Application Knowledge II

Application knowledge is a crucial component in students' ability to solve problems [1]. Students often possess a rudimentary procedural knowledge of how to carry out certain procedures, but do not posses the application knowledge about when to carry out those procedures when solving problems. For example, possessing application knowledge of the physics generalizations for diffusion, photosynthesis and velocity is necessary in solving many chemical, biological and physical problems [2]. Application knowledge of the mathematics generalization for the addition principle in probability is necessary in solving many mathematics problems [3]. It is analogous in playing chess to knowing how to properly move chess pieces on a board (which novices have), but not possessing an application knowledge of numerous key playing positions and the most appropriate next move or chess pieces for each position (which experts have) [4].

A classic study of novices and experts in college physics demonstrates the relationship between application knowledge of physics generalizations and ability in problem solving [5]. Undergraduate physics majors (novices) and college faculty in physics (experts) were individually given 24 cards showing physics problems they had not seen before and were asked to sort the problems into six or more separate categories in any way they liked, and to do so in less than a minute. The problems were depicted as drawings on the cards, such as a drawing of two weights joined by a rope that ran over a pulley. When they finished sorting they were asked to explain the rationale for their classifications. The members of both groups were alike in that they created about 8 categories, and finished in about 40 seconds. But they were unlike in the kinds of categories they created, and in the explanations they gave. Experts sorted the cards into categories corresponding to the physics generalizations exemplified in the problems. Consequently, some problems with pulleys were placed in a pile with some problems having inclined planes if they were examples of the same physics generalization. Experts utilized their application knowledge of the physics generalizations in sorting the problems into categories. Novices, on the other hand, sorted the cards into categories corresponding to their physical characteristics. They placed all cards with pulleys into one pile and all cards with inclined plans in another, and so forth. The novices did not possess the application knowledge needed in recognizing the drawings as examples of specific generalizations. They had to fall back on their application knowledge of shapes. The novices would have been unable to solve the problems shown on the cards unless they were told which equations to use. They are like the chess novices who know the basic procedures for moving chess pieces properly but are unable to recognize examples of key chess positions. Whatever instruction the novice physics students received for the physics generalizations was not effective in developing an application knowledge of them. The novices likely possess only recall knowledge of the physics generalizations: meaning that they are able to recall definitions of the generalizations, but are unable to recognize novel examples of them, such as with the drawings of physics problems.

Students often face severe difficulties in learning application knowledge because many generalizations are abstract and complex [6]. Teachers frequently fail to appreciate that. Duckworth noted that: "Teachers are often, and understandably, impatient for their students to develop clear and adequate ideas, but putting ideas in relation to each other is not a simple job. It is confusing, and that confusion does take time. All of us need time for our confusion if we are to build the breath and depth that gives significance to our knowledge." [7]

Students often intuitively acquire application knowledge of faulty generalizations, and that hinders their learning new application knowledge [8]. Examining studies of students' learning of faulty generalizations provide us a clearer understanding of the implicit cognitive processes involved in application knowledge.

In a study at the high school level a researcher identified students in a physics class who were able to calculate correctly the speed and position of moving objects when given the appropriate equation. Those students were given an assessment task for application knowledge. They were asked to draw the path of a ball that was kicked off a cliff. They had not previously talked about such a situation. Most of them showed the ball going straight out from the cliff and then falling straight down. They drew this path in spite of the fact that it clearly was not a correct example of the physics generalizations about motion. According to the physics generalizations about motion the ball should have fallen off the cliff in a graceful parabola [9]. Those students clearly did not possess an application knowledge of the physics generalizations about motion; although they might have had a recall knowledge of it, meaning that they could retrieve a definition from their long-term memories.

Many young adults go through high school and university physics courses without ever giving up their application knowledge of faulty pre-Newtonian generalizations about motion [10]. For example, the teacher in this study has been instructing college students for an application knowledge of the generalization about Newton's law of inertia. It states, in part, if an object is in motion it continues in a straight line unless it is acted upon by physical forces. Following instruction, students were given an assessment task for application knowledge. The assessment task (a curved pellet shooter) is shown below. They have not seen this curved pellet-shooter example before. They were asked to draw the path of a pellet when it emerged from the curved tube though which it had been shot. Their two most common performances are shown. The correct performance is A [11].

