Student-Generated Stop-Motion Animation in Science Classes: a Systematic Literature Review

Several papers reported significant improvement in students’ domain-specific knowledge (Ekici et al. 2014; Hoban et al. 2009a; Jablonski et al. 2015; Macdonald and Hoban 2009; Wilkerson et al. 2015; Wishart 2017; Yaseen and Aubusson 2018). Also, according to the reviewed papers, student-generated SMAs facilitate conceptual understanding by revising mis- or alternative conceptions and improving students’ mental models (Akaygun 2016; Brown et al. 2013; Church et al. 2007; Fleer and Hoban 2012; Hoban and Nielsen 2013, 2014; Keast et al. 2010; Kidman et al. 2012; Loughran et al. 2012; Mills et al. 2018a; Nielsen and Hoban 2015; Peter et al. 2011).

Neutral or conflicting results have also been reported. Chang et al. (2010) found that students’ understanding of science concepts was better for students who only viewed the animation than the students who designed and developed their animation. Only when integrating peer-evaluation and designing, a significant improvement of students’ learning was found. Peter et al. (2011) pointed out that there was no significant difference between students’ conceptual understanding among SMA and paper sketch groups. Also, in Ekici et al.’ s (2014) study, despite higher achievement scores for SMA generated groups immediately after the intervention, no statistical difference in retention test scores was found between control and experimental groups.

The majority of studies that address non-cognitive learning outcomes report an improvement in motivation, engagement, and interest through the use of SMAs. The autonomy support that is implicit in allowing students to develop their own animations increases autonomous types of motivation and, eventually, engagement (Bogiages and Hitt 2008; Brown et al. 2013; Hoban 2005, 2007; Hoban and Nielsen 2010; Jablonski et al. 2015; Mills et al. 2018b; Peter et al. 2011; Wilkerson et al. 2015). Mills et al. (2018b) study reports differentiated findings in this respect. Despite an improvement in students’ individual interest in learning geology, their maintained situational interest value, i.e., the belief that the content itself is meaningful to one’s life beyond the classroom (Linnenbrink-Garcia et al. 2010), did not improve. Also, Church et al. (2007) claimed that generating a SMA concerning an event of interest in the student’s life (i.e., connecting more closely with their personal lives) appears to improve their motivation and engagement better.

Wilkerson et al. (2018) reported that students’ mechanistic reasoning skills could be elicited and improved by creating mechanistic models with a combination of multiple representations, i.e., drawing, SMA, and simulation. Berg et al.’s (2019) study illustrated that the SMA task enabled students to better engage in reasoning concerning both the macro (i.e., observable) and the sub-micro-level (i.e., a level that cannot be observed) models, and how they relate to each other. Nordin and Osman (2018) found that student-generated SMA can be effective in fostering collaborative problem-solving skills in terms of establishing and maintaining shared understanding and group organization toward finding the appropriate action to solve a physics problem in secondary education. The results of some studies also revealed that the SMA task contributed to the development of twenty-first-century skills such as creativity, communication, and cooperation skills, information literacy, research skills, technology, and media literacy skills (Atalay and Belet Boyaci 2019; Karakoyun and Yapıcı 2018). Skills in using technology, such as taking photos with digital cameras, were also reported as an outcome of intentionally teaching using SMA in early childhood science classes (Fleer and Hoban 2012).

Researchers also noticed that their student teachers were able to further their abilities in many other areas aside from ‘just science’, including information and technology skills, creative writing, group work, and research (Keast et al. 2010; Kidman et al. 2012). Having student teachers create a narrated SMA to explain a science concept in Hoban and Nielsen’s (2014) study provided a context for generating discussions, exchange, and clarification of ideas, which finally contributed to scientific reasoning skills.

Although research confirmed the possibility and the benefits of generating SMA as a teaching approach in science classes (Ekici and Ekici 2014; Fleer and Hoban 2012; Hoban et al. 2007; Keast et al. 2010; Loughran et al. 2012; Nielsen and Hoban 2015; Paige et al. 2016), Vratulis et al. (2011) stated that student teachers did not encourage their pupils to design and make SMA projects in practice—despite the student-centered approach advocated during the program. Therefore, knowledge and beliefs about instructional strategies for teaching science as one of the main elements of Magnusson et al.’s (1999) pedagogical content knowledge model, did not improve in this study. Vratulis et al. (2011) concluded that introducing student teachers to alternate instructional strategies employing digital technology as learners in a teacher education program in itself is not enough for them to deploy these technologies in their schools.

In order to improve learning in any learning environment, it is important to understand students’ learning processes (Beyaztaş and Senemoğlu 2015). The more instructors understand this process, the better their chance of meeting the diverse learning needs and scaffolding their students’ learning (Felder and Brent 2005). In this line, Kidman (2015) distinguishes first- and second-order learning in the context of the process of generating SMAs. First-order learning refers to considering only observable characteristics of the phenomena, with very little analysis of the visual representation to be made. Second-order learning (or deep learning) emerges once the learner engages in the science content behind the phenomenon. Here, the learner mentally engages with prior knowledge, the new information provided in published sources, and the information interpretation provided by group participants. The learner makes meaning of all this information and then re-represents the new findings in a SMA.

