and outlook, drawing together contemporary production examples not only to exemplify different models of practice but also to offer a theoretical and critical commentary on style, technique, and creative methodologies.

Kanfer, Stefan. Serious Business: The Art and Commerce of Animation in America from Betty Boop to Toy Story. New York: Scribner, 1997.

An engaging overview of the history of the American animated cartoon, set within an industry and business context, often described by veteran animators themselves. Though journalistic in tone, the book is often insightful about the relationship between the commercial infrastructure and the eventual production outcomes of competing studios.

McCall, Douglas L. Film Cartoons: A Guide to 20th Century American Animated Features and Shorts . Jefferson, NC: McFarland, 1998.

Divided into three sections, the book provides data on 180 feature animations, films that include animated credits and interludes, and information on more than 1,500 shorts. It also has material on key animation studios. A range of these kinds of texts are found in the bibliography, and they tend to be more authoritative than information of this sort on the Internet.

Russett, Robert, and Cecile Starr, eds. Experimental Animation: Origins of a New Art . New York: Da Capo, 1988.

One of the most significant yet neglected books in animation studies, offering an account of experimental animation as the “origin of a new art,” taking up the work of abstract filmmakers, and focusing on nonobjective, nonlinear shorts. The book includes analyses of work both in the European and in the American experimental tradition, constantly drawing attention to the deployment of new technologies.

Wiedemann, Julius, ed. Animation NOW! London: Taschen, 2004.

A comprehensive pictorial overview of animation practices worldwide, with an accompanying DVD, with short introductions about featured filmmakers, studios, universities, and colleges. The book adds credibility to the status and quality of the visual imagery drawn from animated films, by aligning it with Taschen’s overall strategy in producing publications dedicated to the primacy of the image in its own right.

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  • Published: 17 October 2022

Influence of 3D models and animations on students in natural subjects

  • Milada Teplá 1 ,
  • Pavel Teplý   ORCID: 1 &
  • Petr Šmejkal 1  

International Journal of STEM Education volume  9 , Article number:  65 ( 2022 ) Cite this article

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Studies comparing the effect of dynamic and static visualization suggest a predominantly positive effect of dynamic visualization. However, the results of individual comparisons are highly heterogeneous. In this study, we assess whether dynamic visualization (3D models and animations) used in the experimental group has a stronger positive influence on the intrinsic motivation and learning outcomes of science students (Biology, Chemistry and Geology) than static visualization used in the control group, and whether selected variables (students’ gender, age, educational level, learning domain, and teacher personality) significantly affect the results.

This study was conducted in 2019 with a sample of 565 students from Czech middle (aged 11–15 years) and high (aged 15–19 years) schools using the following research tools: Motivated Strategies for Learning Questionnaire, Intrinsic Motivation Inventory and knowledge tests. The results show that using 3D models and animations in the teaching process significantly increased the students’ intrinsic motivation for learning natural sciences (more specifically, its components (1) interest, (2) effort to actively participate in the educational process, (3) perceived competence and (4) understanding the usefulness of the subject matter), with a mean Hedges’ g  = 0.38. In addition, students in the experimental group reached a significantly higher level of Chemistry knowledge than their peers in the control group. Furthermore, by moderator analysis, we identified three moderator variables, namely student age, instructional domain and teacher personality. These variables significantly affect intrinsic motivation in different ways. The strongest positive effect of dynamic visualizations was found among students aged 11–13, whereas the weakest positive effect was identified among students aged 14–16. Regarding instructional domain animations and 3D models, the strongest positive effect is found in Chemistry ( g  = 0.74) and Biology ( g  = 0.72), whereas the positive impact on Geology is significantly weaker ( g  = 0.45). Teacher personality was found to be a major moderator in student motivation, with significant differences ( g  = 0.40—1.24). Teachers’ attitude towards modern technology plays an important role concerning this effect.


Based on these findings, we conclude that 3D models and animations have a positive effect on students and that teachers should include these visual aids in their lessons. For this reason, teachers are encouraged to implement these dynamic visual aids in their lessons regardless of their beliefs, and to get an adequate support in the process of implementation if necessary.


A lack of Science, Technology, Engineering, and Mathematics (STEM) graduates has long troubled the European Union in general, and the Czech Republic in particular (Gago et al., 2005 ). Despite the growing number of scientific publications in the STEM field (Li, 2021 ; Takeuchi et al., 2020 ) there is a current shortage of STEM graduates resulting from the relatively low number of high school students who enroll in science and technology degrees at universities (Eurostat, 2020 ), mostly due to the low popularity of some subjects. For example, Chemistry is an unpopular subject based on the results of student surveys (Beauchamp & Parkinson, 2008 ; Pavelková et al., 2010 ) because this subject is apparently too abstract for students who struggle to visualize some fundamental concepts, such as an atomic orbital (Chen et al., 2015 ), and to understand the particulate nature of matter (Williamson & Abraham, 1995 ). Even Biology, which is regarded as an easy subject (Hanzalová, 2019 ), covers numerous topics requiring a high level of abstraction, including anatomic structures (Mitsuhashi et al., 2009 ) and cellular biology (Jenkinson, 2018 ), as well as molecular genetics concepts and processes (Malacinski & Zell, 1996 ; Rotbain et al., 2006 ). It is therefore necessary that students are sufficiently motivated to study science subjects, which means increasing students ’ interest in the topics taught, along with overcoming challenging (mostly abstract) topics.

Visual representations have been developed to aid thinking and we generally use it to better understand various data (Mazza, 2009 ; Ware, 2004 ). The goal is to visually represent data in such a way that the most important patterns are clearly distinguishable from their surroundings (Mazza, 2009 ) to enable us capturing and incorporating a new piece of information into the long-term memory (Craik & Lockhart, 1972 ). This predetermines visualizations to be used in teaching these particular abstract topics.

Visualization can be divided into static visualizations (e.g., still illustrations, slides and photographs) and dynamic visualizations (e.g., animations, three-dimensional rotating models, simulations and videos). The latter have been gaining popularity as the use of graphics in computer-based educational environments has increasingly become commonplace (Lin & Atkinson, 2011 ).

Yet individual comparisons between the differential effects of static and dynamic visualization have yielded highly heterogeneous results (Kaushal & Panda, 2019 ).

Considering the above, this study aims to identify the best approach to increase students’ internal motivation for science subjects, because students who are more interested in natural sciences are also more motivated to study these subjects (Berg et al., 2003 ; Klahr & Nigam, 2004 ) and to understand them (Khishfe & Abd-El-Khalick, 2002 ). For this purpose, we assess the influence of dynamic visualization on primary and secondary school students in comparison with static visualization in science subjects (biology, chemistry and geology). More specifically, we examine the influence of static and dynamic visualization on students’ internal motivation (interest/enjoyment, effort, perceived competence, value/usefulness) and on the level of acquired knowledge on the subject matter.

One way to increase students’ interest in science subjects and to support their cognitive processes is to use visualization aids (Bilbokaitė, 2015 ; Nodzyńska, 2012 ; Popelka et al., 2019 ; Rotbain et al., 2006 ; Ryoo & Linn, 2012 ; Wu et al., 2001 ). Visual aids can help students understand particularly difficult and abstract topics (Bunce & Gabel, 2002 ; Harrison & Treagust, 2006 ) by stimulating their imagination and enhancing their ability to understand the subject matter, thereby improving the memorization of these concepts. Visualization can also enable students to adequately understand preconcepts (Tarmizi, 2010 ) while preventing the formation of misconcepts. Generally, visualization plays a key role in explaining the subject matter, focusing on features of microelements invisible to the naked eye (DiSpezio, 2010 ; Gomez-Zwiep, 2008 ; Herman et al., 2011 ). Some subjects, such as Biochemistry (Schönborn & Anderson, 2006) and closely related molecular biology (Jenkinson, 2018 ; Marbach-Ad et al., 2008 ), cannot be effectively taught without visualization. Therefore, visualization tools are crucial for understanding and research in the molecular and cellular biological sciences (Schönborn & Anderson, 2006).