Another study reported that after two months of instruction for an application knowledge of the physics generalization acceleration in an introductory physics course, only 40 percent of the students could correctly perform application knowledge assessment tasks for the generalization [12].

Research indicates that students of all ages, preschool through Ph.D., and in many subject areas, hold onto their application knowledge of faulty generalizations tenaciously even when the evidence that initially produced the generalizations is discredited. They hold on because they believe the faulty generalizations were initially derived from "real life" experiences [13]. For example, many students believe the faulty generalization that heavier objects displace more water than lighter objects. Based on the faulty generalization, they predict that the heavier of two metal cubes of equal size would displace more water in a container than would the lighter of the two metal cubes. They place the cubes in the container of water one at a time and measure the height of the water before and after each cube is placed in the container. They are surprised when both cubes raise the water to an equal level, but they are undaunted. A student explains, "Your tricked us. You brought magic water." Students were willing to violate their understanding about the nature of water in order to defend their faulty generalization about weight determining the displacement of water [14].

Another group of students had acquired an application knowledge of a faulty generalization about heavier objects falling faster than lighter objects. On the basis of that faulty generalization they predicted that if a heavier metal ball and a lighter wooden ball of the same size were dropped at the same time from the same height, the metal ball would hit the ground first. When they tested out their prediction by dropping the two balls at the same time, and watching the balls hit at the same time, they refused to accept the contradictory information. Some claimed they observed the metal ball hitting first. Others claimed the balls must be the same weight even though the scales showed different weights [15].

A teacher tried to change students' application knowledge of the faulty generalization that electricity wears out as it travels from a battery around a circuit containing a light bulb. Students predicted that if the teacher placed two ammeters to measure current on each side of the light bulb, they would show less current on one side. When the ammeters were in place and the current turned on, the ammeters showed the current on each side of the bulb was the same. But instead of changing their faulty generalization, students tended to reject the data on methodological grounds. They claimed the ammeters were not accurate, the light bulb was bad, and the battery did not work [16].

Students' difficulties solving science problems can frequently be traced to their an application knowledge of faulty generalizations. One study reported that 80 percent of the university-level engineering students it focused on were proficient in solving word problems only when they were given the algebraic equations that were appropriate for solving the problems. They were like the novice chess players who knew how to move chess pieces properly, but lacked application knowledge of key playing positions. The difficulties engineering students were having were traced, in part, to their lack of a very basic application knowledge of what "X" means in algebraic equations, such as "6X + 5 = 17 [17].

Many of the difficulties students have in carrying out multi-digit addition and subtraction with regrouping have been traced to their inadequate application knowledge of generalizations about place value [18]. In a large national study it was found that less than 50 percent of third graders possess application knowledge of place value in the hundreds digit, and only 64 percent in the tens digit. One-third of third graders gave incorrect answers on two-digit subtraction problems involving regrouping, and half did so for three-digit problems involving regrouping [19]. Students' severe deficiencies in their application knowledge of key mathematics generalizations, and the resulting inadequate performance in arithmetic computation, have been documented by extensive research [20]. The majority of students in elementary school acquire the ability to "run off" arithmetic procedures, but few acquire an application knowledge of the generalizations that are the basis of those procedures. They consequently have great difficulty finding errors in their work and in solving word problems [21].

A four-year longitudinal study of fifth and eight-graders' acquisition of recall knowledge and application knowledge of such social studies generalizations as freedom and representativeness found that some acquired recall knowledge of the generalizations, but no application knowledge of them [22]. This was evidenced by their ability to recall definitions of the terms, and their inability to recognize novel examples of the terms. Reviews of large-scale studies of students' attitudes and knowledge about government found that students typically exhibit some strong feelings about their government, but possess by little actual recall knowledge of it, and no application knowledge [23]. One study reported that 80 percent of fourth-graders chose the presidency as the most important role for a adult, but less than 25 percent of them could describe any of the president's duties [24].

