Using Concept Maps to Enhance Students' Prior Knowledge in Complex Learning

Using Concept Maps to Enhance Students' Prior Knowledge in Complex Learning

Robert Z. Zheng (University of Utah, USA) and Laura B. Dahl (University of Utah, USA)
DOI: 10.4018/978-1-60566-782-9.ch010
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Abstract

As an instructional tool, concept map has been widely used to teach complex subjects in schools. Research suggests that concept mapping can help bridge learners’ prior knowledge with new learning, reduce the cognitive load involved in learning and improve comprehension, content retention, and knowledge transfer. Existing literature focuses on cognitive features, cognitive styles and differences between instructor provided and student generated concepts. However, little is known about the effects of concept maps as a cognitive tool to influence learners’ learning, specifically before and after the learning takes place. This chapter offers a discussion of general research in concept mapping and theories that support such instruction. Finally, an empirical study is presented with suggestions for future research in concept mapping.
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Introduction

Like other types of learning, complex learning poses considerable challenges to learners due to its high demands on cognitive resources, prior knowledge and information processing (Grice, 1987; Schwartz & Bransford, 1998; Zheng, McAlack, Wilmes, Kohler-Evans, & Williamson, 2009). For many, prior knowledge activation resonates with meaningful learning (Surber & Schroeder, 2007; Winberg & Hedman, 2008). However, Schwartz and Bransford (1998) pointed out that learning can be “problematic if students do not have the relevant prior knowledge to begin with” (p. 475). Thus, how to effectively develop learners’ prior knowledge becomes a focal point for many researchers who explore the issues from the perspectives of cognitive structures (Kinchin, Hay, & Adams, 2000) and memory related instructional pedagogies (Lee, Plass, & Homer, 2006). Par with the prior knowledge research is the focus on cognitive resources in complex learning. Since complex learning requires a high degree of element interactivity and there is a limitation to human capacity in dealing simultaneously with multiple elements (Baddeley & Hitch, 1974; Sweller & Chandler, 1991, 1994), it becomes critical that instruction address the issue of how to optimize learners’ cognitive resources in complex learning, particularly using modern learning technologies such as multimedia and hypermedia. Recent studies have successfully proved that appropriately designed multimedia instruction can significantly reduce learners’ cognitive load, hence enhance their abilities in complex learning (Mayer & Moreno, 2003; Zheng et al., 2009).

Among the efforts to improve learners’ abilities in complex learning is the application of concept map which is used as a tool to facilitate prior knowledge construction and activation as well as to optimize cognitive resources for deep learning. For example, Puntambekar and Goldstein (2007) observed learners who applied concept maps to science learning and found that learners who learned with concept maps were able to navigate better through the content and engage in deep learning. In a separate study, Roberts and Joiner (2007) used the concept map as an educational strategy to help a group of autistic students learn science. Results showed that students with concept mapping condition outperformed those without. Despite the fact that concept mapping has displayed proven educational benefits for learners, its use in schools and classrooms does not seem to be widespread (Kinchin, 2001). Kinchin concluded that school ecology (i.e., the existing curricular structure and the underlying philosophy of curriculum) as well as teachers’ epistemology may hinder the use of concept map in schools. Existing literature focuses on cognitive features, cognitive styles and differences between instructor provided and student generated concepts (Roberts & Joiner, 2007; Puntambekar & Goldstein, 2007; Shmaefsky, 2007). However, little is known about the effects of concept maps as a cognitive tool to influence learners’ learning, specifically before and after the learning takes place. This chapter offers a discussion of general research in concept mapping and theories that support such instruction. The chapter starts with defining the concept map and describing the status of practices and research in concept mapping, followed by a discussion on complex learning and cognitive issues involved in complex learning. Review of cognitive learning theories will be made with emphases on working memory theory (Baddeley, 1986), dual-coding theory (Paivio, 1986) and cognitive load theory (Sweller, 1988). Finally, an empirical study will be presented with discussions and suggestions for future research in concept mapping.