The benefits of visualization tools lie in facilitating the understanding process best described by the so-called scaffolding theory (Eshach et al., 2011 ; Wood et al., 1976 ). “The scaffolding metaphor means that given appropriate assistance, a learner can perform a task otherwise outside his/her independent reach” (Eshach et al., 2011 , p. 552). The scaffolding theory, originally requiring an adult to assist and help students (Wood et al., 1976 ), has been subsequently extended by Puntambekar and Hübscher ( 2005 ) to teaching tools able to control and measure the amount of information given, thus reducing the number of acts needed to reach understanding (Puntambekar & Hübscher, 2005 ; Tabak, 2004 ; Wood et al., 1976 ). More recently, Chang and Linn ( 2013 ) showed that interactions with visualization tools are even more beneficial than visualization itself. Therefore, visualization aids that promote further interactions aim to be more effective.

Dynamic visualization

Dynamic visualization aids (e.g., animations, simulations, three-dimensional rotating models and videos) can be used in the teaching process for several purposes. First, animations can serve as a means of gaining attention. This category includes various animated arrows or highlights (Berney & Bétrancourt, 2016 ). Secondly, animation may be used to demonstrate concrete or abstract procedures required to be memorized and performed by the learner, such as tying nautical knots (Ayres et al., 2009 ; Schwan & Riempp, 2004 ). Thirdly, animation-based teaching is effective in describing processes that change over time and space (Ainsworth & VanLabeke, 2004 ; Rieber, 1990 ; Schnotz & Lowe, 2003 ). Dynamic visualization is especially suitable for dynamically visualizing abstract objects which students cannot easily imagine. Therefore, teaching through dynamic visualization is significantly more effective, especially in difficult scientific disciplines in which dynamic visualization can support the students’ cognitive processes (Bilbokaite, 2015 ; McElhaney et al., 2015 ).

This correlation is evident in processes that change over time (Ainsworth & VanLabeke, 2004 ; Rieber, 1990 ). For this reason, visualization is widely used in areas related to physical, chemical or biological disciplines. McElhaney et al. ( 2015 ) specifically mention that dynamic visualization can help pupils/students visualize unobservable dynamic phenomena, such as global climate change, tectonic plate motion, heat transfer, gene expression, cellular respiration and other cellular processes (e.g., cell division)—i.e., topics taught in science subjects such as geology, biology and chemistry.

Advantages and disadvantages of dynamic visualization

Both advantages and disadvantages of using dynamic visualization in teaching have been reported in comparison with static visualization. The benefits of dynamic visualization include enabling and facilitating effects (Kühl et al., 2011 ; Schnotz, 2005 ; Schnotz & Rasch, 2005 ) because the continuous representation of changes supports the perceptual and conceptual processing of dynamic information (Berney & Bétrancourt, 2016 ), in addition to preventing students from developing misconceptions and drawing erroneous conclusions from a mere static representation of the curriculum (e.g., misinterpreting a picture), which is related to an unnecessary cognitive load (Bétrancourt et al., 2001 ; Kühl et al., 2011 ). Dynamic visualization also reduces cognitive load associated with gradual steps (Berney & Bétrancourt, 2016 ), by helping students contextualize separate knowledge, for example relationships among pictures or schemes, which subsequently reduces working memory demands. Another benefit of dynamic visualization includes the ability to control its pace, such as pausing, rewinding or replaying (McElhaney et al., 2015 ).

Conversely, a disadvantage of dynamic visualization is the great amount of information given (Ainsworth & VanLabeke, 2004 ; Bétrancourt & Réalini, 2005 ), all of which (even transient) is processed and stored in the working memory, which could potentially lead to cognitive overload (Chandler, 2004 ; Chandler & Sweller, 1991 ; Jones & Scaife, 2000 ; Lowe, 1999 ; Mayer & Moreno, 2002 ). Dynamic visualization offers only temporary information, which (due to working memory overload) can be replaced by subsequent information (Bétrancourt & Tversky, 2000 ). By contrast, static images presenting different states or steps allow students to examine and compare these states, whereas dynamic visualization provides one step at a time. This stepwise presentation results in another disadvantage of dynamic visualization, which is the inability to compare individual steps (Bétrancourt et al., 2001 ). Another disadvantage of dynamic visualization is the split attention effect. When multiple events overlap in dynamic visualization (animation), attention fragmentation may occur, causing imperfect information acquisition (Löwe, 2003 ). Other disadvantages include oversimplifying a curriculum problem, which may give students a false impression that they understand the problem (Schnotz & Rasch, 2005 ).

Dynamic vs static visualization impact

Considering the widespread use of dynamic visualization, researchers have sought to study its impact on students. In 2000, a review conducted by Bétrancourt and Tversky ( 2000 ) compared 17 studies on the differences between common educational methods (extrapolation and analysis, among others) and the educational process supported by animations. Most studies (10 of 17) showed a positive impact of using animations, but the remaining 7 found no effect or only non-significant effects of incorporating animations into the educational process. In 2007, Höffler and Leutner conducted a meta-analysis of 26 studies published in 1973–2003 (Höffler & Leutner, 2007 ), including 76 pairwise comparisons of the effect between dynamic visualizations and static visualizations. This meta-analysis showed a positive effect of the animations compared with the static visualizations, with an average effect size d  = 0.37, which indicates a small to medium effect. However, the authors also included video-based visualization in their study. Even studies comparing static to video-based visualization have shown a significantly higher effect on average ( d  = 0.76; a total of 12 comparisons) than other comparisons based on computer graphics ( d  = 0.36; a total of 64 comparisons). The total number of participants was not specified.

The research of Höffler and Leutner was closely followed by a similar review study by Berney and Bétrancourt ( 2016 ), who also analyzed research articles published up to 2013, and focused on comparing differences in the benefits of static and dynamic visualization. The authors included 61 published studies, totaling 140 pairwise comparisons of dynamic and static visualizations intended for teaching. In contrast to the previous study, which assessed effect size based on Cohen’s d (Cohen, 1988 ), the magnitude of the effect was expressed as Hedges’ g (Hedges, 1981 ) in this meta-analysis, but the results confirmed the positive effect of animations when compared with static visualizations, with a difference in effect magnitude g  = 0.23, which represents a small effect. As the studies included more than 7000 subjects, the results can be considered reliable. The comparison between Cohen’s d of the previous meta-analysis and Hedges’ g of this analysis shows a decrease in effect size. The authors explain that the effect size is smaller because they included more total and pairwise comparisons in the analysis. The authors further highlight that, although the overall effect was positive, almost 60% of the studies did not show significant differences between dynamic and static visualization.

The results from the aforementioned meta-analyses suggest a predominant, albeit slight, positive effect of dynamic visualization (most often in the form of animation). Moreover, the results from individual comparisons are highly heterogeneous. Some studies show the positive influence of animations on the educational process (Lin & Atkinson, 2011 ; Marbach-Ad et al., 2008 ; Özmen, 2011 ), whereas others are less clear in their conclusions (Boucheix & Schneider, 2009 ; Bulman & Fairlie, 2016 ; Mayer et al., 2007 ; Tversky et al., 2002 ).

