Elementary Students' Strategies for Writing-to-Learn in

Elementary Students' Strategies for Writing-to-Learn in ScienceAuthor(s): Perry D. KleinReviewed work(s):Source: Cognition and Instruction, Vol. 18, No. 3 (2000), pp. 317-348Published by: Taylor & Francis, Ltd.Stable URL: http://www.jstor.org/stable/3233922 .Accessed: 02/10/2012 16:44

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COGNITION AND INSTRUCTION, 18(3), 317-348 Copyright C 2000, Lawrence Erlbaum Associates, Inc.

Elementary Students' Strategies for Writing-to-Learn in Science

Perry D. Klein Faculty of Education

The University of Western Ontario London, Canada

Previous research has shown that writing can contribute to learning, but few studies have examined the cognitive processes through which this occurs, particularly among elementary students. A total of 70 children in Grades 4, 6, and 8 carried out science experiments concerning buoyancy or the balance beam, stated their explanations of the phenomena, and then wrote journal-style notes while thinking aloud. Fifteen of the students constructed more complex explanations during writing. Four aspects of the data were analyzed: writing operations, transitional sequences among writing operations, text features, and strategies for generating content. An analysis of these data yielded 7 factors: Text Production, Searching From Experiment, Brainstorming, El- aborative Genre, Goal Setting, Searching From Text, and Reviewing Beliefs. In a logistic regression analysis, Brainstorming, Searching From Text, and Searching From Experiment contributed significantly to the likelihood of explanatory gains; Text Pro- duction contributed marginally. It was concluded that for elementary students, writing-to-leam depends on strategies that are diverse, local in scope, independent of one another, and moderate in sophistication. Instructional implications are discussed.

During the past 2 decades, many language educators and researchers have argued that writing allows students to understand difficult content, to think critically, and even to construct new knowledge (e.g., Emig, 1977; Newell, 1998; Spivey, 1990; Young & Sullivan, 1984). At the same time, educators in content areas such as science, mathematics, and history have argued that writing can contribute to students' learning in these disciplines (e.g., Audet, Hickman, & Dobrynina, 1996; Rivard, 1994; Wadlington, Bitner, Partridge, & Austin, 1992). For several decades, the claim that writing contributes to learning remained untested empirically (for re- views, see Ackerman, 1993; Applebee, 1984). More recently, several studies have

Requests for reprints should be sent to Perry D. Klein, Faculty of Education, University of Western Ontario, London, Ontario, Canada N6G 1G7. E-mail: [email protected]

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shown that writing generally produces positive effects on students' recall and comprehension of text and lecture material (Beins, 1993; Foos, 1995; Hinkle & Hinkle, 1990; Horton, Fronk, & Walton, 1985; McCrindle & Christensen, 1995; Wiley & Voss, 1996).

However, only a few researchers have addressed the question of how writing contributes to learning (Galbraith, 1992; Hayes, 1987; Langer & Applebee, 1987; Marshall, 1987; Newell, 1984; Penrose, 1992; for a review, see Schumacher & Nash, 1991). A model of cognitive processes in writing-to-learn would be valuable for several reasons. Theoretically, there has been a long history of controversy in the humanities and social sciences concerning the effects of literacy on cognition (e.g., Goody & Watt, 1968; Harris, 1989; D. R. Olson, 1996; Ong, 1982). Em-

pirically, the results of writing-to-learn studies have been somewhat confusing: Some investigations have shown uniformly positive results, whereas others have shown a mixture of positive and null findings (e.g., Boyles, Killian, & Rileigh, 1994; Langer & Applebee, 1987; Newell, 1984; Newell & Winograd, 1989; Penrose, 1992). A theory of writing-to-learn may help to explain these disparate outcomes. Most important, a cognitive model of writing-to-learn could assist educators in designing writing assignments, planning strategy instruction, and guiding students during composition.

