Symbol system theory

Jinnie Shin

Centre for Research in Applied Measurement and Evaluation, University of Alberta

Background and Definition

With the introduction of various technologies in education, a great amount of attention has been given to understanding the effects of media in learning. For example, numerous studies have been conducted to understand whether students’ learning changes significantly when content is delivered via various technologies (Ogbuju et al., 2012). To explain the impact of media on student learning and achievement, Salomon (1977) introduced the symbol system theory to understand how different media influence knowledge acquisition. He defined symbol systems as languages used to convey meanings and messages in media, while emphasizing how different media symbol systems impact the effectiveness of information delivery and student learning (Salomon, 1977).

Common examples of symbol systems include pictures, printed texts, graphs, films, and television programs. For example, much of his early research was focusing on investigating student learning using a famous children’s TV show “Sesame Street”. He claimed that students’ active involvement and attention was a key factor to promote effective learning through symbolic features of media. In other words, students who were engaged as an ‘active learner’ could learn more effectively than ‘passive learners’ from the same symbol systems. In addition, he emphasized the degree of matching between the symbol systems and learning content as another important factor for effective learning (Ouyang & Stanley, 2014).

Symbol Systems and Learning

Salomon (1977) introduced five specific aspects of media symbol systems that could potentially affect student learning and knowledge acquisition. He argued that the distinctive characteristics of media promote varying levels of cognitive processes from students. First, he believed that different media symbol systems could emphasize different learning contents. Salomon (1977) explained that the choice of media symbol systems can determine which instructional contexts are presented and emphasized in a lesson. Second, he believed that certain media symbol systems can be memorized and recognized relatively easily by students, thus, maximizing the information storage. Third, different needs of information processing and recoding are required from students for varying types of symbol systems. Recoding refers to learner’s mental processes, which translate the presented symbolic system into a personalized preferred system in an attempt to understand the information easily. Therefore, symbol systems explain the types and degrees of knowledge students could acquire in a lesson.

For example, songs and chants are widely used symbol systems to teach English to young learners. Learning English expressions using songs and chants may emphasize the intonation and pronunciation of words and phrases via their auditory symbols. Thus, students could save much effort for processing the information (i.e., intonation and pronunciation) compared to when the contents are presented in a written text. However, Salomon (1977) also emphasized students’ active engagement as a core determiner for effective learning. Therefore, careful examination is required to investigate whether less recoding (or mental engagement) of student would lead to less effective learning.

Symbol Systems and Educational Technology

Symbol system theory has laid a theoretical foundation for the importance of using multimedia to promote effective learning in education (Ouyang & Stanley, 2014). For example, integrating multimedia, such as text, graphics, and animation to present comprehensive information in a class could provide opportunities for students to process knowledge in various meaningful ways (Crosby & Stelovsky, 1995; Jarosievitz (2011). Also, Salomon’s unique definition of an effective learning environment, where learning occurs based on interactions between student and media, has influenced making informed decisions in designing classroom instructions using various technologies (Salomon, Perkins & Globerson, 1991; Jonassen, 1996; Jonassen & Reeves, 1996; Kozma, 1987).

For example, Leung et al., (2018) introduced virtual reality (VR) systems as an ideal learning setting for future classroom learning to promote effective learning. They emphasized how VR systems often provide an immersive environment for learners by conveying pictorial, auditory, and haptic symbols. The variety of symbol systems that VR systems could include significantly increase the scope of learning content conveyed to students. Also, VR systems often provide highly engaging and realistic scenarios, thus, promoting more active and engaging learning opportunities, which Salomon (1977) referred to as “mindful” learning. The following examples show how VR systems are currently used in classroom settings:

• Class VR in Education: Class VR provides an immersive classroom learning experience using a standalone headset. It comes with a user-friendly interface, which students can easily control with a simple gesture, such as hands or head movements, to navigate and select activities. In addition, teachers can plan and prepare a VR lesson in advance and deliver it to students for their self-regulated studies.
• Dash VR App: Dash VR provides a structural and guided tour of the solar system to students with optional instructional features, such as voiceover and subtitles in multiple languages.
• VR for the Primary School Curriculum: PRIMEVR provides various scenarios of how VR can be integrated into teaching different disciplines (e.g. Science, Geography, and History). They also provide a curriculum package design to maximize the usage of their VR systems with simple training for instructors.

• Expeditions App: Google’s VR app help students to easily access VR experiences with their phones. Students can have a virtual ‘field-trip’ to an ancient Egyptian tomb or a tour of the solar system to readily experience what they read in the textbook.

Issues

Salomon’s symbol system theory was heavily influenced by Gardner’s (1980) multiple intelligences theory. Gardner (1980) proposed eight distinct categories of intelligence that learners might hold to explain the importance of modality of learning. Both theories are built based on a strong assumption that different cognitive processing can only be activated by different symbol systems or knowledge representations. However, much dispute has existed surrounding whether the symbol representation, indeed, stimulates distinctive cognitive processes. For example, Clark (1983) has argued that many of the symbol systems used in classroom learning could not serve to promote various distinctive cognitive processing, which indicates the mode of instruction as a less significant factor for effective learning.

Links

• Instructional Design- Symbol-systems theory
• The Importance of Multimedia in Classroom
• 6 Pros & Cons of Technology in the Classroom in 2019

References

• Boyd-Barrett, O., & Braham, P. (2013). Media, knowledge and power. Routledge.
• Emeka, O., Charity, M., Philip, C., & Onyesolu, M. O. (2012). E-learning system: Educational content delivery through mobile phones. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(2), 101-106.
• Jonassen, D. H. (1996). Learning with technology: Using computers as cognitive tools.Handbook of research for educational communications and technology.
• Ouyang, J. R., & Stanley, N. (2014). Theories and research in educational technology and distance learning instruction through Blackboard. Universal Journal of Educational Research, 2(2), 161-172.
• Leung, T., Zulkernine, F., & Isah, H. (2018). The use of Virtual Reality in Enhancing Interdisciplinary Research and Education. arXiv preprint arXiv:1809.08585.
• Salomon, G. (1979). Interaction of Media, Cognition, and Learning. San Francisco: Jossey-Bass.
• Salomon, Perkins, & Globerson (1991) differentiate cognitive effects with and effects of technology. Effects with technology comprise intellectual
• Tsai, C. W., Shen, P. D., & Chiang, I. C. (2018). Investigating the effects of ubiquitous self-organized learning and learners-as-designers to improve students’ learning performance, academic motivation, and engagement in a cloud course. Universal Access in the Information Society, 1-16.