Moderator variables influencing the effect of dynamic visualization on students

As detailed in the section above, previous empirical studies exploring the influence of animations lack uniform results (Kaushal & Panda, 2019 ). These disparities have led researchers to search for potential moderators of the effect of using dynamic visual aids on student performance. These factors, which our study also addressed, include the instructional domain (subject), student gender and education level.

Instructional domain (subject)

The influence of the instructional domain, for which animation was created, was studied in the meta-analysis by Höffler and Leutner, ( 2007 ). The results showed that the instructional domain in which the analysis is performed is a determining factor of the effect size. The highest magnitude of the effect was measured in chemistry ( d  = 0.75; a total of 7 comparisons), followed by mathematics ( d  = 0.62; 5 comparisons) and physics ( d  = 0.28; 39 comparisons). The smallest effect was found surprisingly in biology ( d  = 0.13; 12 comparisons). However, due to the low number of comparisons, whose final effect size has been included in the overall result, the statistical power of this comparison was low (Höffler & Leutner, 2007 ).

The meta-analysis conducted by Berney and Bétrancourt ( 2016 ) also examined the influence of moderating variables that affect the effectiveness of animations in the teaching process. This meta-analysis among other things showed the subject in which the analysis is performed is a determinant of effect size as well. The highest effect was measured in “natural sciences” ( g  = 1.26; 8 comparisons), with a relatively large effect in chemistry as well (g = 0.77; 8 comparisons), but with a low effect size in biology (g = 0.20; 33 comparisons). However, even in these results, only a few subjects were compared, which reduced the statistical power of the results.

In the meta-analysis by Castro-Alonso et al. ( 2019 ), the influence of the subject on the effectiveness of dynamic visualization (animation) in teaching was also investigated. The authors focused on STEM and found that the dynamic type of visualization is more effective in geology and other sciences ( g  = 0.38; 11 comparisons) and subsequently in biology and medical sciences ( g  = 0.27; 11 comparisons) than in technical or mathematical subjects ( g  = 0.15; 15 comparisons) or even physics and chemistry ( g  = 0.19; 23 comparisons). Nevertheless, the number of overall comparisons was relatively low, which reduces the statistical power of the results.

The division into the instructional domain also entails some difficulties, which may be, for example, the attractiveness of the discussed topic. The whole content of individual scientific disciplines is not homogeneous, and one chapter may be more attractive for students than another, which has a great influence on the overall results.

Student gender

In their meta-analysis, Castro-Alonso et al. ( 2019 ) found that student gender is a key factor because dynamic visualizations are less effective in a sample of participants with fewer females than males. In particular, studies involving fewer than 59% of females showed a moderately positive effect of dynamic visualization ( g  = 0.36, 35 comparisons) and studies involving 60% or more females did not show any dynamic visualization effect (g = 0.07, 47 comparisons). The authors suggested that the unequal ratio of females to males, in some studies, may be a significant factor in explaining variations in effect size across studies.

Unfortunately, student gender factor has been overlooked in many studies (Garland & Sanchez, 2013 ; Schnotz et al., 1999 ; Wang et al., 2011 ), and most of which do not even provide gender ratios for the whole sample (Castro-Alonso et al., 2019 ). In addition, many studies are conducted with undergraduate pedagogy and psychology students, and males are markedly under-represented in these degrees (Castro-Alonso et al., 2019 ).

Gender has a strong influence on cognitive load (Bevilacqua, 2017 ). Thus, this factor must be analyzed. In their meta-analysis, Zell et al. ( 2015 ) concluded that gender has a significant effect on attention, memory and problem solving ( d  = 0.22), especially among the participants with the best results. Gender can also affect the participants’ perceptions of spatial imagination (Höffler, 2010 ; Ikwuka & Samuel, 2017 ; Wong et al., 2018 ; Zell et al., 2015 ).

Education level

Level of education plays a huge role, mainly because cognitive ability correlates with age (within individual differences) (Damon et al., 2006 ). This is reflected not only in different subjects (instructional domain), but more specifically in individual topics. The level of education must also be taken into account when choosing teaching methods, because it is at the age of middle school students when abstract and scientific thinking gradually develops (Damon et al., 2006 ; Goswami, 2010 ).

The literature shows that dynamic visualizations and animations have a positive impact on school children (Bétrancourt & Chassot, 2008 ), university students (Jaffar, 2012 ) and adults (Türkay, 2016 ). McElhaney et al. ( 2015 ) assessed, whether the effect of dynamic visualization depended on education level, and found that the difference between the effects of dynamic and static visualizations is higher in primary and secondary school students ( g  = 0.27; 10 comparisons) than in post-secondary level students ( g  = 0.07; 37 comparisons), which showed almost no effect.

The variable education level was also examined by Castro-Alonso et al. ( 2019 ), who concluded that the use of dynamic visualization is most effective among primary school students ( g  = 0.53), followed by secondary school students ( g  = 0.44), and the least effective among university students ( g  = 0.19) (Castro-Alonso et al., 2019 ).

Teacher personality

A substantial amount of variance in instructional quality can be explained by teacher characteristics such as cognitive ability, personality, professional knowledge, constructivist beliefs, enthusiasm and instructional quality (Baier et al., 2019 ). Teacher personality plays an important role in the educational process and should not be omitted. Kim et al. ( 2018 ) showed that even though domains of teacher personality do not predict academic achievement, they are able to predict subjective measures of teacher effectiveness as well as evaluation of teaching (Kim et al., 2019 ). Especially extraversion and enthusiasm have been identified as very strong predictors of instructional quality (Baier et al., 2019 ). Some of these domains are also crucial factors in the implementation and acceptance of technology in education (Tzima et al., 2019 ).

Objectives, hypothesis and research questions

The results from empirical studies are not uniform. Thus, further research is required to determine when animations are more effective than static visual aids (Kaushal & Panda, 2019 ) by continuously exploring dynamic visualizations and by defining potential moderators, which may significantly affect the potential impact of these aids on students.

Currently, many ongoing discussions (especially among teachers and politicians) address the use of dynamic visualizations (e.g., animations, simulations, three-dimensional rotating model, and videos) and their impact on the quality of the education process. Furthermore, the Strategy of Digital Education of the Czech Republic has already been approved since 2014 (MEYS, 2020 ). This strategy, aimed at the digitalization of education in middle and high schools, prioritized opening up the education system to new teaching methods through the use of digital technologies. Accordingly, new visualization equipment was purchased for 60 Czech schools. However, the effectiveness of these visual aids, their impact on the quality of educational process and the influence of potential moderator variables must be evaluated before expanding this strategy to the entire country.

Considering the above, we conducted a proof-of-concept study to assess whether using visual aids positively influenced students. The basic research method was a comparative study in the form of a pedagogical experiment, which investigated the impact of dynamic visualization as a teaching tool on chemistry students (and students of other science subjects) in comparison with those taught using static representations. A different representation of the curriculum was chosen as the independent variable, and it was investigated as to whether the difference could cause a change in both the intrinsic motivation of the students as well as the level of acquired knowledge (dependent variable). Thus, this is research in science didactics using ICT technology to serve as a teaching tool, delivering teaching content and motivating students in the process. Our study was designed and conducted at middle and high schools and mainly focused on the influence of using 3D models and animations in lessons of natural sciences (Biology, Chemistry and Geology)—more specifically on students’ intrinsic motivation and on their level of knowledge. Furthermore, the roles of potential moderator variables, such as gender, level of education, instructional domains and teacher personality, are discussed in our research.