Key Terms in this Chapter

Complex learning: involves the activation of multiple elements to comprehend rules and principles, understand relationships among various entities and solve problems. It differs from simple learning in that it requires a high level of mental effort. For example, learning vocabulary is a simple learning process because the elements of the material to be learned do not interact with each other. The mental effort involved in simple learning is low. However, if the learning involves syntax, the elements of information that must be learned may be difficult to assimilate, because they cannot be acquired in isolation. In this case, learning gets complicated which requires high level of mental effort due to its high degree of element interactivity.

Concept Map: Are graphical ways of working with ideas and presenting information. They reveal patterns and relationships and help students to clarify their thinking, and to process, organize and prioritize. The visual representation of information through word webs or diagrams enables learners to see how the ideas are connected and understand how to group or organize information effectively.

Working Memory: Working memory is a theoretical framework that refers to the structures and processes used for temporarily storing and manipulating information. According to Baddeley and Hitch (1974), the working memory consists of two “slave systems” responsible for short-term maintenance of information, and a “central executive” responsible for the supervision of information integration and for coordinating the slave systems. One slave system, the articulatory loop, stores phonological information and prevents its decay by silently articulating its contents, thereby refreshing the information in a rehearsal loop. The other slave system, the visuo-spatial sketch pad, stores visual and spatial information. It can be used, for example, for constructing and manipulating visual images, and for the representation of mental maps. The sketch pad can be further broken down into a visual subsystem (dealing with, for instance, shape, color, and texture), and a spatial subsystem (dealing with location). The central executive system is, among other things, responsible for directing attention to relevant information, suppressing irrelevant information and inappropriate actions, and coordinating cognitive processes when more than one task must be done at the same time. Studies show that the working memory is very limited in both duration and capacity. The working memory typically stores about seven elements but normally operates on only two or three elements.

Cognitive Structures: Refer to patterns of human thinking processes which, according to Kinchin and Hay (2000), can be categorized as spoke, chain, and network. Spoke thinking reflects a central to peripheral node relationship in which simple association exists with no understanding of processes or interactions. It is marked by a low level complexity. Chain thinking shows a temporal sequence with logic relations between the nodes. It is hierarchical and accumulative. Thus, loss of one link can lose the meaning of whole chain. Network thinking has the highest level of complexity. It is marked by a high level of element interactivity where nodes are related in multiple ways. In network thinking, missing one link has little consequences as “other routes” through the network thinking are available, thus can compensate for the missing link. One important feature of network thinking is constant reorganization, a process that refines, organizes and prioritizes information.

Cognitive Load: According to Cognitive Load Theory (CLT), three types of cognitive load exist: intrinsic load, extraneous or ineffective load, and germane or effective load. The intrinsic cognitive load refers to cognitive load that is induced by the structure and complexity of the instructional material. Usually, teachers or instructional designers can do little to influence the intrinsic cognitive load. The extraneous cognitive load is referred to the cognitive load caused by the format and manner in which information is presented. For example, teachers may unwittingly increase learner’s extraneous cognitive load by presenting materials that “require students to mentally integrate mutually referring, disparate sources of information” (Sweller et al., 1991, p.353). Finally, the germane cognitive load refers to cognitive load that is induced by learners’ efforts to process and comprehend the material. The goal of CLT is to increase this type of cognitive load so that the learner can have more cognitive resources available to solve problems (Brunken, Plass, & Leutner, 2003; Marcus, et al., 1996).

Prior Knowledge: Prior knowledge refers to the knowledge which includes facts, concepts, rules, principles, and relationship between concepts, rules, and principles, in a specific domain. This type of prior knowledge is stored in an individual’s schema. Learning suffers if learners lack sufficient prior knowledge. Without prior knowledge, learners are limited in their ability to construct new knowledge (Potelle & Rouet, 2003). As a result, new learning can be very difficult and very tiring (Novak, 1998).

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