Writing-to-learn in elementary classrooms particularly requires investigation. On one hand, several authors have recommended this activity as a tool for learning even in the primary grades (e.g., Atwell, 1990; Bridges, 1997; Rosaen, 1990), and studies of the effects of writing on elementary students' learning have produced promising results, although they have been few in number (Copeland, 1987; Da- vis, Rooze, & Runnels, 1992; Konopak, Martin, & Martin, 1990). On the other hand, some theorists have posited that writing-to-learn depends on sophisticated strategies (e.g., Bereiter & Scardamalia, 1987; Flower & Hayes, 1980, 1981), and most children's writing is relatively nonstrategic (Bereiter & Scardamalia, 1987; Beringer et al., 1992; V. L. B. Olson, 1990; van Gelderen, 1997). This dilemma

suggests several possible pedagogical options: Should elementary teachers assign writing-to-learn activities and expect that children will benefit by applying novice

strategies? Should they try to teach elementary children more sophisticated writing strategies? Or should they reserve writing-to-learn for secondary and tertiary education? A choice among these options will depend, in part, on a theory of how children write-to-learn in the elementary grades.

Authors vary widely in their hypotheses concerning the level of expertise re-

quired for learning through writing; I briefly discuss four such hypotheses (cf. Klein, 1999b). At one end of the spectrum, Britton's (1980/1982b) hypothesis that writing shapes learning "at the point of utterance" assumes little strategic expertise. Britton believed that the knowledge generated by experience is often tacit. Speaking or writing imposes the structures of syntax and semantics on this experience, rendering it explicit. To describe this process, he used the metaphor of"har-

LEARNING THROUGH WRITING IN SCIENCE 319

vesting" connections from language (Britton, 1979/1982a, p. 126). This can occur as a student begins to write a sentence, without yet having formulated the ending, generating the ending in the course of producing the sentence, without conscious deliberation. Consequently, as a vehicle for learning, Britton (1980/1982b) recommended informal, speech-like, "expressive" writing, rather than repeated drafting and revising (pp. 139, 144). To date, evidence for the "shaping at the point ofutter- ance" hypothesis has been anecdotal (e.g., Britton, 1980/1982b; Murray, 1978). During the early elementary grades, mechanical demands such as handwriting and spelling substantially constrain the quality of students' writing; around the fourth grade, many students begin to write fluently enough that mechanics takes a back seat to other factors (Bereiter & Scardamalia, 1987; Beringer et al., 1992; Bourdin & Fayol, 1994; Juel, Griffith, & Gough, 1986). Therefore, if Britton's proposal is valid, this fluency may enable learning through writing during the latter half of elementary school.

A second hypothesis proposes that writing preserves students' ideas, so that they can review and build on them. This building may include evaluating ideas critically, reorganizing or constructing new relations among them, and deriving new inferences (Donald, 1991; Emig, 1977; Goody & Watt, 1968; Young & Sullivan, 1984). These may be expressed as additions or changes to students' texts. This is called the forward search hypothesis because it suggests that writers' initial text drafts stimulate and constrain their production of subsequent ideas. To date, arguments for the forward search hypothesis have been based on anecdotes (e.g., Britton, Burgess, Martin, McLeod, & Rosen, 1975, p. 25), and on analogies from the role that concrete symbolic representations play in other problem-solving activities, such as paper-and-pencil arithmetic (e.g., Young & Sullivan, 1984). Indi- rect support for the forward search model can also be gleaned from research showing that reviewing contributes to the coherence of text (Benton, Kiewra, Whitfill, & Dennison, 1993; Breetvelt, van den Bergh, & Rijlaarsdam, 1994; Penningroth & Rosenberg, 1995).