The aim of our research was to assess the effect of 3D models and animations used in natural science classes on students. The size effect was measured on Hedges’ g scale.

The following research questions were developed:

How does the use of 3D models and animations affect students’ intrinsic motivation—more specifically students’: (1) interest; (2) effort to actively participate in the educational process; (3) perceived competence; (4) understanding of the usefulness of the subject matter?

How does this effect change after the intervention time (three months) of regular usage of 3D models and animations?

What is the effect of using 3D models and animations on acquired knowledge in Chemistry and Biology?

What roles do potential moderators (instructional domains, gender, level of education and teacher personality) play in the effectiveness of 3D models and animations?

Based on the results mentioned in the previous section, we set the following hypothesis:

3D models and animations have a positive influence on the intrinsic motivation of students in comparison with static visualization.

3D models and animations have a positive effect on learning outcomes in comparison with static visualization.

The variables of gender, age, educational level, learning domain, and teacher personality significantly affect the results.

The first and second hypotheses are based on the assumption that visualization can serve as a scaffolding tool for learners (Puntambekar & Hübscher, 2005 ). These hypotheses are supported by the benefits of dynamic visualization reported in Chapters 1.1 and 1.2, i.e., dynamic visualization helps students visualize abstract objects that they struggled to imagine (Bilbokaite, 2015 ; McElhaney et al., 2015 ) and unobservable dynamic phenomena (McElhaney et al., 2015 ), preventing misconceptions (Bétrancourt et al., 2001 ; Kühl et al., 2011 ) and reducing cognitive load (Berney & Bétrancourt, 2016 ). The hypotheses are contradicted by the findings of several meta-analyses (e.g., Berney & Bétrancourt, 2016 ; Castro-Alonso et al., 2019 ; Höffler & Leutner, 2007 ; McElhaney et al., 2015 ), as summarized in more detail in Chapters 1.3 and 1.4.


In total, 565 (middle and high school) students (321 females and 238 males; 6 students omitted this information), aged 11 to 20, were included in this study and divided into two groups (242 students in the control group and 323 students in the experimental group). Most of them were Biology students (350), in addition to Chemistry (124) and Geology (70) students. All students of both groups had similar educational and socioeconomic backgrounds.

In accordance with the precepts of a proof-of-concept study, the teachers and consequently their students who participated in this research were randomly selected. The teachers involved in this research were required to teach the same subject (Chemistry, Biology or Geology) in two classes of the same grade so that each teacher taught students in both the experimental class and control class, that is, to enable a direct comparison between the two classes. Of the 50 teachers who met the criteria for participation in this study, 11 were randomly selected to participate in this research. The teachers were employed at a middle or high school and had a master’s degree. The median years of experience in teaching science was 15.5 years, and all teachers agreed to use 3D models and visualizations in some of their classes.

As explained above, all teachers taught in two classes of the same grade, an experimental class and a control class. All students of the experimental classes formed the experimental group (EG), whereas all students of the control classes formed the control group (CG).

In this article, experimental teaching is teaching in which EG students were taught using dynamic visualization aids. The teachers incorporated dynamic visualizations (three-dimensional rotating models and animations) into the lessons in the experimental class for 3 months, without changing their teaching methods. Teachers were instructed to use dynamic visualization in almost every lesson depending on the topic under discussion.

In turn, control teaching herein is classical (traditional) teaching in which the traditional way of teaching (thus far) was followed, i.e., in the same way as in the experimental group, albeit without dynamic visualization aids. The teacher could use visual aids in the control class as well (pictures and schemes, among others), but not dynamic visualizations (three-dimensional rotating models or animations).

Each teacher taught the same topics in both control and experimental classes.

Topics from general chemistry (the state of substances, the formation of chemical bonds, ions and acid–base reactions) and organic chemistry (hybridization, stereochemistry, the structure of hydrocarbons and their derivatives) were taught in chemistry. In turn, Biology introduced mainly topics from human biology (human anatomy, muscles, blood circulation, the human skeleton and digestive system), zoology (differences in systems and body structure), general biology (prokaryotic and eukaryotic cell) and botany. Lastly, in Geology, mainly external and internal geological processes were taught.

Learning environment

The application software Corinth was used as the source of 3D models and animations. This app was developed by experts from several universities and is designed to support the digitalization of the educational process at middle and high schools (Corinth s.r.o., 2020). In addition, Charles University, primarily experts in didactics of natural sciences (including authors of this article), helped to develop this application. Corinth is mainly intended for lessons of natural sciences and offers various visual aids for the educational process. The software consists of a library with 1500 visual objects—mostly 3D models, microscope images, videos, photo galleries and animations (Fig.  1 ). The following topics are covered in this application: Biology, Geology, Chemistry, Physics, Astronomy, Geometry and a few cultural and historical topics. In contrast to common textbooks, online videos or presentations, students can manipulate the object as if they were holding the actual object in their own hands. Therefore, each student can focus on specific details overlooked in 2D images. Moreover, students can turn the 3D models around, zoom in or out on the picture, highlight the objects or look inside them and pause the animations. All models also provide a short description of individual parts, as well as other important comments and notes—for example visualization in augmented reality (AR). This function uses the camera of the equipment to project the chosen 3D model or animation on real time captured backgroud. Application Corinth is known in the US thanks to the educational platform Lifeliqe, which received the 2017 Best App for Teaching and Learning award from the American Association of School Librarians (ALA, 2017).

figure 1

One of the models used in experimental teaching—a 3D model of the heart (Corinth s.r.o., 2020)

Measures, knowledge test and questionnaires

Several research tools were used in the preliminary study and subsequent research.

Two types of research tools were used to determine the effect on students’ motivational orientation:

Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich et al., 1991 ) Intrinsic Motivation Inventory (IMI) (McAuley et al., 1989 ; Ryan, 1982 ).

The level of knowledge was determined through knowledge pretests and posttests.

At the beginning of the research, each teacher was interviewed to assess their expectations and experience. At the end of the research, each teacher was interviewed, providing feedback on the lessons taught in this project.

Standardized questionnaires: MSLQ and IMI

The MSLQ (Motivated Strategies for Learning Questionnaire) is a tool for identifying students’ motivational strategies in the learning process, compiled by Pintrich, Smith, Garcia and McKeachie, and serves to identify and evaluate students’ motivational orientations and their use of different strategies for self-learning, i.e., in the process of self-regulation (Pintrich et al., 1991 ). Based on this questionnaire, a Pre-questionnaire was designed by selecting 16 statements from the four following scales:

intrinsic goal motivation (e.g., “in a class like this, I prefer course material that really challenges me so I can learn new things. The most satisfying thing for me in this course is trying to understand the content as thoroughly as possible.”);

self-efficacy for learning and performance (e.g., “I’m confident I can do an excellent job on the assignments and tests in this course. Considering the difficulty of this course, the teacher, and my skills, I think I will do well in this class.”);

extrinsic goal motivation (e.g., “Getting a good grade in this class is the most satisfying thing for me right now. If I can, I want to get better grades in this class than most of the other students.”);

control beliefs (e.g., “It is my own fault if I don’t learn the material in this course. If I don’t understand the course material, it is because I didn’t try hard enough.”).

