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PRINCIPLE 9:
Designers Should Scaffold learners to Organize
Their Understanding of the Problem
Younghoon Kim, Steven McGee, & Namsoo Shin
Copyright © 2003

What is organized knowledge?
Organized knowledge refers to how a student’s memory is organized (Gobbo & Chi, 1986; Goldsmith, Johnson, & Acton, 1991; Hunt & Ellis, 1999; Shavelson, 1972, 1974). It’s also known as cognitive structure, knowledge structure, and structural knowledge. Cognitive psychologists hypothesize that relevant concepts or schemata in a specific knowledge domain are interconnected and interrelated in an expert’s long-term memory (Hunt & Ellis, 1999). Learners acquire knowledge and continually organize it as they integrate it into existing knowledge structure stored in memory (Hunt & Ellis, 1999). Learners use their existing knowledge structures to interpret new scientific concepts, data, or information necessary to perform more complex learning tasks like problem solving (Glynn & Duit, 1995).

Why is organized knowledge important?

Organized knowledge leads to better understanding of the subject. Learner with a well-organized knowledge base in a particular domain generally understand better than those with less organized knowledge (Alexander & Judy, 1988). Well-organized knowledge means that key concepts in a content domain are closely and correctly interrelated, integrated, and cohesive. That lets learners better use and access their knowledge (Gobbo & Chi, 1986). According to science learning research, inaccurate or incomplete knowledge (e.g., misconceptions) does not help comprehension and will eventually interfere with learning (Alexander & Judy, 1988).

Organized knowledge facilitates problem solving. How learners organize knowledge in their own memory plays a major role in solving problems successfully, research reports. Problem solving requires learners to apply their organized knowledge to a novel problem (Mayer, 1999). In Gobbo and Chi’s study (1986) expert learners with well-organized knowledge used their knowledge in a more sophisticated and accessible way (e.g., inferring and reasoning) than novice learners did. Thus, successful problem solving depends on how well learners organize the knowledge necessary for solving problems.

Organized knowledge is necessary for the efficient and effective use of metacognitive strategies. Experts’ knowledge organization in a domain can enable them to use metacognitive strategies to successfully complete a task (Alexander & Judy, 1988). If learners don’t posses a well-organized knowledge in a domain, they can’t solve problems effectively, according to Alexander and Judy.

How does a designer support learners’ knowledge organization?
The following teaching strategies help learners understand. The strategies grow out of a wide range of empirical research in various content domains.

Help learners identify key concepts and represent the connections between key concepts. Guidance helps learners identify key concepts or principles related to a problem while they are exploring given information. Guidance enables learners to think about and focus on key conceptual knowledge necessary to solve a problem (Quintana, 2002; Hannafin, Land, & Oliver, 1999). The specific strategies are:

  1. Use outlining or summarizing strategy. This strategy supports learners’ cognitive processing in selecting and organizing information (Mayer, 1999). When learners read, teachers ask them to outline or summarize information in the learners’ own words. This strategy lets learners recognize and organize key concepts from instructional materials they are studying. The key is for learners to combine ideas from materials and their own understanding instead of simply copying key ideas from the materials (Wittrock, 1990).

  2. Use questioning strategy. Questioning strategy maximizes reading comprehension. It also generally helps learners identify key facts and ideas, integrate new information with their existing knowledge, and refine their conceptual understandings through conversation with their teacher (Mayer, 1999; Wittrock, 1990). Different types of questions require different levels of cognitive processing and learning (Grabowski, 1996). For instance, “what” questions (e.g., what is the solar system?) focus mainly on learners’ conceptual understanding. They force learners to organize and elaborate key concepts and ideas. “Why” questions (e.g., why is the balance of ecosystem important?) spur learners’ higher-order thinking. They ask learners to apply their understanding to a situation. In addition, activities that force learners to ask and answer their own questions increase learners’ participation in the learning process (Wittrock, 1990).

  3. Use graphic representation of knowledge. Graphic representation (e.g., diagrams, illustrations, and concept maps) of knowledge shows relationships among concepts or of cause-effect relationship in a content domain (Jonassen, 2000). This helps learners build their own understanding of information they study. There are two ways to use graphic representation in the classroom (Jonassen, Beissner, & Yacci, 1993): 1) A designer creates the graphic representation to help people learn. 2) Learner create their own graphics as they study. According to Novak et. al. (1983) and Jonassen (2000), the latter approach helps participants learn better.

Provide feedback about learners’ knowledge representation. Providing feedback on learners’ representations of causal relationship is important. It lets learners know whether their representations are appropriate and engaging (Baumgartner & Bell, 2002; Jonassen, 2000). When learners evaluate their representations, they revise and refine their representations. That indicates a meaningful thinking process (Jonassen, 2000).

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References.
Alexander, P. A., & Judy, J. E. (1988, Winter). The interaction of domain-specific and strategic knowledge in academic performance. Review of Educational Research, 58(4), 375-404.

Baumgartner, E., & Bell, P. (2002). What will we do with design principles? Design principles and principled design practice. Paper presented at the annual meeting of the American Educational Research Association, New Orleans.

Beissner, K., Jonassen, D. H., & Grabowski, B. L. (1994). Using and selecting graphic techniques to acquire structural knowledge. Performance Improvement Quarterly, 7(4), 20-38.

Glynn, S. M., & Duit, R. (1995). Learning science in the schools: Research reforming practice. Mahwah, NJ: Lawrence Erlbaum Associates.

Gobbo, C., & Chi, M. (1986). How knowledge is structured and used by expert and novice children. Cognitive Development, 1, 221-237.

Goldsmith, T. E., Johnson, P. J., & Acton, W. H. (1991). Assessing structural knowledge. Journal of Educational Psychology, 83, 88-96.

Grabowski, B. L. (1996). Generative learning: Past, present, and future. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology, 897-913. New York: Simon and Schuster Macmillan.

Hannafin, M., Land, S., & Oliver, K. (1999). Open learning environments: Foundations, methods, and models. In Charles M. Reigeluth (ed.), Instructional design theories and models: A new paradigm of instructional theory (volume ii). Mahwah, NJ: Lawrence Erlbaum Associates.

Hunt, R. R., & Ellis, H. C. (1999). Fundamentals of cognitive psychology (6th ed.). Boston: McGraw-Hill College.

Jonassen, D. H., Beissner, K., & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale, NJ: Lawrence Erlbaum Associates.

Jonassen, D. H. (2000). Computers as mindtools for schools: Engaging critical thinking (2nd ed.). Upper Saddle River, NJ: Prentice-Hall Inc.

Mayer, R. H. (1999). Designing instruction for constructivist learning. In C. M. Reigeluth (ed.), Instructional design theories and models: A new paradigm of instructional theory (vol. II). Mahwah, NJ: Lawrence Erlbaum Associates.

Novak, et al. (1983). The use of concept mapping and knowledge Vee mapping with junior high school science students. Science Education, 67(5), 625-45.

Quintana, C. (2002). Design principles for educational software: Using process maps to describe to space of possible activities for learners. Paper presented at the annual conference of the American Educational Research Association. New Orleans.

Shavelson, R. J. (1974). Methods for examining representations of a subject-matter structure in a student’ memory. Journal of Research in Science Teaching, 11(3), 231-249.

Shavelson, R. J. (1972). Some aspects of the correspondence between content structure and cognitive structure in physics instruction. Journal of Educational Psychology, 63(3), 225-234.

Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partner in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 2-9.

Wittrock, M.C. (1990). Generative processes of comprehension. Educational Psychologists, 24, 345-76.

 

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