Designing
Investigations
Designers Should Scaffold Students
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). Students
acquire knowledge and continually organize it as they integrate it
into existing knowledge structure stored in memory (Hunt & Ellis,
1999). Students 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.
Students 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 students 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 students organize knowledge in their own memory plays a major
role in solving problems successfully, research reports. Problem solving
requires students to apply their organized knowledge to a novel problem
(Mayer, 1999). In Gobbo and Chi’s study (1986) expert students
with well-organized knowledge used their knowledge in a more sophisticated
and accessible way (e.g., inferring and reasoning) than novice students
did. Thus, successful problem solving depends on how well students
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 students 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
students’ knowledge organization?
The following teaching strategies help students understand.
The strategies grow out of a wide range of empirical research in various
content domains.
- Help students identify key concepts and represent the connections
between key concepts.
Guidance helps students identify key concepts or principles related
to a problem while they are exploring given information. Guidance
enables students 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 students’ cognitive processing in selecting
and organizing information (Mayer, 1999). When students read,
teachers ask them to outline or summarize information in the students’
own words. This strategy lets students recognize and organize
key concepts from instructional materials they are studying. The
key is for students 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 students
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 students’ conceptual understanding. They
force students to organize and elaborate key concepts and ideas.
“Why” questions (e.g., why is the balance of ecosystem
important?) spur students’ higher-order thinking. They ask
students to apply their understanding to a situation. In addition,
activities that force students to ask and answer their own questions
increase students’ 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
students 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 students learn. 2) Students
create their own graphics as they study. According to Novak et.
al. (1983) and Jonassen (2000), the latter approach helps students
learn better.
- Provide feedback about students’ knowledge representation.
Providing feedback on students’ representations of causal relationship
is important. It lets students know whether their representations
are appropriate and engaging (Baumgartner & Bell, 2002; Jonassen,
2000). When students evaluate their representations, they revise and
refine their representations. That indicates a meaningful thinking
process (Jonassen, 2000).
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.
|