This Pre-questionnaire was used in both the experimental and control classes at the beginning of the second lesson—before using the experimental teaching methods for the first time.

The IMI (Intrinsic Motivation Inventory) tool is an internal motivation questionnaire based on Ryan’s research ( 1982 ), but its final form was compiled by McAuley et al. ( 1989 ) and is used to assess the subjective experience related to the student’s internal motivation and personal self-reflection. Based on IMI, three questionnaires (Post-questionnaire 1, Post-questionnaire 2_1 and Post-questionnaire 2_2) were created, each of which consisted of 25 statements from the four following scales:

interest/enjoyment (e.g., “This activity was fun to do. I enjoyed doing this activity very much.”);

effort/importance (e.g., “I put a lot of effort into this. I tried very hard on this activity.”);

perceived competence (e.g., “I was pretty skilled at this activity. I am satisfied with my performance on this task.”);

value/usefulness (e.g., “I think this is an important activity. I believe doing this activity could be beneficial to me.”).

Both tools use a seven-item Likert’s scale for each statement (Likert, 1932 ) enabling participants to express their level of agreement with each statement from “strongly agree” = 1 to “strongly disagree” = 7 (Pintrich et al., 1991 ; Ryan, 1982 ). Both tools have been used in many earlier studies in the field of intrinsic motivation and self-regulation (Monetti, 2002 ; Niemi et al., 2003 ; Wolters, 2004 ). These tools were also used to measure intrinsic motivation for natural sciences (Šmejkal et al., 2016 ). An advantage of such research tools is their flexibility as modular aids adaptable to specific research needs. Therefore, they do not require using their full versions (Markland & Hardy, 1997 ; Pintrich et al., 1991 ; Rotgans & Schmidt, 2010 ).

Knowledge tests

The acquired knowledge was evaluated using knowledge tests. Due to the difficult process of developing these tests, this analysis was performed only at randomly selected schools and in randomly selected classes of those schools, totaling 4 tests (2 Chemistry tests and 2 Biology tests). The tests were created specifically for each school and class by a panel of experts, more specifically two experts in didactics and three teachers of the subject (Chemistry/ Biology). The tests were compiled based on the curriculum and on the goals set by the teacher, in line with the revised version of Bloom's taxonomy of cognitive goals (Airasian et al., 2001 ). In chemistry in particular, we were able to include a larger number of tasks focused on engaging of higher level thinking skills, such as conceptual and procedural knowledge in Knowledge Dimension and remembering, understanding and application in Cognitive Process Dimension. Each knowledge test was administered twice, once during the first lesson (Pretest) and then during the penultimate lesson (Posttest). The tests were identical for the experimental and control groups.

The research survey was performed in 2019. All teachers involved in the research completed a 2-day training course before the research survey to acquaint themselves with the educational aid, its content and technical aspects (e.g., how to install Corinth software on their mobile device or how to incorporate educational content in presentations and other educational materials). Throughout this study, the teachers were in contact with the researchers and with the Corinth technical support as well. All teachers were also familiarized in detail with the course of the research, all research tools and their purpose in the research, and with the way in which students were supposed to fill in the questionnaires. Students were informed about the pedagogical research, and the research questionnaires were filled in anonymously.

The pedagogical experiment proceeded as follows. Before the actual start of the experiment, an initial interview was conducted with all the teachers. The aim of the initial interview was to find out what the teachers’ expectations are in relation to the implementation of dynamic visualization, specifically the implementation of Corinth in the classroom. The interview included a total of 17 questions, which were thematically divided into four areas: teacher-oriented questions (5), student-oriented questions (4), questions oriented to the content of the Corinth application (5), questions oriented to the school’s attitude towards the implementation of the Corinth application in the classroom (3).

In the first to third lessons, both the experimental and control groups used the classical teaching style. All students filled the Pretest during the first lesson, the Pre-questionnaire at the beginning of the second lesson, and the Post-Questionnaire 1 at the end of the third lesson. From the fourth lesson onward, control group (CG) students were taught using a classical teaching style, whereas the experimental teaching style was implemented in the experimental group (EG). The EG students filled in Post-Questionnaire 2_1 after the first experimental lesson. The same questionnaire, Post-Questionnaire 2_2, was filled in by the EG students again in the last lesson, after three months of intensive learning using dynamic visualizations. In the penultimate lesson of the pedagogical experiment, the students filled in the posttests. After the pedagogical experiment, an output interview was conducted with the teachers.

Figure  2 schematically shows the time course of research and the sequence of research tools.

figure 2

Diagram showing the timeline of the implementation of each research tool

Results and discussion

Data from 565 students were used in the statistical analysis. The anonymized data were processed in the statistical software IBM SPSS using appropriate statistical methods. Significance was assessed using both parametric and non-parametric tests, setting the significance level at α  = 0.05. Initially, the effect size was calculated based on Hedges’  g (Hedges & Olkin, 1985 ).

Reliability of the data from the questionnaires and calculation of the study variables

In all scales, the reliability of each questionnaire mentioned above was assessed by calculating the Cronbach’s alpha coefficient (Cronbach, 1951 ).

Almost all values of reliability exceeded the generally accepted minimum of 0.70 (Nunnally, 1978 ), except for the Cronbach’s alpha of “control beliefs” of the Pre-Questionnaire, which was 0.60 (see Table 1 ). This value was nevertheless close to the required level and was therefore accepted. In conclusion, the data are internally consistent and reliable. Based on this model approved by confirmation analysis (Šmejkal et al., 2016 ), new variables were calculated as an average of each item from one of the scales described in Methodology.

The influence of 3D models and animations on students’ intrinsic motivation

To assess the influence of using 3D models and animations on students (RQ1), specifically: (1) interest in the subject matter; (2) effort during the educational process; (3) perceived competence; (4) usefulness of the subject matter, two statistical tests were performed.

First, the students’ motivation in the control lesson was evaluated using data from the Pre-Questionnaire and from the Post-Questionnaire 1. Based on the data, we assessed whether the CG and EG significantly differed. Because the data did not conclusively show a normal distribution, the Mann–Whitney U test was used as the appropriate statistical test, albeit showing no significant difference between the CG and the EG. The significance level of all scales exceeded 0.05, except for “self-efficacy for learning and performance”. Although a significant difference was found in this scale, the Hedges’ g demonstrated that the difference was very small (Table 2 ). Therefore, the students of the EG and CG reached similar values of intrinsic motivation in most scales.

Second, we assessed whether the perception of control and experimental lessons significantly differed among students who had experienced both teaching styles (only students in the EG). The corresponding data were retrieved from the Post-Questionnaire 1 and Post-Questionnaire 2_1 and analyzed statistically. For this purpose, the non-parametric Wilcoxon signed-rank test was used because some of the data did not show a normal distribution. The results from the test highlighted significant differences between the students’ evaluation of the control and the first experimental lessons in all scales ( p -values were significantly lower than 0.05 in all scales, see Table 3 ). The values of Hedges’ g also suggested that using 3D models and animations had a strong positive effect on the students’ intrinsic motivation, particularly in their interest/enjoyment of the teaching process ( g  = 1.05) and perceived value/usefulness of the subject matter ( g  = 1.02), in addition to a medium positive effect on perceived competence ( g  = 0.41) and a low, albeit positive influence on effort/ importance ( g  = 0.27). In short, after the first experimental lesson the students’ motivation (more specifically their interest in the subject matter, their effort to understand the subject matter and their perceived teacher competence and subject matter importance) significantly differed between the control and experimental lessons, with a large weighted mean effect size ( g  = 0.69). It can be concluded that 3D models and animations have a significant, positive effect on all components of intrinsic motivation, thus corroborating the findings of Berney and Bétrancourt ( 2016 ). All components of intrinsic motivation are positively influenced by the use of 3D models and animations when comparing experimental and control lessons. Overall, owing to the incorporation of 3D models and animations into the educational process, students are more interested in the subject matter and therefore willing to put more effort into learning new skills, thereby improving their learning outcomes.