Studies indicate that basing instruction on current textbooks results in students' inadequate acquisition of application knowledge because the texts have major instructional weaknesses. Examinations of social studies textbooks found that they do not provide the kind of information about generalization examples, both familiar and novel, that are needed in developing students' application knowledge of social studies generalizations [25]. In fact, the authors of the textbooks seem to assume that students reading the texts have already acquired application knowledge of the generalizations that the texts are presumably intended to teach.

A growing realization among teachers of the general ineffectiveness of most traditional textbook-based instruction is causing many of them to provide their students more "hands-on" laboratory activities. But these laboratory activities are unlikely, by themselves, to increase students' application knowledge of generalizations. They are often just confusing. What is needed is more emphasis on "minds-on" activities [26]. The activities should provide students both appropriate application-knowledge instructional tasks to perform actively, and appropriate application-knowledge guidance while they perform those tasks.

Asian children in elementary school consistently out perform children from the U.S. Studies over the last two decades of U.S. and Asian classrooms indicate a reason U.S. children perform poorly is that Asian children receive more instruction for application knowledge while they are learning mathematical procedures. For example, Japanese and Taiwanese first and fifth-graders score much higher on test items measuring application knowledge of place value and of problem solving in multi-digit addition and subtraction with regrouping than do U.S. first and fifth-graders [27]. Korean children aged 6, 7 and 8 carry out multi-digit addition and subtraction with regrouping much more accurately and have a greater understanding of what they are doing than do their U.S. counterparts [28]. Korean, Japanese and Chinese children possess a higher degree of application knowledge of generalizations for place value than do children in the United States [29]. School in Mainland China, Japan, the former Soviet Union and Taiwan all begin instruction for application knowledge of generalizations for place value and procedures in multi-digit addition and subtraction with regrouping earlier than do schools in the U.S., and they complete this instruction earlier [30]. Studies in Japanese, Chinese and U.S. classrooms found that Asian teachers provide instruction for application knowledge while U.S. teachers seldom do. The result is that Asian children understand why a certain mathematical procedure works, how to use it, and how to spot and correct errors [31].

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1. (For example). Brewer, W.F., & Chinn, C.A. (1991). Entrenched beliefs, inconsistent information, and knowledge change. In L. Birnbaum (Ed.). The International Conferences of the Learning Sciences: Proceedings of the 1991 Conference (pp. 67-73). Charlottesville, VA: Association for the Advancement of Computing in Education.

2. Schoenfeld, A.H. (1985). Mathematical problem solving. New York: Academic Press.

3. Anderson, J.R., & Lebiere, C. (1998). Atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates.

4. Ericsson, K.A., Charness, N., Feltovich, P., & Hoffman, R.R. (Eds.). (2006). The Cambridge handbook of expertise and expert performance. Cambridge, UK: Cambridge University Press.

5. Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics knowledge by experts and novices. Cognitive Science, 5, 121-152.

6. Chi, M.T.H. (2000) Cognitive understanding levels. In A.E. Kazkin (Ed.). Encyclopedia of psychology (pp. 146-151).

7. Duckworth, E. (1987). "The having of wonderful ideas" and other essays on teaching and learning. New York: Teachers College, Columbia University. p. 82.

8. Chi, M.J.H. (2005). Common sense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161-199.

9. Strieley, T. (1988), August/September). Physics for the rest of us. Educational Researcher, 17(6), 7-12.

10. (For example). Champagne, A.S., Klopfer, L.E., & Anderson, J.H. (1980). Factors influencing the learning of mechanics. American Journal of Physics, 48, 1074-1079.

11. Adapted from Green B.F., McCloskey, M., Caramazza, A. (1985). The relationship of knowledge to problem solving, with examples from kinematics. In S.F. Chipman, J.W. Segal, & R. Glasser. (Eds.). Thinking and learning skills (Vol. 2, pp. 127-140).

12. Reif, F. (1987). Instructional design, cognition, and technology: Application to the teaching of scientific concepts. Journal of Research in Science Teaching, 24, 309-324.

