Improving Critical Thinking Through Argument Mapping

As you may have figured out from the focus of my ongoing blog, my book and my previous research, critical thinking (CT) is my specialty area of research. However, perhaps something that I don’t mention enough within this blog is that CT wasn’t the primary focus of my Ph.D. research—rather, it was The Evaluation of Argument Mapping as a Learning Tool; that is, argument mapping’s effects on a series of educational outcomes, including memory and CT. To clarify, an argument map is a visual representation of a logically structured network of reasoning, in which the argument is made unambiguous and explicit via a ‘box and arrow’ design, in which the boxes represent propositions (i.e. the central claim, reasons, objections, and rebuttals) and the ‘arrows’ among propositions indicate the inferential relationships linking the propositions together (Dwyer, 2011; van Gelder, 2002). As part of my Ph.D., three large-scale experimental studies were conducted with the main results indicating that argument mapping (AM) can significantly facilitate memory performance beyond that of more traditional study methods and that the provision of AM-infused CT training can significantly enhance CT performance (Dwyer, 2011). Given these observed benefits, I think it worthwhile to share a little bit about AM here and the rationale for why it works, especially for those who wish to enhance their own or even others’ CT.

An example of an argument map

Source: (Dwyer, 2011; van Gelder, 2007)

Notably, though other forms of argument diagramming exist, such as concept mapping and mind-mapping, they differ substantially from AM based on the manner in which they are organized and the way in which each ‘proposition’ is presented. The problem with many concept mapping techniques is that they do not present an argument per se. Instead, they present a graphical structure that acts as a representation of a separate text, which might be used to diagram: the links among concepts, decision-making schemes, a set of plans or instructions, or at best, act as an argument overview – which does not represent the argument in full. Thus, because the text of the argument and the diagram may often be separate entities, concept mapping may become more cognitively demanding by adding the necessity of switching attention from text to diagram and vice versa (e.g. Chandler & Sweller, 1991; Pollock, Chandler & Sweller, 2002; Tindall-Ford, Chandler & Sweller, 1997). In addition, if the reader of a concept map is not familiar with the information from the text that the map is derived, then the map itself becomes meaningless. Neither sentences nor any inferential structures to facilitate comprehension are requisite. In this context, concept mapping strategies may not necessarily be useful pedagogical aids that are open to analysis by everyone.

Although AMs have been in existence for almost 200 years (Buckingham-Shum, 2003; see Whately, 1826), their construction was a slow, tedious task completed through pen and paper; and thus, not widely used as a learning tool, despite potential advantages over standard prose as a medium for presenting reasoning. With the advent of various user-friendly AM software programs, the time required to construct an AM has been substantially reduced. Perhaps as a result of the relatively recent advancements in AM software, little research has been conducted to test its effects on learning. However, the little research that has examined AM’s effects on CT has revealed beneficial effects (Alvarez-Ortiz, 2007; Butchart et al., 2009; Dwyer, Hogan & Stewart, 2011; Dwyer, Hogan & Stewart, 2012; van Gelder, 2001; van Gelder, Bissett & Cumming, 2004). The rationale for why AM has a beneficial effect on CT consists of reasoning pertaining to the former’s diagrammatic, dual-coding nature, Gestalt grouping principles and hierarchical organization.

First, unlike standard text, AMs represent arguments through dual modalities (visual-spatial/diagrammatic and verbal/propositional), thus facilitating the latent information processing capacity of individual learners. Dual-coding theory (Paivio, 1971; 1986), Mayer’s (1997) conceptualisation and empirical analysis of multimedia learning, as well as Sweller and colleagues’ research on cognitive load (Sweller, 2010) suggest that learning can be enhanced and cognitive load decreased by the presentation of information in a visual-verbal dual-modality, provided that both visual and verbal forms of representation are adequately integrated (i.e. to avoid attention-switching demands). Given that AM supports dual-coding of information in working memory via integration of text into a diagrammatic representation, cognitive resources previously devoted to translating prose-based arguments into a coherent, organised and integrated representation are ‘freed up’ and can be used to facilitate deeper encoding of arguments within AMs, which in turn facilitates later recall (e.g. Craik & Watkins, 1973), as well as subsequent, higher-order thinking processes, such as CT (Halpern, 2014; Maybery, Bain and Halford, 1986). Furthermore, previous research on using diagrammatic learning tools, like AM, has shown positive effects on learning outcomes (Berkowitz, 1986; Larkin & Simon, 1987; Oliver 2009; Robinson & Kiewra, 1995) and offers advantages over traditional text-based presentation of information because the indexing and structuring of information can potentially support essential computational processes necessary for CT.

Second, AMs utilize Gestalt grouping principles (e.g. similar color-coding and close proximity) that facilitate the organization of information in working memory and long-term memory, which in turn facilitates CT. For example, color can be used in an AM to distinguish evidence for a claim (i.e. green) from evidence against a claim (i.e. red); thus, all reasons are similarly color-coded, as are objections. More generally, a good AM is designed in such a way that if one proposition is evidence for another, the two will be appropriately juxtaposed and the link explained via a relational cue, such as because, but and however (van Gelder, 2001).

With respect to proximity, modern AM allows single propositions or entire branches of the argument to be removed or transferred from one location to another (and edited in the process) in order to facilitate reconstruction. The manner in which propositions and chains of reasoning can be manipulated within an AM may encourage deeper analysis and evaluation of the argument, as well as further refinements of its inferential structure. Similar propositions can be grouped together, which eases their assimilation and removes the need to switch attention as in text-based information (e.g. from one paragraph, or even one page, to another and back and forth). Such grouping also makes the search for specific, relevant information more efficient, which in turn supports perceptual inferences.

Finally, the third potential reason for why AM has a beneficial effect on CT is that AMs present information in a hierarchical manner, which also facilitates the organization of information for promoting CT. When arguing from a central claim, one may present any number of argument levels which need to be adequately represented for the argument to be properly conveyed. For example, an argument that provides a (1) support for a (2) support for a (3) support for a (4) claim has four levels in its hierarchical structure. More complex or ‘deeper’ arguments (e.g. with three or more argument levels beneath a central claim) are difficult to represent in text due to its linear nature; and yet it is essential that these complex argument structures are understood by a student if their goal is to analyze and evaluate the argument, to infer their own conclusions. The hierarchical nature of AM allows the reader to choose and follow a specific branch of the argument in which each individual proposition is integrated with other relevant propositions in terms of their inferential relationship.

Moreover, asking students to produce AMs can provide educators with valuable insights into a student’s ‘mental model of the argument in question’ (Butchart et al., 2009). Such information can be used to support teachers in offering feedback to students or scaffolding student learning from simple to complex levels of argument comprehension, analysis, and evaluation. Logically, as expertise in AM grows, so does the ability to present a well-structured argument, which allows for improvement in writing ability as well.