Hoban et al. (2009b) observe that the learning pathway in the SMA learning approach is cyclical, iterative, and dynamic rather than linear. This recursive checking of information was clearly manifested in Hoban et al.’s (2011) study when students referred back to the support material and discussed it with their peers. Also, according to Kidman et al.’s (2012) observations, in a collaborative setting, groups can choose to superficially accept one representation of the chunks (i.e., surface learning) or through discussion and planning agree by consensus on the key “chunks” that need to go in their SMA (i.e., deep learning). According to scholars, this iterative process is responsible for revealing and revisiting students’ misconceptions (Hoban et al. 2009b; Hoban and Nielsen 2013) and encouraging their conceptual learning (Hoban et al. 2011).

According to Wilkerson et al. (2015), intentionally letting students generate and evaluate knowledge, e.g., asking them to construct a model and share their product individually, will help them to both realize the wealth of knowledge they already have about the subject and evaluate that knowledge. Moreover, scholars reported that asking students to share their final products can also bring their conflicting ideas and uncertainties about the science concept to the fore (Mills et al. 2018a), leading to a substantive discourse in the classroom (Brown et al. 2013). This discourse, especially in the form of peer-evaluation, can effectively improve the accuracy of the representations (Hoban and Nielsen 2014) and contribute to students’ conceptual learning (Chang et al. 2010; Mills et al. 2018a) and student teachers’ better mastery of subject matter (Hoban and Nielsen 2013). In this regard, Kamp and Deaton (2013) reported that providing a rubric for students’ peer-evaluation could support the evaluation process of final student-generated SMAs. Also, Hoban and Nielsen (2013) claimed that clear explanation as a narration in the final representation provoked the student teachers’ better realization of subject matter.

In their study with 28 elementary school students, Wilkerson et al. (2018) found that groups struggling to construct a model progressed when they received guidance about general modeling strategies (i.e., rules and constraints for making a model), but not when they received guidance about model content. They claim that in terms of complex systems reasoning, students benefit from explicit support in considering the elements, behavior, and interactions in a system both before and during the exploration and construction of models. Their findings also highlight that an iterative modeling activity across multiple representations (drawing, SMA, and simulation) can facilitate and deepen student learning and engagement.

According to Nielsen and Hoban’s (2015) study, expert’s representations can scaffold students’ understanding while students discuss their own representations. In the same line, Yaseen and Aubusson (2018) claim that teachers can ask students to identify, discuss, explain, and map the similarities and differences between student- and expert- generated animations. They reported that with this sort of mediation, conceptual learning was improved more than if the students had only viewed the experts’ animations.

Also, asking questions by the teacher is highlighted in some papers as a way to scaffold students “learning toward the science part of the animation during the developing process of a SMA” (Mills et al. 2018a; Wilkerson et al. 2015; Yaseen and Aubusson 2018). In Yaseen and Aubusson (2018)’s study, students focused on the technical quality of animation, but the teacher took the discussion beyond this, by asking students to consider the scientific propositions implicit in the animations. These guiding questions helped the students to both understand and explain the exact science concepts related to the subject of their SMA. Also, they claim that teachers can enhance students’ critical thinking by asking why their SMA might be inadequate and how it could be improved and by showing them other ways of looking at a specific concept. Similarly, Mills et al. (2018a) reported that teachers’ questions to encourage thinking and provoke scientific explanations were crucial in revising students’ conflicting ideas and developing conceptual understanding.

Keast et al. (2010) found that the SMA approach was shown to be most effective when it used to present a science concept which is small, self-contained, and easy to chunk and represent. Peter et al. (2011) reported that student-generated SMA improves learning, especially for content that involves dynamic events, e.g., moon phase, cellular division, or smell diffusion. In terms of cognitive load, Kidman and Hoban (2009) claimed that when a topic requires a considerable representation effort (such as the fiddly detail in the representation of chromosome mapping), cognitive load focuses on the representation, rather than scientific processes.

Hoban et al. (2011) claimed that a key feature of generating a SMA is that it does not involve the use of any specific software. In most of the studies, learners used generic software such as Apple’s QuickTime Pro (Hoban 2005; Hoban and Ferry 2006; Vratulis et al. 2011), Windows Movie Maker (Jablonski et al. 2015; Nordin and Osman 2018; Wishart 2016), SAM animation (Church et al. 2007; Hoban et al. 2009a; Hoban and Nielsen 2013; Wilkerson et al. 2015) and mobile apps like MyCreate (Mills et al. 2018b), iMotionHD (Wishart 2017), and iStopMotion (Berg et al. 2019; Kamp and Deaton 2013).