To assess whether the positive effect of the application decreases with the intervention time of its incorporation into the educational process over time (3 months) (RQ2), data from the Post-Questionnaire 2_1 were compared with data from the Post-Questionnaire 2_2. Based on the character of the data, the non-parametric Wilcoxon signed-rank test was used for this analysis, rejecting the null hypothesis in 3 of the 4 scales because significant differences were found between the answers of the two questionnaires (see Table 4 ). The comparison of the effect size showed that the decreases in the scales were low ( g  = 0.32) in perceived competence, medium ( g  = 0.41) in value/usefulness and medium/large ( g  = 0.60) in interest/enjoyment. However, no significant decrease was found in effort/importance over time. As in similar cases it can be expected that after starting to use 3D dynamic animations, the so-called “Novelty Effect” (Clark & Sugrue, 1988 ) may be observed. Therefore, a study was carried out to monitor changes (in motivation, knowledge) depending on the intervention time of using dynamic 3D animations, for it has been shown that the intervention time of using the aid can lead to a decrease in students attention and motivation (Tsay et al., 2018 ).

The comparison between the traditional teaching style and the experimental method after 3 months of intensive use of 3D models and animations in the lessons showed a consistently significant difference in 3 of the 4 scales (based on the Wilcoxon signed-rank test on the data from the Post-Questionnaire 1 and Post-Questionnaire 2_2, Table 5 and Fig.  3 ), with a medium/large positive effect size in the value/usefulness scale ( g  = 0.64), a medium effect size in the interest/enjoyment scale ( g  = 0.49) and a small but positive effect size in the effort/importance scale ( g  = 0.26). Based on the results, using 3D models and animations primarily affects the students’ interest and perceived value of the subject matter. The overall positive effect was evident, even after three months of using the 3D models and animations, as shown by the weighted mean effect size ( g  = 0.38). In other words, the use of 3D models and animations enhances the perceived importance of the subject matter, most likely by lowering the level of cognitive processes and abstraction necessary for understanding the concepts of phenomena studied in natural sciences (Chandler & Sweller, 1991 ), which proves a scaffolding potential of used visualization. This experimental approach to teaching prevents the decrease (and in some cases even leads to an increase) in the students’ interest in the subject matter. Furthermore, the students are also willing to put more effort into understanding a given topic. From a long-term perspective, these two trends are crucial because effort/importance reach the same values over time. Accordingly, the occasional use of 3D models and animations helps students understand the importance of the subject, thereby increasing the long-term efforts that they put into the educational process (Ryan & Deci, 2000 ). Based on the above stated findings, it can be declared that the first hypothesis was confirmed.

figure 3

The bar chart illustrates the medians of the components of intrinsic motivation after the control lesson, after the first experimental lesson and after the last experimental lesson (seven-item Likert’s scale)

In comparison with the findings of previous studies, our positive effects of the use of 3D models and animations are significantly stronger than the results from the meta-analysis performed by Berney and Bétrancourt ( 2016 ) and by Castro-Alonso et al. ( 2019 ), with an average effect size of 0.23 (Hedges’ g ). In turn, the results from this study are similar to those of the meta-analysis by Höffler and Leutner ( 2007 ), who reported an average effect size of 0.37 (Cohen’s d ). The differences in results of the studies may be caused by the heterogeneity of the studies included in the analyses. Moreover, Castro-Alonso et al. ( 2019 ) also address this issue in their meta-analysis where they observed a significant heterogeneity between effect sizes. Therefore, they recommend focusing on different variables influencing these results.

The effect of using 3D models and animations on the level of acquired knowledge

The effect of using 3D models and animations on the level of acquired knowledge (RQ3) was assessed based on the results from knowledge tests.

The reliability of each knowledge test was determined by calculating the corresponding Cronbach’s alpha (see Table 6 for results). The required value of reliability of the test used for individual pedagogical diagnosis is 0.8 (Chráska, 1999 ). According to George and Mallery ( 2003 ), a Cronbach’s alpha value between 0.7 and 0.8 is also acceptable. Therefore, based on the Cronbach’s alpha values calculated in this study, the knowledge tests meet the required reliability standards.

The data were analyzed using the parametric, two-tailed t -test. The results showed no significant difference in the Pretest between the CG and EG at the beginning of the research (Pretest Chemistry: t  = -0.192, df = 54, p  = 0.848, M control  = 4.29, SD = 2.532, M experimental  = 4.44, SD = 3.292, g  = 0.050; Pretest Biology: t  = − 1.283, df = 54, p  = 0.205, M control  = 15.55; SD = 5.954; M experimental  = 17.52; SD = 5.402, g  = 0.342).

At the end of the research, the students were asked to complete the same knowledge tests (Posttests). The results from the two-tailed t -test conclusively demonstrate that Chemistry students in the EG performed better than their peers in the CG. The calculated difference was significant and deemed large (Posttest Chemistry: t  = − 3.601, df = 58, p  = 0.001, M control  = 16.531, SD = 7.326, M experimental  = 23.839, SD = 8.394, g  = 0.916). Furthermore, in the Biology tests, students in the EG also performed better than the students in the CG (Posttest Biology: t  = − 1.189, df = 50, p  = 0.240, M control  = 25.92, SD = 10.488, M experimental  = 29.04, SD = 8.373, g  = 0.322), albeit non-significantly. One of the possible explanations is the higher level of abstraction and visualization required in Chemistry. Considering the individual development of visual orientation and abstract thinking, 3D models require a lower level of visual orientation from students, and animations can support the understanding of abstract concepts. Thus, the combination of these tools improves the understanding of the subject matter and therefore the results from the evaluation phase of the educational process. The second hypothesis (H2) was as well as confirmed. The higher level of visual orientation and abstract thinking necessary for understanding Chemistry may account for the stronger impact of using visual aids in the educational process. The results from this analysis are in line with the Cognitive Load Theory (Chandler & Sweller, 1991 ), according to which visualization decreases the cognitive load and therefore lowers the total cognitive steps necessary for succeeding in a given task. The combination of this factor with the significant, positive influence on the students’ interest is one of the signs of scientific literacy, as defined by PISA (OECD, 2006), thus opening up new research opportunities.

The results from knowledge pretests and posttests are summarized in Fig.  4 . The chart shows box diagrams of the results of 4 tests (2 pretests and 2 posttests) separately for both subjects, that is, chemistry and natural sciences. The comparison of the box diagrams shows no significant differences between CG and EG in knowledge pretests (especially in chemistry), but the differences become more pronounced in knowledge posttests (again, mainly in chemistry).

figure 4

Results from the knowledge tests in CG and EG

The results shown above are in line with the outcomes of the teacher interviews. The teachers reported increased interest and motivation of the students during the lessons with 3D models and animations.

“The pupils ’ interest increased, they were drawn into the lessons, everyone was paying attention, they were enjoying it. The lessons were more interesting for the them. Thank you.”

Furthermore, the teachers also reported that passive students were more easily activated.