13. (For example). Carpenter, T.P., Moser, J., & Romberg, T. (Eds.). (1982). Addition and subtraction: A developmental perspective. Hillsdale, NJ: Erlbaum.

14. Linn, M.C. (1983). Content, context, and process in adolescent reasoning. Journal of Early Adolescence, 3, 63-82.

15. (For example). Eylon, B. & Linn, M.C. (1988). Learning and instructor: An examination of four research perspectives in science education. Review of Educational Research, 58, 251-301.

16. Johusa, S., & Dupin, J.J. (1987). Taking into account student conceptions in instructional strategy: An example in physics. Cognition and Instruction, 4, 117-135.

17. Clement, J., Lockhead, J., & Monk, G. (1979). Translation difficulties in learning mathematics. American Mathematics Monthly, 88 (4), 287-290.

18. Fuson, K.C. (1990). Conceptual structures for multi-digit addition, subtraction, and place value. Cognition and Instruction, 7, 343-403.

19. Kouba, V.L., Brown, C.A., Carpenter, T.P., Lindquist, M.M., Silver, E.A., & Swafford, J.O. (1988). Results of the fourth NAEP assessment of mathematics: Number, operations and word problems. Arithmetic Teacher, 35, (8), 14-19.

20. (For example). Resnick, L.B., & Omanson, S.F. (1990). Learning to understand arithmetic. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 3, pp. 41-95). Hillsdale, NJ: Erlbaum.

21. Brown, A.L., & Campione, J.C. (1990). Interactive learning environment and the teaching of science and mathematics. In M. Gardner, J.G. Greeno, F. Reif, A.H. Schoenfeld, & E. Stage (Eds.), Toward a scientific practice of science education (pp. 111-140). Hillsdale, NJ: Erlbaum.

22. Sinatra, G.M., Beck, I.L., & McKeown, M.G. (1992). A longitudinal study of the characteristics of young students' knowledge of their country's government. American Educational Research Journal, 29, 633-661.

23. (For example). McKeown, M.G., & Beck, I.L. (1990). The assessment and characteristics of young learners' knowledge of a topic in history. American Educational Research Journal, 27, 588-627.

24. Greenstein, F.J. (1965). Children and politics. New Haven: Yale University Press.

25. Beck, I.L., McKeown, M.G., & Gromoll, E.W. (1989). Learning from social studies texts. Cognition and Instruction, 6, 99-158.

26. (For example). Smith, E.L., & Anderson, C.W. (1984). The planning and teaching of intermediate science study: Final report. (Research Series No. 147). East Lansing: Michigan State University, Institute of Research on Teaching.

27. Stigler, J.W., Lee, S.Y., & Stevenson, H.W. (1990). The mathematical knowledge of Japanese, Chinese, and American elementary school children. Reston, VA: National Council of Teachers of Mathematics.

28. Song, M., & Ginsburg, H.P. (1987). The development of informal and formal mathematics thinking in Korean and U.S. children. Child Development, 57, 1286-1296.

29. Miura, I.T., Kim, C.C., Chang, C-M, & Okamato, U. (1988). Effects of language characteristics on children's cognitive representation of number: Cross-national comparisons. Child Development, 59, 1445-1450.

30. Fuson, K.C., Stigler, J.W., & Bartsch, K. (1988). Grade placement of addition and subtraction topics in China, Japan, the Soviet Union, Taiwan, and the United States. Journal for Research in Mathematics Education, 19, 449-458.

31. Perry, M. (2000). Explanation of mathematical concepts in Japanese, Chinese, and U.S. first and fifth-grade classrooms. Cognition and Instruction, 18, 181-207.

Application Knowledge I

Application knowledge refers to recognizing whether novel objects or actions are examples of a generalization [1]. Cognitive psychologists also refer to generalizations by such terms as; mental models [2], schema or schemata [3], scripts [4]. production systems [5], and cognitive representations [6]. Application knowledge involves two implicit and unobservable cognitive processes; (a) constructing and encoding generalizations in long-term memory for patterns of information perceived in the environment, and the transfer activity [7] of (b) matching previously encoded generalizations with new examples from the environment.

