Encyclopedia of Educational Philosophy and Theory

2017 Edition
| Editors: Michael A. Peters

Argument Mapping Software: Semiotic Foundations

Reference work entry
DOI: https://doi.org/10.1007/978-981-287-588-4_27



The abilities to argue, to evaluate the quality of argumentative reasoning, and to structure reasoning by means of arguments, all these argument-related skills are crucial not only for the scientific mind but for everybody who strives for a self-determined life without external manipulation, for competent decision making about important matters of life, for self-confident interaction with others, and for participation in public deliberation. Reacting to the need to teach the skills of argument, researchers in the areas of education, philosophy, and computer science developed over the last decades a large number of software tools known as “argument mapping software” or “computer-supported argument visualization” (CSAV) tools.

Based on the fact that any argument needs to be represented in one way or another, and given the large variety of software tools that allow the visualization, mapping, or diagramming of arguments, it turned out to be helpful – especially for the design of future CSAV tools – to study the construction of arguments not only from a conceptual and empirical point of view but also from a semiotic point of view. Semiotics – the theory of signs and representations – should provide important conceptual and theoretical means to understand what happens when people visualize arguments and when they learn by studying and reconstructing arguments.

The following considerations on the semiotic foundations of CSAV tools concentrate on two questions: What can we learn from semiotics about ways to acquire the ability to construct clear and convincing arguments by means of CSAV? How can a semiotic reflection help to determine principles and requirements for the design of argument visualization software? After describing some of the challenges that CSAV attempts to address in education, this contribution differentiates several approaches to “argument” and “argument visualization” in the educational literature and discusses semiotic foundations of CSAV based on Charles S. Peirce’s work on conditions that are required for interpreting signs and for learning by experimenting with diagrams.

Why Computer-Supported Argument Visualization?

The book title Arguing to Learn (Andriessen et al. 2003) may be the best expression of the assumption that there is a close relationship between the construction of arguments and learning. This is convincing especially for the learning of scientific reasoning and the content taught in science education. Since in science every theory and every thesis has to be justified by evidence, learning science needs to go hand in hand with learning how to argue.

Software tools have the potential to support the construction of arguments which is especially important in settings where students collaborate on their own in small groups. CSAV can help to scaffold student argumentation by providing specific structures that guide both interaction among students and the construction and experimental manipulation of arguments.

The same functionality of CSAV – providing structure for both interaction and understanding of possible organizations of content – has been the focus of research that emphasizes that an important skill students should acquire is the ability to cope with “wicked problems.” Horst Rittel and Melvin Webber defined “wicked problems,” in contrast to “tame problems,” as those problems whose understanding depends on someone’s point of view. “There is no definitive formulation of a wicked problem.” “The formulation of a wicked problem is the problem!” (Rittel and Webber 1973, p. 161). Based on a variety of different perspectives – each representing specific needs, interests, world views, beliefs, and values of people – problems and possible solutions can be “framed” in a variety of ways.

Rittel and Webber recommended that the multi-perspectivity of wicked problems should be addressed based on “an argumentative process in the course of which an image of the problem and of the solution emerges gradually among the participants, as a product of incessant judgment, subjected to critical argument” (Rittel and Webber 1973, p. 162). CSAV tools promise to support students in their collaboration on wicked problems (Kirschner et al. 2003).

Other educational uses that have been suggested for CSAV include the training of critical thinking and stimulating reflection on the quality of one’s own arguments and reasoning.

Different Approaches to “Argument” and “Argument Visualization”

Available argument mapping software addresses these educational challenges in a variety of ways which are connected to different conceptualizations of what an “argument” is. Three definitions can be distinguished. The first one originates in philosophy. It defines, in one of many similar forms, an argument as a set of one or more premise-conclusion sequences so that either one or more premises are intended to support a conclusion or a conclusion is intended to be justified by one or more premises. A second understanding of argument adds to this first one a focus on the ability to supplement any position with a counterposition (“pros and cons” or “confronting cognitions;” see Andriessen et al. 2003).

A third tradition in educational argument theory uses “argument” in a broader sense that is influenced by work on “wicked problems.” Since the focus is here on clarifying “issues,” many scholars include in the process of argument visualization also activities such as formulating questions, ideas, pros, and cons, problem solving, the generation of hypotheses and evaluation criteria, expressing doubt and disbelief, and reifying, contrasting, criticizing, and integrating perspectives (Kirschner et al. 2003).

Semiotic Foundations

Whatever definition of argument one prefers, there are at least two areas of theory development in semiotics that can contribute to a better understanding of problems related to learning by means of CSAV on the one hand and to the determination of principles and requirements for the design of argument mapping software on the other. The first of these concerns reflections on the conditions of interpreting signs and representations adequately. Since CSAV is all about visualizing arguments, the problem of interpreting of what can be seen is fundamental. The second relevant area of semiotics discusses conditions of how we can learn and reason by means of external representations via what Charles S. Peirce called diagrammatic reasoning.