“The pupils were curious what new things they would see.”

Moreover, the improvements in illustration of the subject matter facilitated the explanation and understanding of abstract concepts for teachers and students, respectively.

“Teaching has become more interesting and students ’ imagination and understanding of the subject matter has improved.”

Educators also mentioned that incorporating 3D models and animations is helpful, especially for students with a lower level of visual orientation or abstract thinking.

The influence of potential moderators on dynamic visualizations

The next step of our research was the analysis of the impact of the following potential moderators of the effect of using animations and 3D models on the students’ motivation: student gender, level of education, student age, instructional domain and teacher personality (RQ4). However, the obtained conclusions did not fully confirm the third hypothesis (H3).

Based on the results from the Mann–Whitney U test, student gender played no role in the evaluation of the first experimental lesson ( g  = 0.10, N female  = 151, N male  = 104), and all intrinsic motivation components were equal between male and female students in the first experimental lesson. Similar results were found when comparing the corresponding data from the first experimental lesson with the data from the control lesson (separately for each gender). The weighted mean effect size on female students ( g  = 0.69, N  = 129) and the weighted mean effect size on male students ( g  = 0.68, N  = 91) were virtually equal (see Table 7 ). In our study, student gender is not a moderator variable of intrinsic motivation. In contrast, other studies have shown that student gender is a strong moderator variable of the effect of dynamic visualizations on learning (Castro-Alonso et al., 2019 ). The variability in the findings of these studies may be caused by the differences in individual methodologies, learning environments and male:female ratios of participants. Therefore, this potential moderator must be further explored to find more evidence about its role.

Level of education

The results from the Mann–Whitney U test showed no level-of-education effect on the results of the students ( g  = 0.13, N middle school  = 128, N high   school  = 129) in the first experimental lesson. Similar results were found when comparing the corresponding data from the first experimental lessons with the data from the control lessons (separately for middle and high school students), as shown by the weighted mean effect sizes for middle ( g  = 0.67, N  = 108) and high school ( g  = 0.70, N  = 112) students (see Table 7 ). The level of education shows no effect on the results of our study, despite the findings of Castro-Alonso et al. ( 2019 ), who reported that dynamic visualizations were more effective among middle school students than among high school students, but the difference was quite small. This finding could be caused by the non-linear variation of the results with student age.

Student age

The first experimental lesson was attended by 257 students aged 11 to 20. Data analysis highlighted a significant, quadratic relationship between student age and all components of intrinsic motivation [interest: F (255) = 5.07, p  = 0.007; effort: F (255) = 9.08, p  = 0.000, competence F (255) = 4.60, p  = 0.011: value: F (255) = 3.34, p  = 0.037, see Fig.  5 ]. Based on the relationship between these pairs of variables, a few general trends can be described, for example: younger students (11–12 years of age) perceive the incorporation of dynamic visualizations into the teaching process highly positively. However, as the students become older, they gradually evaluate the use of animations and 3D models in the educational process less favorably—the students’ evaluation is the least positive among 15-year-old students (at the age when they graduate from middle school in the Czech Republic). However, student feedback becomes more positive among high school students aged 16–18. Unfortunately, the sample of students older than 18 years was too small to enable any prediction. In any case, the power of the models is low.

figure 5

Fitted line plot of the quadratic model for the relationship between student age and interest, effort, competence or value

Instructional domain

The Kruskal–Wallis test showed a significant influence of the subject Biology ( N  = 154), Chemistry ( N  = 64) or Geology ( N  = 36) on two components of intrinsic motivation, interest/ enjoyment ( η 2  = 0.079) and value/usefulness ( η 2  = 0.066) in the first experimental lesson. Biology students showed the highest interest in the subject, whereas the lowest interest was found among Geology students. Furthermore, the same trend was observed in the scale value/ usefulness. As for the other components of intrinsic motivation (effort/ importance and perceived competence), the students in the first experimental lesson reached similar values in each school subject.

Comparing the data from the first experimental lesson with those from the control lesson (separately for each subject), we can summarize the results as follows: animations and 3D models have the strongest positive effect on Chemistry ( g  = 0.74, N  = 56) and Biology ( g  = 0.72, N Biology  = 133), whereas the positive impact on Geology is significantly weaker ( g  = 0.45, N Geology  = 31) than on the other subjects (see Table 7 ). Considering these results, the instructional domain is a significant moderator variable. Given the limited number of questionnaires from the experimental lessons in Geology and Chemistry, and since only selected topics were taught, the results cannot be generalized. However, based on our findings, we assume that including dynamic visualization (3D models and animations) in biological, chemical and geological disciplines is beneficial, as evidenced by other authors (Jenkinson, 2018 ; McElhaney et al., 2015 ; Mitsuhashi et al., 2009 ).

In total, 11 teachers (9 females and 2 males) participated in this study. Using the Kruskal–Wallis test, we found a significant influence of teacher personality on all components of intrinsic motivation ( η 2 is between 0.070 and 0.132) in the first experimental lesson.

The overall effect of animations and 3D models on the students’ intrinsic motivation was evaluated based on the comparison between the data from the first experimental lesson and the data from the first control lesson. The calculated values of the weighted mean effect sizes (Hedges’ g ) ranged from 0.40 to 1.21 (see Table 7 ).

The largest differences in size effect were found between individual teachers. Therefore, teacher personality is a significant moderator variable. However, due to the limited number of questionnaires from the experimental lesson, the power of the test comparing subgroups is low. The analysis of structured interviews with teachers shows that students whose teachers worry about the failure of the educational application, and question the positive effect of visualization on learning, evaluate the experimental lesson less positively than students whose teachers are more confident about the experimental teaching process. In their interviews, all teachers also mentioned the time needed to adjust their lesson plans to incorporate visual aids appropriately.

“Initially, I spent more time and put more effort into lesson preparation process because I wanted the app to fit into my teaching plan.”

This concern was justified because many teachers had to learn how to work with the application software before they could use its visual aids in the teaching process. Based on these results, teachers must have a positive attitude towards modern technology, as innovators and early adopters, according to Aldunate and Nussbaum ( 2013 ), in addition to adequate technical support at schools in case of any technical difficulties. Furthermore, teachers should be familiar not only with the teaching content of the application software but also with all technical issues. However, teacher personality was not analyzed as a moderator variable of the effectiveness of dynamic visualizations in the teaching process, and therefore should be the subject of further studies.

Study limitations

The main limitation of this study is that all data reflect only the students’ attitude towards the teaching process and their own level of understanding of the subject matter. Therefore, the students’ level of self-confidence also interferes with the results. Nevertheless, this effect is partly compensated for by structured interviews with the teachers, who also evaluated the teaching process from their perspective. The data collected from students and teachers matched. During the interviews, teachers stated that the incorporation of 3D models and animations into the teaching process had a positive impact on their students, who found the models interesting, entertaining and attractive. Therefore, the students appeared more motivated to learn and interested in the subject matter. Furthermore, the teachers expressed a deeper interest in 3D models and animations of physiological processes in plants and humans from not only a biological but also a chemical standpoint (reaction mechanisms, for example).

The effect of teacher personality on the results of the experiment was also partly compensated for by the fact that all teachers taught both groups (experimental and control classes of the same grade). Moreover, the teachers included the same topics in the teaching process in both classes, further offsetting this factor. The generalizability of the results might be limited also by the small sample size. Especially small number of teachers with different teaching styles and personality characteristics may have influenced some of the research results. In this regard, the findings of the present study may provide a good starting point for the design of such studies in a larger scale aiming toward equal sample sizes and similar education level.