The forward search hypothesis implies a greater degree of writing expertise than does Britton's shaping at the point of utterance hypothesis. Elementary students often do not review their texts; when they do, most have difficulty detecting, diagnosing, and correcting problems. The revisions that they make usually address local, surface-level problems (Bereiter & Scardamalia, 1987; Beringer & Swanson, 1994; V. L. B. Olson, 1990; van Gelderen, 1997). There is no single grade level at which revision strategies mature once and for all: On one hand, children in the middle elementary grades can be taught strategies for making meaning-level revisions (Cameron & Moshenko, 1996; Englert & Raphael, 1989; Harris & Graham, 1996); on the other hand, in a large-scale study, Beringer, Whitaker, Feng, Swanson, and Abbott (1996) found that it was not until junior high school that revision began to significantly improve the quality of students' text, and the ability to make global, gist-level revisions appears to develop during

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college and beyond (Hayes, Flower, Schriver, Stratman, & Carey, 1989; Wallace & Hayes, 1991). Therefore, if the forward search hypothesis is validated, then de- pending on the scope of revision it requires, writing-to-learn could emerge late in elementary education or considerably later.

Galbraith's (1992, 1996) theory of discovery can be viewed as a refinement of the forward search model. It includes both a spontaneous writing phase and a revision phase. Galbraith theorized that writers' episodic knowledge inheres in parallel distributed networks. This knowledge is often implicit, and comprises the writer's disposition toward a topic. In contrast, semantic memory comprises conceptual relations that interpret these experiences. As the writer expresses this disposition in sentences, the conceptual combinations available in semantic memory constrain this process, so that each sentence can only partially express the disposition. This can result in a text that includes disparate ideas. The writer then com-

poses successive drafts in which he or she evaluates, revises, and integrates information to coherently express the disposition. Consistent with this hypothesis, Galbraith predicted that low self-monitors, who express their affective states di-

rectly, would use writing to make their implicit knowledge of a topic explicit; high self-monitors, who control their expressive behavior to present themselves desir- ably to others, would impede this process by pursuing rhetorical goals early in the writing process. His findings were complex, but confirmed that for the low self-monitors, discovery consisted of unplanned generation of local ideas during writing; for high self-monitors, discovery consisted of applying existing ideas to the task, resulting in little knowledge change.

Bereiter and Scardamalia's (1987) knowledge transforming model, a third hypothesis concerning writing-to-learn, is currently the most influential in educational psychology (Bereiter, Burtis, & Scardamalia, 1988; Scardamalia & Bereiter, 1985, 1989; Scardamalia, Bereiter, & Steinbach, 1984). It presents expert writing as a dialectical interplay between two problem spaces. In the rhetorical space, the writer defines goals concerning audience and purpose. To support these rhetorical goals, the writer sets content subgoals. The writer then engages in problem solving in the content space to achieve these subgoals. For example, the writer may set the rhetorical goal of explaining a difficult term to his or her readers. To at- tain this goal, the writer may set the content subgoal of clarifying the meaning of the relevant concept. Content operations such as recalling examples of the concept, comparing it to other related concepts, or identifying a superordinate category to which it belongs could contribute to attaining this subgoal. As the writer clarifies the term for the readers, he or she also clarifies the concept for himself or herself.

To date, the knowledge transforming model has been indirectly supported by research showing that expert writers define rhetorical goals, generate plans to address these goals, and revise the gist of their texts to achieve them (Bereiter & Scardamalia, 1987; Flower & Hayes, 1980; Hayes et al., 1989). Moreover, writers


who use knowledge transforming strategies produce texts that are rated significantly higher than those who do not on holistic measures such as quality, coherence, and reflectivity (e.g., Bereiter et al., 1988; Scardamalia & Bereiter, 1985, 1989; Scardamalia et al., 1984). However, no research has yet linked the rhetorical goal-content goal dialectic to learning.

Bereiter and Scardamalia's (1987) hypothesis implied that writing-to-learn requires sophisticated strategies such as elaborating rhetorical goals and revising content plans to change the gist of a text. If this hypothesis is validated, then writing-to-learn may require strategies beyond those of most elementary school students. For example, Bereiter and Scardamalia argued that knowledge transformation through persuasive writing minimally requires the author to consider a thesis, a contrary thesis, and a possible solution to the contradiction between the two, a level of writing that they found in the essays of only 6 of 30 Grade 5 students.