The two implicit cognitive processes in application knowledge are explicitly illustrated in the differing behaviors of two groups of chess players, ordinary players (novices) and grandmasters (experts) [8]. During chess exhibitions an expert grandmaster plays against 25 or 30 novice ordinary players at the same time and beats all of them. The expert walks up to a partially played chess board, and within seconds moves a chess piece, and then goes on to the next player. The novice who was just moved against takes a number of minutes to decide on the next move.

An early assumption was that experts were geniuses. But that was found not to be the case. A study of chess players ranging from amateurs to grandmasters found no connection between their playing strengths and their visual-spatial abilities, as measured by shape-memory tests [9]. Experts are simply able to use a rapid, generalization-guided perception to recognize the particular pattern of key chess position they see on the chess board. They then make the chess move they already know to be associated with that key position. It has been estimated that grandmasters possess encoded generalizations for roughly 50,000 to 100,000 key chess-board positions [10]. Stated another way, chess experts have encoded mental models of thousands of key chess positions. And they are able to quickly match each mental model they have encoded in long-term memory with an example of it on a partially played chess board. Each key position is associated with a critical next chess move, which they are able to make within seconds. Novices, by contrast, have typically encoded generalizations for only a few key chess positions.

Another early assumption was that experts excel in their memorization ability. That was also found not to be the case. In a memory test novices and experts were shown drawings of key chess positions on boards and given up to 30 seconds to memorize each of them, and then without the drawings to reconstruct the chess positions from memory. Novices were unable to recall more than a few details of the positions on the chess board. Experts were able to look at a drawing for only a few seconds and later recall all the chess positions perfectly. Experts remembered more positions because they recognized examples of key chess positions they had already encoded in their long-term memories. Novices possessed little or no knowledge of key chess positions from which to draw upon. On the other hand, when both groups were shown drawings of randomly placed chess positions, with none being key chess positions you would find in an actual game, there was no difference in their ability to remember. The experts remembered no better because they did not recognize any examples of the key chess positions they knew, so they were unable to utilize their vast knowledge base of chess positions.

Surprisingly, the preponderance of evidence indicates that experts in any field are made, not born. Motivation is more important than innate ability. Expertise is the result of highly motivated and persistent engagement in an effective instructional environment.

Similar differences in the application knowledge of novices and experts have been found in people of differing ages and in such diverse fields as playing bridge [11], playing baseball [12], playing basketball [13], electronics [14], computer programming [15], physics [16], general science [17], mathematics [18], reading [19], writing [20], political science [21], legal analysis [22], biology [23], and environmental science [24]. Even those fields that are commonly attributed to naturally endowed artistic skill, inspiration and genus, such as music and painting, have been found to require years of highly motivated and persistent study in order to acquire the application knowledge that is necessary for high levels of competent artistic performance [25].

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1. Anderson, R.C., & Faust, G.W. (1973). Educational psychology: The science of instruction and learning. New York: Harper & Row.

2. Johnson-Laird, P.N. (1983). Mental models. Cambridge, MA: Harvard University Press.

3. (For example). Rumelhardt, D.E., & Ortony, A. (1976) The representation of knowledge in memory. In R.C. Anderson, R.J Spiro, & W.E. Spiro, & W.E. Montague (Eds.), Schooling and the acquisition of knowledge. Hillsdale, NJ: Erlbaum.

4. (For example). Walker, C.H., & Yekovich, F.R. (1987). Activation and use of script-based antecedents in anaphoric reference. Journal of Memory and Language, 26, 673-691.

5. Anderson, J.R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

6. Kaput, J.J. (1985) Representation and problem solving: Methodological issues related to modeling In E.A. Silver (Ed.), Teaching and learning mathematical problem solving: Multiple research perspectives (pp. 381-398). Hillsdale, NJ: Erlbaum.

7. Cormier, S.M., & Hagman, J.D. (1987). (Eds.), Transfer of learning: Contemporary research and application. New York: Academic Press.