In some studies, however, domain-specific software was used to develop student-generated SMAs. For example, Chang et al. (2010) used “Chemation” for making a molecular SMA in Chemistry. Akaygun (2016) used “ChemSense” which is specifically designed to generate drawings and animations for chemistry concepts through a stop-motion technique. Yaseen and Aubusson (2018) used K-Sketch for generating SMAs about different states of matter by students. Akaygun (2016) also suggested that participants in K-Sketch produced their animations faster and had a significantly lower cognitive load than common animation software, such as ChemSense, with which it was compared. Also, according to his findings, K-Sketch provides the most freedom for representing dynamics, and ChemSense provides more options for representing structural features. SiMSAM (Simulation, Measurement, and Stop-Action Moviemaking) allows students to create SMAs by using an external camera to capture successive photos of drawings or craft materials and was used in different studies by Wilkerson et al. (2015, 2018). This software was designed specifically to support learners in “discovering” aspects of kinetic molecular theory.

Most of the empirical research in the domain of student-generated SMAs have been performed in a collaborative setting (Brown et al. 2013; Jablonski et al. 2015; Mills et al. 2018a; Vratulis et al. 2011; Wilkerson et al. 2015, 2018; Wishart 2016, 2017; Yaseen and Aubusson 2018). Many researchers argued that generating discussion is the main benefit of student-generated SMAs in collaborative settings. Brown et al. (2013) claim that the collaborative creation of a SMA facilitates rich opportunities for students to use discourse as a representational form to generate and mediate between other representational forms. Their findings are in line with Hoban and Nielsen’s (2014) study, which suggests having student teachers create a narrated animation in order to explain science, the narrative sparking subsequent discussion. Loughran et al. (2012) found that collaborating with peers in developing SMAs helped student teachers recognize and respond to a range of pre- or alternative conceptions that they held, not only in terms of pre-conceptions’ nature but also in terms of their origin and possible address in the classroom.

According to Wishart (2016, 2017), group discussions were found to be key to student science learning through the co-construction of meaning. In the same line, Kidman (2015) asserted that SMA, when used in a collaborative inquiry-based learning context, is a technique in which “the new ‘meaning-making’ of the designers facilitates the new ‘meaning-making’ of others.” Paige et al.’s (2016) study with science student teachers showed that working in a collaborative setting was preferred to an individual setting by the students.

The main trend is that SMA can be applied to almost all age levels, from early childhood to university classrooms (Hoban and Nielsen 2014). Studies include 4-year olds learning science concepts (Fleer and Hoban 2012), elementary classes co-constructing spinning in space through collaborative generating of a SMA (Brown et al. 2013), and smell diffusion by grade 6 students through multimodal representation, including SMA (Wilkerson et al. 2015).

On the middle school level, more studies with SMA have been performed (e.g., Jablonski et al. 2015; Mills et al. 2018a; Mills et al. 2018b). The same holds for secondary school classrooms (e.g., Church et al. 2007; Kamp and Deaton 2013). In the context of university teacher education programs, there have been many studies (24 out of 42) on how creating a SMA influenced student teachers in learning various science concepts (Berg et al. 2019; Hoban et al. 2009a; Hoban and Nielsen 2012, 2013), pedagogical intent (Hoban et al. 2007; Hoban and Ferry 2006; Keast et al. 2010; Nielsen and Hoban 2015), and technological pedagogical content knowledge (Paige et al. 2016). Finally, Wishart (2016), in a multi-level study (primary, middle, and secondary school), found that students of different ages can benefit from generating SMA in science classes. She found that the younger students (aged 8–9) most often enjoyed making, whereas the older ones (aged 15–16) most enjoyed seeing their finished results.

Kidman et al. (2012) compared learning processes in student-generated SMAs with Peirce’s (1955) model of semiotic systems and claimed that students ‘prior knowledge serves as a “referent” in this system, and it is important for starting the cumulative semiotic progression responsible for learning through generating SMA. As learners revisit their prior knowledge through different semiotic systems during design and development, building from one representation to the next promotes learning (Nielsen and Hoban 2015). However, Hoban and Nielsen (2013) found that the process of creating a SMA was effective even if students (pre-school children) did not have any prior knowledge about the domain. This finding is in line with Church et al. (2007) and Mills et al. (2018a), who found that the SMA task induced higher cognitive activity and led students to critically examine their own mental models even in the absence of prior instruction.

Paige et al. (2016) found that learners who were novices in digital literacies had difficulties accessing basic digital technology and equipment, impeding their construction of a SMA. This finding supports the general importance of improving ICT skills in a cross-curricular fashion. Brown et al. (2013) claim that explicit teaching of representational literacies, i.e., to effectively communicate ideas using multimedia (Flood et al. 2004), could be beneficial in this respect.