Interpreting Signs and Representations

Charles S. Peirce – who was the first in modern times to develop the fundamentals of semiotics from a philosophical point of view – always claimed that a sign fulfills its function to represent something for someone (or for something other) as “a medium” that is embedded in a triadic relation: it is determined by its object and it determines its “interpretant.” Later in his life, Peirce understood this “interpretant” in a rather formal sense as “the proper significant outcome of a sign” or its “proper significate effect” (Peirce, 1931–1958, CP 5.473 and 475). However, if we ask what it takes to interpret a sign correctly, then it seems necessary – as Hoffmann and Roth (2010) argued – to take a fourth element into account besides object, sign, and interpretant: background knowledge about the sign’s meaning that someone needs to activate in order to either interpret a sign according to what he or she already knows about it or create new meanings. Peirce described this – very late in his life – as “collateral knowledge” and argued that “no sign can be understood… unless the interpreter has ‘collateral acquaintance’ with every object of it” (Peirce, 1931–1958, CP 8.183).

This insight is indeed crucial for teaching. When we teach the analysis or construction of arguments, we talk about “reasons,” “conclusions,” and other things we take for granted, but do we really know that students interpret these concepts in the same way we do? A case in point is Deanna Kuhn’s famous study on the skills of argument that demonstrated that 60% of participants were not able to provide “genuine evidence” for a theory they themselves formulated to explain things such as school failure and unemployment (Kuhn 1991). Looking at the transcriptions of some of the interviews her team conducted, it seems more likely that many of the participants simply didn’t have any clue what the term “evidence” really means and what the difference is between answering the question “how do you know that this is the cause” and just telling a story about a causal relation.

For the design of CSAV tools, the insight that the correct interpretation of signs and representations depends on the availability of the right collateral knowledge is particularly important for the graphical elements that are used to visualize arguments. For example, it seems obvious that the separation of reasons and conclusions into different text boxes helps students to “see” the structure of an argument. However, it might not be clear what exactly the meaning of the arrow is that connects these text boxes. Does it mean “therefore” or “because”? Even if the direction is clear, the arrow alone is not sufficient to distinguish in the case of pq between “p therefore q” and “p since q.” In the first case p is the reason, while it is the conclusion in the second case. Thus, it might be helpful to provide necessary collateral knowledge in the representation itself by naming graphical elements explicitly, for example, by adding “therefore” or “because” to arrows.

The Rules of Representational Systems

A second area of semiotics that is relevant here is informed by Peirce’s work on “diagrammatic reasoning.” It is relevant especially for a better understanding of how students can learn how to improve the quality of their own arguments and for a better understanding of how CSAV can support them in their efforts. Diagrammatic reasoning is reasoning by means of representations which visualize, in particular, structures and relations. A central idea of diagrammatic reasoning is that an external visualization of what we think about the issue in question allows us to identify problems and gaps in our own thinking, leads to the identification of relations we were not aware of before, and stimulates thus creativity and learning (Stjernfelt 2007; Hoffmann 2011; Semetsky 2013).

A “diagram,” for Peirce, is a representation whose main function is to represent a certain group of relations, namely, those relations that are rationally comprehensible. Relations are rationally comprehensible if they can be represented in a “consistent system of representation” (Peirce, 1931–1958, CP 4.418).

The rational character of diagrams is crucial for diagrammatic reasoning. Peirce defines diagrammatic reasoning as reasoning with diagrams that are constructed by the means provided by a certain system of representation – be it a logical system, or an axiomatic system as in geometry, or simply the vocabulary and grammar of a language. Such reasoning with diagrams is realized when we experiment with a diagram according to the rules of the chosen representational system. Experimenting with diagrams, transforming them according to the rules of the system, and observing what happens are crucial for scientific discoveries and the development of new knowledge. The reason is that such experimentation can lead to the discovery of regularities and of “relations between elements which before seemed to have no necessary connection” (Peirce, 1931–1958 CP 1.383), which again can lead to the creation of new concepts and theories. Examples in the history of science are the discovery of incommensurability in geometry and irrational numbers in arithmetic, the formulation of Desargues’ theorem in projective geometry, and Maxwell’s development of the electromagnetic field concept. In all these cases, it is important that the discovery of something new is conditioned on the rules of the chosen representational system. If there were no rules according to which experiments are performed in diagrammatic reasoning, it would never be possible to distinguish arbitrary observations from those that can be used to create new knowledge.

In the context of education, and especially considering an edusemiotic perspective taken by educational theory and pedagogical practice, the discovery of new knowledge is no less important than other significant functions of diagrammatic reasoning: to structure and organize one’s reasoning and to support the evaluation and improvement of reasoning. Diagrammatic reasoning can help students to reflect on their reasoning without being constrained by the limits of their working memory, analyze a problem more thoroughly and systematically, clarify and coordinate confused ideas about a problem, clarify implicit assumptions, identify background knowledge that might be inadequate, structure a problem space, change perspectives, identify unexpected implications, play with interpretations, discover contradictions, and distinguish the essential from the peripheral (Hoffmann 2011). However, a precondition for reflecting on one’s own reasoning process by means of diagrammatic reasoning is that students have clear standards at their disposal to evaluate the quality of their reasoning. There need to be criteria with respect to which reasoning can be assessed. Those criteria are given in the rules of the representational systems according to which diagrams are constructed and experiments performed. Logic, for example, provides rules that can be used to perform self-controlled reasoning. We control our reasoning by comparing it with the norms of logic.