8. Simon, H.A., & Chase, W.G. (1973). Skill in chess. American Scientist, 61, 394-403.

9. Gobet, F., Voogt, A., & Retschizki, J. (2004). Moves in mind: The psychology of board games. Psychology Press.

10. Ross, P.E. (August, 2006). The expert mind. Scientific American, 295, 64-71.

11. Charness, N. (1979). Components of skill in bridge. Canadian Journal of Psychology, 33, 1-16.

12. Chiesi, H.L., Spillich, G.J., & Voss, J.F. (1979). Acquisition of domain-related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior, 18, 257-273.

13. Allard, F. & Burnett, N. (1985). Skill in sport. Canadian Journal of Psychology, 39, 294-312.

14. Egan, D.E., & Schwartz, B.J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149-158.

15. (For example). Adelson, B. (1985). Comparing natural and abstract categories: A case study from computer science. Cognitive Science, 9, 417-430.

16. (For example). McDermott, L. July, 1984). Research in conceptual understanding in mechanics. Physics Today, 24-32.

17 (For example). Goggo, C., & Chi, M.T.H. (1986). How knowledge is structured and used by expert and novice children. Cognitive Development, 1, 221-237.

18. (For example). Schoenfeld, A.H. (1986). On having and using geometric knowledge. In J. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 225-264). Hillsdale, NJ: Erlbaum.

19. Lesgold, A.M., & Resnick, L.B. (1982). How reading difficulties develop: Perspective from a longitudinal study. In J.P. Das, R. Mulcahy, & A. E. Walls (Eds.), Theory and research in learning and learning disability. New York: Plenum.

20. Bruce, B., Collins, A. Rubin, A.D., & Gentner, D. (1982). Three perspectives on writing. Educational Psychologist, 17, 131--145.

21. Voss, J.F., Greene, T.R., Post, T.A., & Penner, B.C. (1983). Problem solving skill in social science. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 17, pp.05-323). New York: Academic Press.

22. Lunderberg, M.A. (1987). Metacognitive aspects of reading comprehension: Studying understanding in legal case analysis. Reading Researach Quarterly, 22, 407-432.

23. (For example). Fisher, K.M. (1985). A misconception in biology: Amino acids and translation. Journal of Research in Science Teaching, 22, 53-62.

24. Tutor;, M.T. (1992). Expert and novice differences in strategies to problem solve an environmental issue. Contemporary Educational Psychology, 17, 329-339.

25. Hayes, J.R. (1985). Three problems in teaching general skills. In S.F. Chipman, J.W. Segal, & R. Glaser (Eds.), Thinking and learning skills (Vol. 1, pp. 391-406). Hillsdale, NJ: Erlbaum.

Organization Strategy MatrixThird

This posting focuses on Steps One and Two of an Organization Strategy when the Organization Task in Step One uses a Matrix. The effective use of a matrix has also been called networking [1], and concept mapping [2]. Helping students use a matrix as an organizer is particularly effective when verbal target information is presented in expository material that is unfamiliar and difficult, such as commonly found in texts in mathematics, science and social science [3]. There is some evidence that using a matrix results in more learning than just reading a text, reading with key ideas extracted, or reading a text with an outline [4]. The instructional effectiveness of using a matrix when learning verbal target information may, in part, be because it provides a visual image. In effect, it utilizes two instructional strategies, Elaboration and Organization. A matrix can be constructed for most verbal information that can be outlined.

A matrix usually contains two parts, concepts and links. Concepts are ideas and links are the relationship among and between those ideas. Here is a simple matrix that has concepts in boxes and lines for links.

SCIENCE UNIT ON PLANTS

This illustration focuses on a small portion of a science unit on plants. It is for an objective that has been illustrated in previous posts in order to clarify the differences between using different instructional strategies for Recall Knowledge. The teacher begins Step One of an Organization Strategy with an Organization Task. First, the teacher has students read the portion from the textbook that is shown below.