In addition to these three hypotheses concerning the process of writing, researchers have also proposed that the structure of students' texts affects their learning. Some genres, such as argumentation, comparison and contrast, metaphor, and analogy, are thought to require deep processing, including the construction of relations between prior knowledge and new knowledge, and among elements of new knowledge (Hayes, 1987; Langer & Applebee, 1987; Newell, 1984; Newell & Winograd, 1989; Schumacher & Nash, 1991; Wiley & Voss, 1996). The genre hypothesis has been tested more extensively than the three process hypotheses previously described, with generally positive results. In several reading-to-write studies, composing in elaborative genres has significantly improved students' recall and comprehension of source texts relative to composing in less elaborative genres; however, results have been somewhat mixed, even within studies (e.g., Hayes, 1987; Klein, 1999a; Langer & Applebee, 1987; Newell, 1984; Penrose, 1992). The implications of the genre hypothesis for writing-to-lear in elementary school are somewhat unclear. On one hand, students begin to incorporate genre elements such as comparison, explanation, and evidence into their reading and writing by midelementary school; on the other hand, genre knowledge continues to develop throughout secondary and postsecondary education (Bereiter & Scardamalia, 1987; Chambliss, 1995; Englert, Stewart, & Hiebert, 1988; Langer, 1986; Wright & Rosenberg, 1993). To date, no research has yet identified the level of genre knowledge that is required to support learning through writing.

This study examined the way in which an episode of informal writing contributed to learning in elementary school science. Informal, journal-style composing was selected partially for external validity. The writing across the curriculum movement has emphasized journal writing, and it has become a common practice in elementary school content-area instruction (e.g., Atwell, 1990; Guthrie & Cox, 1998; Rosaen, 1990). It was also expected that because journal writing is relatively open ended, it would be accessible to students at a wide range of levels of writing

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achievement, and allow them to use a variety of writing strategies and text features, the effectiveness of which could then be compared.

Science experimentation was selected as a context for writing, in part, again, for external validity. Science educators have expressed considerable interest in writing-to-lear (e.g., Rivard, 1994; Rowell, 1997), and journal writing has become a widespread activity in elementary science education (e.g., Atwell, 1990; Guthrie & Cox, 1998; Peturson, 1990). However, to date, empirical studies of writing-to-lear have most often focused on social studies content (Ackerman, 1993). More important, writing has often been advocated as a tool for discovery (or redis- covery) of knowledge. However, most studies of writing-to-learn have employed reading-to-write tasks (see Ackerman, 1993, for a review). Although reading-to-write permits students to construct their own knowledge, many instead rely heavily on the structure and content of source texts (e.g., Flower et al., 1990; Nash, Schumacher, & Carlson, 1993; Penrose, 1992). It was expected that science experimentation would afford students more scope to construct their own texts and knowledge by providing them with relatively "raw" data.

Given the limited previous research concerning cognitive processes in writing-to-learn, and the specificity inherent in studying any one genre of writing and one subject area, my intention was not to test the four hypotheses just described. However, it was expected that these hypotheses would aid in interpreting the results of this investigation; and conversely the results of this investigation could in- form the future development of hypotheses about elementary writing-to-learn. Children in Grades 4, 6, and 8 each carried out a science experiment concerning either buoyancy or the forces acting on a balance beam. They stated their explanations of the phenomena, then wrote journal-style notes while thinking aloud. Students whose explanations improved during the writing interval were identified. Their verbal protocols and texts were compared to those of students who did not make such explanatory gains to identify writing operations, transitions among writing operations, text features, and content-generating strategies associated with learning during the writing interval. Factor analysis was applied to simplify the data, and the resulting factor scores were entered into a logistic regression analysis to test their effects on learning during the writing interval.

METHOD

Each student participated in one of two science tasks. The tasks were selected to ex- emplify important concepts in elementary school science. The problems in both tasks can, in principle, be solved using data that students can collect quickly, yet each requires thoughtful analysis. Each task also allows solutions that vary widely in complexity, making them appropriate for students of disparate ages, abilities, and levels of prior knowledge.