The use of 3D models and animations in lessons of natural sciences is positively perceived by students at both middle and high schools. These conclusions are supported by the positive impact of dynamic visualizations on intrinsic motivation in comparison with static images of 3 subject matters (Biology, Chemistry and Geology), as shown by the weighted mean effect size (Hedges’  g  = 0.38). Accordingly, the Czech educational system must respond to the specific needs of the current generation, by updating education materials in lockstep with the most recent advances in technology, and by introducing subject matter topics in a more dynamic, realistic and effective way. Our research demonstrates that appropriately incorporating visual aids simplifies abstract processes and enhances understanding. As a result, students may be more interested in learning and may even consider studying the subject matter at a higher level (for example at university). For this reason, teachers should include these visual aids in their lessons regardless of their age or beliefs. Similarly, university educators should also train future teachers in working with digital technologies (Evagorou et al., 2015 ), so that they are more confident in using them without fear of potential technical failures.

Availability of data and materials

Not applicable.

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The authors thank all the collaborating teachers in this research for their enthusiasm and valuable feedback. The authors also thank Dr. Carlos V. Melo for editing the manuscript.

This work was supported by University research centres of Charles University: UNCE/HUM/024 and funding project Progres Q17 .

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Teplá, M., Teplý, P. & Šmejkal, P. Influence of 3D models and animations on students in natural subjects. IJ STEM Ed 9 , 65 (2022).

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The Soundtrack, "Sound and Music in Emerging Media" special Issue edited by Jennifer O' Meara

Simone Dotto

Mario Wienerrother's Musicless Musicvideos series have recently surfaced on the Internet and since 2006, they have gained millions of views as a humorous YouTube sensation. As a professional sound designer, he re-edits already released and often widely known music videos by depriving them of music and adding a different, entirely sonic audio track that he records himself. All of these reworkings seem to raise the same hypothetical question: what if these audiovisual excerpts were deprived of their most signifying aural expression, namely music? Would they still have the same meaning? Would they tell the same 'story'? This article aims to explore Wienerroither's practices and the effects they provoke on our understanding of the aesthetic conventions of the music video. As a first step, I will provide a detailed account of the process for realizing the Musicless Musicvideos by drawing on an interview I carried out with the author. Secondly, I will investigate a number of Wienerroither's productions in relation to (1) the aesthetic conventions of music video and (2) the practical realm of Foley artistry. The objective of the article is to demonstrate how, by relying on a sound-centred practice, Musicless Musicvideos undermine the overall balance between the visual and the aural elements in a typical music video.

New Cinemas: Journal of Contemporary Film

Chris Pallant

Home on the Range (Will Finn and John Sanford, 2004) concluded what had been, for Disney, a stylistically progressive sequence of theatrically released features defined in this article as the Neo-Disney period that broke with the hyperrealist conventions most commonly associated with the studio's output. It is this critically neglected sequence of films, comprising Fantasia 2000 (James Algar et al., 1999), The Emperor's New Groove (Mark Dindal, 2000), Atlantis: The Lost Empire (Gary Trousdale and Kirk Wise, 2001), Lilo and Stitch (Dean DeBlois and Chris Sanders, 2002), Treasure Planet (Ron Clements and John Musker, 2002), Brother Bear (Aaron Blaise and Robert Walker, 2003) and Home on the Range, which provide the focus of this article. By analysing the artistic and narratological composition of these films, this article seeks to demonstrate that the studio's feature animation is more heterogeneous and progressive than received notions of Disney allow for.

Sof Guevara

The relationship between the visual and the acoustic worlds has been a historical exploration in the arts, physics, as well as psychology and neuroscience. In multimedia, the analysis of their cross-modal relationships and the formal relationships that do not take into account the narrative or meaning in film, are not a developed field in multimedia analysis today. To approach this subject, in this study we have analysed the Toccata & Fugue in Dm sequence of the music film Fantasia (1940), by Walt Disney. This is one of the first cases of commercial abstract imagery combined with music, and a unique film in its audiovisual relationships. We have approached the analysis in two ways: first, using a descriptive strategy, starting with Chion’s masking method and highlighting where the multimedia interacts or fails to do so, and then extracting visual and aural data of the sequence manually and digitally, and assembling a chart with these descriptors, inspired by Eisenstein. Our results have shown that this sequence is purely governed by music. We did not find steady general rules as to formal audiovisual relationships. Intensity was found to be one of the most repeated audiovisual congruences, interpreted by loudness in sound and luminance in vision. No significant sound to visual relationships were found regarding timbre. Even though our results are specific to this case, this paper can serve as one more stone in the path to a different kind of filmic analysis.

This is a book primarily for professionals and lovers of animation, but it can also be employed as a textbook for other fields of audiovisual media. Pikkov is a director of animated films who, in addition to creating animated films, teaches in the Department of Animation of the Estonian Academy of Arts. Animasophy analyses the interrelations of the history, theoretical essence and practical expression of the animated film. REVIEW:

Nathan Phillips

Sebastian Diaz-Gasca

Videogames are interactive media that have developed into various genres since their creation in the second half of the 20th century. These genres differ both in the way their stories are told, and in the ways their in which narratives are developed. Role Playing Games (RPGs) are known for their extended hours of gameplay and their intricate stories or narratives. One narrative element that stands out is the cut‐scene, in which the interactive nature of games is inhibited, allowing for a guided, non‐interactive narrative to occur. Cut‐scenes are used to aid the narrative as they describe events that would otherwise be difficult to explain in interactive mode. Music plays an important role in the game narrative: it interprets and enhances the events displayed on the screen and also creates a setting which supports the illusion of being in another world. Videogames, especially RPGs, require extensive amounts of gameplay time for the story to develop. During this time, music is reused and themes and motifs reappear in the game. As the narrative evolves, music too will evolve alongside it, adding to and transforming the connotations of the music in the context of the game. This dissertation will focus on Final Fantasy X as a case study. Final Fantasy X is a well‐known RPG that contains many narrative elements that are now considered standard. The evolution cut‐scene music in this game will be investigated in order to demonstrate first, how music interacts and, secondly, how changes with the game narrative and how segments of the same musical theme can carry several connotations within the game narrative according to the context.

Journal of Popular Music Studies

Carol Vernallis


Proceedings of the National Academy of Sciences of the United States of America

Pavel Masek


Songklanakarin Journal of Science and Technology

Pakamas Chetpattananondh

Cara Buka Grosir Sembako

harga jualgrosir

2009 International Conference on Asian Language Processing

Applied Thermal Engineering

Zorka Pintarič

Temas Agrarios

Carlos Enrique Cardona Ayala

Global Change Biology

Laura Chasmer

samuel runtulalo

Gunnar Gillberg

International Journal of Computer Vision

Terry Peters

Schopenhauer Jahrbuch

Osman Daniel Choque Aliaga

Digestive Diseases and Sciences

Sharon Lopez

Archivos de Medicina del Deporte


Neotropical Biology and Conservation

Marco Vattano

Arquivo Brasileiro de Medicina Veterinária e Zootecnia

Fabíola Bono Fukushima

HAL (Le Centre pour la Communication Scientifique Directe)

isabelle sacareau

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Kültür ve Turizm Bakanlığı/Atatürk Ansiklopedisi

Serhan Kemal Saygı

AHFE international

Maria Lucia Okimoto

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Emilio Ferrari

Jesica González Contreras

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Joanna Fagan


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