Second, the teacher tells students that they will make a matrix about what they read about leaves in order to help them remember it. The matrix will have concepts circled and links with labeled lines. The teacher begins by writing the word plants at the top of the screen and circling it, saying that it is about plants. The teacher asks what plants have, when a student says, "Leaves," the teacher writes and circles the word leaves below the word plants. The teacher then draws a line with an arrow between the words plant and leaves, and labels the line, have. The teacher says that the line an arrow on the screen shows that plants have leaves. The teacher continues this process until the matrix on the screen shows all the information they read in the selection about leaves, and looks something like the matrix below.

Third, the teacher helps students encode the completed matrix by having them make their own copy. The teacher provides Organization Guidance by keeping all students on task completing their copying. When the teacher notices an error in the matrix, students are copying, he has them look at the completed matrix on the screen. He also praises students when they are correct.

The teacher begins Step Two of an Organization Strategy by removing the matrix from the screen and giving students a Recall-Practice Task. Students put their maps away. They will write their answers to the questions the teacher will show on the screen. The teacher shows these questions.

1. What gives leaves their green color?

2. What is the lamina of a leaf?

3. What is the petiole of a leaf?

4. What does chlorophyll do?

5. What two things do veins do?

The teacher provides Recall-Practice Guidance by walking around the room and watching students as they write their answers. When students have difficulty recalling an answer, the teacher guides them in thinking back to the matrix they wrote earlier. If they still have trouble, the teacher can provide more explicit guidance by having them look back briefly at their matrix.

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1. Dansereau, D.F. (1985) Learning strategy research. In J.W. Segal, S.F. Chipman, & R. Glaser (Eds.), Thinking and learning skills (Vol. 1, pp. 209-240.

2. (For example). Novak, J.D., Gowin, D.B., & Johansen, G. T. (1983). The use of concept mapping and knowledge Vee mapping with junior high school science students. Science Education, 67, 625-645.

3. (For example). Armbruster, B.B., & Anderson, T.H. (1984). Mapping: Representing informative text diagrammatically. In C.D. Holley, & D.F. Dansereau (Eds.), Spatial learning strategies (pp. 189-209). New York: Academic Press.

4. Kauffman, D.F., & Kiewra, K.A. (2009). What makes a matrix so effective? An empirical test of the relative benefits of signaling, extraction and localization. Journal of Instructional Science. (Published online, April, 2009).

Organization Strategy: Outline

This posting focuses on Steps One and Two of an Organization Strategy when the Organization Task in Step One uses Outlines. An outline visualizes as a hierarchical structure of super- ordinate, ordinate and subordinate headings the relationships between and among separate items of verbal target information. Students who are given outlines to follow while reading a selection from a textbook remember significantly more of the target information than students not given outlines to follow when reading the same selection [1]. Using outlines enhances students' retrieval of verbal target information from long-term memory that they read or hear for two reasons [2]. First, outlines help students pick out the target information from the complex mass of verbal information they are reading or hearing. Second, the hierarchical structure of ordinate and subordinate headings help students encode the target information in connected networks in ways that facilitate later retrieval from long-term memory. Two kinds of outlines are effective in an Organization Strategy: Names and Attributes and Narrative Discourse Structure.

NAMES AND ATTRIBUTES

The teacher can help students organize verbal target information around outline headings that refer to names and attributes. Students' retrieval from long-term memory of verbal target information they hear in lectures or read in textbooks is enhanced whey they are helped to organize it around outline headings that identify names and attributes [3]. Here is a portion of an uncompleted names-and-attributes outline