Logic, however, is only one possible normative standard for self-controlled reasoning. The “critical questions” that Douglas Walton and his colleagues developed for each of a multitude of different argument schemes fulfill the same function (Walton et al. 2008). They provide a standard that students can use to assess – and to improve, if necessary – the arguments they encounter or create.

A major opportunity that CSAV provides in this regard is that the rules of representational systems can be implemented in the user interface of software. This has been done, for example, in the collaborative argument visualization software AGORA-net (http://agora.gatech.edu/). Here, the user needs to select a logically valid argument scheme to complete the construction of an argument. This selection creates automatically a further premise which transforms the given argument into a logical argument, directing thus the attention to the question how premises and conclusion should be formulated or the structure of the argument changed, to create a more convincing argument.

What edusemiotics, and specifically the semiotics of Charles S. Peirce, can show, thus, is that a necessary condition for learning something by means of diagrammatic reasoning is knowing and accepting the rules of a chosen system of representation, a system by means of which diagrams can be constructed and experiments be performed. Such a system constrains reasoning in a way that our cognitive energy gets focused on points that are crucial for reflection on one’s own reasoning – just like a fireman’s jet of water will be the more focused the more it is constrained. Without any constraints there would be no direction for our reasoning.


“Everybody takes the limits of his/her own field of vision for the limits of the world,” wrote Arthur Schopenhauer. Education always aimed at extending the limits of our vision and at broadening our horizon. But how can we teach the abilities to recognize and to overcome cognitive limits in a more focused way? Computer-supported argument visualization (CSAV) has been proposed as a method to support the development of these abilities. Not only does CSAV promise to support the development of argumentation skills – that is, the ability to construct clear and convincing arguments and to evaluate the quality of arguments and argumentative reasoning – it also promises to stimulate self-reflection, creativity, and cognitive or conceptual change. When we use argument mapping software to support a position on an issue or to justify a thesis, claim, or recommendation, we can learn something about our own thinking. Every argument that we construct shows what we know, what we believe, and often also which values or ethical principles drive our reasoning. Argument mapping, thus, allows us to reflect on our knowledge, beliefs, and values and on how they are related in argumentative structures.

Additionally, graphical representations and diagrams in general, and argument mapping in particular, can help to represent scientific controversies and dilemmas and to structure planning processes and deliberation. And it can teach students the ability to cope, in collaboration with others, with wicked problems and to resolve conflicts that are determined by clashing values, ideas, and world views.

Since the “visualization” in “argument visualization” is crucial for CSAV, semiotics has been proven to be an important theoretical tool to reflect on some of the foundations of CSAV. As edusemiotics in general shows, representations have to be interpreted, and representational systems have certain cognitive effects. To understand how CSAV affects learning and how CSAV should be designed to achieve and optimize certain educational effects, semiotics can play an important role.


  1. Andriessen, J. E. B., Baker, M., & Suthers, D. D. (Eds.). (2003). Arguing to learn. Confronting cognitions in computer-supported collaborative learning environments. Dordrecht: Kluwer.Google Scholar
  2. Hoffmann, M. H. G. (2011). Cognitive conditions of diagrammatic reasoning. Semiotica, 186(1/4), 189–212.Google Scholar
  3. Hoffmann, M. H. G., & Roth, W.-M. (2010). Four functions of signs in learning and interdisciplinary collaboration. In I. Semetsky (Ed.), Semiotics education experience (pp. 131–150). Rotterdam: Sense Publishers.Google Scholar
  4. Kirschner, P. A., Buckingham Shum, S. J., & Carr, C. S. (Eds.). (2003). Visualizing argumentation: Software tools for collaborative and educational sense-making. London: Springer.Google Scholar
  5. Kuhn, D. (1991). The skills of argument. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. Peirce. (CP). (1931–1958) Collected papers of Charles Sanders Peirce. Cambridge, MA: Harvard University Press.Google Scholar
  7. Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4, 155–169.CrossRefGoogle Scholar
  8. Semetsky, I. (2013). The edusemiotics of images: Essays on the art ~ science of Tarot. Rotterdam: Sense Publishers.CrossRefGoogle Scholar
  9. Stjernfelt, F. (2007). Diagrammatology: An investigation on the borderlines of phenomenology, ontology, and semiotics. Dordrecht: Springer.CrossRefGoogle Scholar
  10. Walton, D. N., Reed, C., & Macagno, F. (2008). Argumentation schemes. Cambridge/New York: Cambridge University Press.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Georgia Institute of TechnologyAtlantaUSA