We are living through a “visual boom”, increasingly surrounded by visual data. Visual literacy, along with ability to decode information, which is close reading visuals, and vice versa, to encode, which is creating a visual information, is now prevalent in all facets of life. Advertisement, social media, science and technology, university admission tests (SAT, IELTS etc.) go side by side with graphical information adding more and more questions with charts. Despite this, predominant part of obtaining information during lessons comes from straight text and “80% of instruction is delivered orally” (University of Illinois Extension, 2009). Infographics are left underutilized, though “we can arrange and present information in a concise and easy-to-understand way, by using a combination of words, images and shapes” (Dewan, 2015). 

An extensive part of literature indicates the importance of paying particular attention to students’ skills to evaluate, analyze and interpret visual data. Timothy Gangwer (2015), an author of the book “Visual Impact, Visual Teaching…”, specifies that 65% of people are visual, and “whether you are an early childhood teacher or high school chemistry teacher, visual teaching is a template for all your instructional strategies” (Gangwer, 2015). Therefore, it is in teachers’ best interest to create an appropriate learning environment and materials to allow students to critically read and view pictorial information as the language of the message.

The literature provides variety of definitions and meanings to visual literacy interlinking it with an ability to interpret images. For example, Case-Gant (1973) defined visual literacy as a set of skills that makes it possible for a learner to read and interpret visual messages in communication and understand messages conveyed vie images. Hortin (1980) explained visual literacy by understanding and applying images and is when learners think and learn in terms of images. Yenawine (1997) stated that a person who is skilled in visual literacy is able to find meaning in imagery. While above-mentioned researchers refer to visual literacy as an ability to interpret visual messages, Metros (2008) also described this skill as being able to compose visuals that bear meaningful communications. 

Krum (2013) provides an interesting analogy to the objectives of infographics comparing it with public speech, which has three functions: to inform, entertain and persuade the audience. In the same vein, infographics have introductions to grab reader’s attention, where the purpose is also stated. Then, they all complete the idea with conclusions and call for action, so the readers are aware of what they should do with this information. 

In spite of the increasing exposure to visual stimuli in everyday life, most school learners do not possess visual literacy skills to work with visuals effectively. Consequently, school graduates are not able to benefit from visuals in either academic life or their career. Hortin (1980) states that visually-literate students gain considerable advantage in close-reading and comprehending visuals and thinking in a visual way. Thus, allocating time for visual literacy has a compelling advantage. 

There is a gap in students’ comprehension and analysis of charts, where they, in most cases, use descriptive language without an attempt to group, compare, contrast and summarize the focal points. Therefore, this study attempts to analyze the effect of applying infographics (both decoding and encoding them) on students’ ability to synthesize, compare, contrast, group and summarize key information of pictorial information. The following research questions were addressed:

1)    What challenges do students face while interpreting graphical information?

2)    How does the use of infographics affect students’ comprehension and interpretation of graphical information?

The given study is collaborative research of an English, Chemistry, Math and ICT teachers as part of Lesson Study conducted in 2020-2021 academic year in Nazarbayev Intellectual School. The entire class of eleventh-grade, ten students, was a part of the research. These students were selected for several reasons. The major reason is that they are mature enough to comprehend and analyze non-straight texts containing difficult concepts. Another reason is that their writing skills are sufficiently developed to put graphical data into words, and vice versa. Additionally, they have already developed the ability to think critically, however underutilized, to be able to grasp ideas and concepts beyond a shallow level. To address research questions, a mixed research design was employed. Combining both quantitative and qualitative data provides “a very powerful mix” (Miles & Huberman, 1994, p. 42). Qualitative data, such as students’ written assignments, was analyzed and checked according to predetermined criteria: identifying key information (main trends), grouping and categorizing information, contrasting and comparing, summarizing and verbal interpretation of data, in other words, putting digital data into written form (statistics, percentages, fractions, etc). In order to analyze data, an examination of students’ IELTS Writing Task 1, which assesses skills to analyze and interpret graphical data, was conducted. Since this exam is one of the main External Summative exams for all graduating students, this type of essay was chosen as a diagnostic instrument. Apart from this, open-ended interviews that provided students’ reflection offered different perspectives on the research topic, providing “a complex structure of the situation” (Creswell, p. 537). Students’ specific mistakes identified were calculated, analyzed, and grouped. The study was carried out in four phases and spanned six months. Phase one aimed at defining specific students’ needs. At an initial meeting, our LS group identified 3 case students in each class. One student out of 10 performed exceptionally, getting band 5.0 out of 9.0, who was defined as a Student A, one who got 4.0 is a Student B, and the lowest band identified was 3.5 is a Student C. The second phase included joint development of lesson plans, then we devised data-collection methods. During an implementation stage, the lesson was delivered with my colleagues observing the lesson according to a pre-arranged schedule, followed by the fourth phase - debrief, which included analyzing collected data, students’ works, and any sought-after changes.

Data analysis revealed the following most common mistakes that were defined as a major benchmark for the researcher to be guided by throughout the study. 1. Not having a clear overview: students do not outline 2-3 most noticeable key features of the graph; 2. Not selecting the most significant information: students describe every piece of information given in the graph, not grouping it. 3. Not organizing ideas into logical paragraphs: students do not structure their ideas into chunks, complicating one paragraph with a lot of details. 4. Not deriving commonalities and discrepancies between data: students use mere descriptive manner of presenting data; 5. Not backing up describing trend with statistics: students use narrative way of presenting data, not supporting their statements with figures (percentage, years, tones, etc.) from the chart. Just in the middle of the intervention, one of my STEM colleagues suggested doing things backward, i.e. not only decoding graphs, as English and science teachers in most cases apply, but encoding them. The focus here is to make students see. Thus, we decided to try reversed pattern–encoding graphical information. Students were interpreting graphs they were given, and then, they swapped their works and tried to reproduce peers’ graphs on paper. Following this, they compared the original version with their replica. It was a kind of an eye-opener for most, who saw the way they describe charts, as the exact reproduction of their writing was different. After a series of such activities, coupled with strategies for academic writing and graphics learning, students started to display improvements both in their analysis and consequently in interpretations. In conclusion, applying infographics at both ends – decoding and encoding, students were able to see their own mistakes and learn from them, which is a vital metacognitive skill.

1.Case-Gant, A. (1973). Visual literacy: An exciting environmental adventure. Richmond, VA: Richmond Public Schools. Retrieved from ERIC database 2.Creswell, J. (2013). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 3.Clark, R. C., & Lyons, C. (2010). Graphics for learning: Proven guidelines for planning, designing and evaluating visuals in training materials. 4.Dewan, P. (2015). Words Versus Pictures: Leveraging the Research on Visual Communication. Partnership: The Canadian Journal of Library and Information Practice and Research, 10(1). 5.Gangwer, T. (2015). Visual impact, visual teaching: Using images to strengthen learning. New York: Skyhorse Pub. 6.Hortin, J., A. (1980). Visual literacy and visual thinking. Retrieved from ERIC database (ED214522). International Society for Technology in Education (2008). 7.Krum, R. (2013). Cool infographics: Effective communication with data visualization and design. 8.Metros, S., E. (2008). The educator’s role in preparing visually literate learners. Theory Into Practice. 9.Miles, M.B., Huberman, A. M. (1994). Qualitative data analysis. 10.Yenawine, P. (1997). Thoughts on visual literacy. In J. Flood, S., B. Heath, & D. Lapp (Eds.), Handbook of Research on Teaching Literacy through the Communicative and Visual Arts.