Depending on the ability level of students, the teacher might use the entire uncompleted outline in an Organization Task in Step One of an Organization Strategy in one of two ways. First, if students are more able, the teacher might give students a copy of the entire uncompleted outline before they read an assigned section on vitamins in their textbooks or listen to a lecture on vitamins. Students write the information in the relevant blanks on the outline as they read or listen. Second, if students are less able the teacher probably should have students read the assignment or listen to the lecture before getting the outline. When the lecture is over or the reading completed, the teacher hands out the uncompleted outline and shows a copy of it on the screen. The teacher leads students in a discussion of the information that belongs in each blank, and shows the information on the screen. Students are given time to copy the information on their own outlines. The teacher provides Organization Guidance by focusing students' attention on the information in the outline, and corrects students' inaccurate responses and confirms their correct responses. In Step Two of an Organization Strategy the teacher provides a Recall-Practice Task by having students put away their completed outlines. The teacher asks them questions about the target information, such as, "What are the animal sources of Vitamin A?" Students might answer orally or in writing. Having them answer in writing will assure the teacher that every student is performing the task. When students have difficulty retrieving the information from memory, the teacher refers them back to the outline, and may eventually have to have them look back at their outlines.

NARRATIVE DISCOURSE STRUCTURE

Students in the early elementary grades begin intuitively using the essential structural elements in narrative discourse when they encode and later retrieve verbal information presented to them in stories, biographies and novels [4]. After reading a short fiction story, for example, readers are able to retrieve information that pertains to the structural elements of narrative discourse better than information that does not pertain to those elements. They remember verbal information that pertains to the narrative discourse elements of setting, theme, plot and resolution better than they do verbal information that does not pertain to those elements. Utilizing the naturally occurring phenomenon of narrative discourse as an organizer is an effective way of helping students encode and retrieve verbal target information they read or hear in fiction and nonfiction stories and in biographies.

Here are the essential structural elements in narrative discourse in an outline format that can be used in an Organization Task [5].

In Step One of an Organization Strategy, a history teacher whose class is presently studying Theodore Roosevelt's presidency, might begin an Organization Task by having students read the section in the textbook about Roosevelt's first State of the Union presentation to the U.S. Congress. Then the teacher projects onto a screen a discourse outline. The teacher helps students identify the relevant information in the outline. Students write their own copy of the outline they are developing. When focusing on the minor setting of Roosevelt's six-minute tirade about modern anarchists of the kind that abuse the First Amendment and incite anarchy, and who assassinated Roosevelt's predecessor, William McKinley, the teacher focuses their attention on this section: The wind is sowed by the men who preach such doctrines, and they cannot escape their responsibility for the whirlwind that is reaped....If ever anarchy is triumphant, its triumph will last for but one red moment, to be succeed for ages by the gloomy night of despotism..." [6]. The teacher helps students identify the five events in the Plot that pertain to Roosevelt's six-minute tirade. To help them understand events in Roosevelt's day the teacher asks students to compare how the First Amendment is being abused today by people who incite anarchy. When students have been helped to complete the outline, the teacher has them put it away and begins Step Two of an Organization Strategy by using a Recall-Practice Task that consists of questions about the target information for the topic of Roosevelt's presidency. The teacher might ask questions orally, which allows only a few students to participate, or the teacher might have students write their answers, which has every student participating. When students have difficulty the teacher guides them in recalling the outline they completed. The teacher might have to provide more explicit guidance by having them look back at their written outline.

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1. Glynn, S.M., & DiVesta, F.J. (1977). Outline and hierarchical organization as aids for study and retrieval. Journal of Educational Psychology, 69, 89-95.

2. Goetz, E.T., & Armbruster, B.B. (1980). Psychological correlates of text structure. In R.J. Spiro, B.C. Bruce, & W.F. Brewer (Eds.), Theoretical issues in reading comprehension (pp. 201-220). Hillsdale, NJ: Erlbaum.

3. (For example). Frase, L.T. (1969). Paragraph organization of written materials: The influence of conceptual clustering upon the level and organization of recall. Journal of Educational Psychology, 60, 394-401.

4. (For example), Whaley, J.F. (1981). Readers' expectations for a story structure. Reading Research Quarterly, 17, 90-114.

5. Adapted from Gordon C.J., & Braun, C. (1985). Metacognitive processes: Reading and writing narrative discourse. In D.L> Forrest-Pressley, G.E. MacKinnon, & T.G. Waller (Eds.), Metacognition, cognition, and human performance (Vol 2, pp. 1-76). New York: Academic Press.

6. Morris, D. (2001). Theodore rex. New York: Random House.