TABLE 1 Students' Preexperiment, Postexperiment, and Postwriting Explanations of Buoyancy by Grade Level

Preexperiment Postexperiment Postwriting

Grade Grade Grade Grade Grade Grade Grade Grade Grade Explanation 4 6 8 4 6 8 4 6 8

None 0 1 0 0 0 0 0 0 0 Weight 7 7 6 4 4 1 3 2 1 Type of material 2 1 2 2 2 3 2 0 2 Characteristic 3 3 2 4 3 1 4 3 1

"weight" of material Weight per volume 0 1 1 2 4 6 2 6 5 Density versus water 0 0 0 0 0 0 0 1 2 Other 0 0 0 0 0 0 1 1 0

In one task, students learned about buoyancy, that is, why objects sink or float. In a cross-sectional study concerning children's understanding of buoyancy, Inhelder and Piaget (1958) found that children's explanations could be grouped according to the following categories, which increased in complexity with age and education: (a) contradictory or fragmentary explanations; (b) weight; (c) type of material; (d) characteristic "weight" of a type of material; (e) density, that is, weight per volume; and (f) density of a given object relative to the density of the liquid in which it is immersed (see Table 1). Subsequent developmental and instructional studies have shown that although students need not progress through all of these explanations, this sequence correlates with age and educational level (Halford, Brown, & Thompson, 1986; Kohn, 1993; Smith, Carey, & Wiser, 1985).

In the second science task, students learned about the forces acting on a balance beam. The balance beam has several pegs on each arm, and disc-shaped weights can be placed on these pegs. The students' task was to predict whether the beam would balance, tip to the right, ortip to the left, and to explain these predictions (Inhelder & Piaget, 1958). Siegler (1985) found that most students' predictions followed pat- terns consistent with one of four rules; each ofthese includes the conditions stated in the previous rule, so the distinctive part of each is paraphrased here:

1. If the weight is the same on both sides of the fulcrum, then predict that the scale will balance; if side X has more weight, predict that it will tilt down.

2. If the weight is the same and side X has more distance, then predict that X will tilt down.

3. If side X has more weight and side X has less distance, then make an edu- cated guess.

4. If side X has more weight and side X has less distance, then compute torques: tl= wl x d; t2 = w2 x d2 (cf. Case, 1992; see Table 2).

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TABLE 2 Students' Preexperiment, Postexperiment, and Postwriting Explanations of

Balance by Grade Level

Preexperiment Postexperiment Postwriting

Grade Grade Grade Grade Grade Grade Grade Grade Grade Explanation 4 6 8 4 6 8 4 6 8

1. Weight 0 2 0 0 0 0 0 0 0 2. Weight and distance 7 4 3 2 2 1 1 1 0

if weights are equal 3. Distance and weight 6 4 8 11 8 7 11 9 9

compensate (unquantified)

4a. Partial torque rule 0 0 0 0 0 3 1 0 1 4b. Torque rule 0 0 0 0 0 0 0 0 1

Hardiman, Pollatsek, and Well (1986) noted that some participants also constructed quantitative rules less general than the multiplication rule, for example, stating that if the weight is twice as great on one side of the fulcrum, but the distance is twice as great on the other side, then the beam will balance.

The procedures for the two science tasks differ sufficiently to warrant separate descriptions.

The Buoyancy Task

Participants. The task was conducted in a school serving a middle-class

neighborhood in a middle-sized Canadian city. The researcher randomly selected children from two class lists in each of Grades 4,6, and 8. Thirty-six children whose

parents consented by letter participated, including a total of 19 boys and 17 girls.

Materials. Materials included a 6-liter bucket of water and a sorting mat with three sections labeled float, sink, and other. The 10 objects to be tested were a small wooden block (10 cc, 6 g); a large stone (70 cc, 187 g); a small stone (11 cc, 27 g); a

large wooden block (845 cc, 435 g); a sealed plastic vial filled with salt (180 cc, 216

g); a sealed plastic vial filled with wheat germ (180 cc, 57 g); a sealed plastic vial filled with water (180 cc, 178 g); a medium-sized wooden block (88 cc, labeled 50

g); a smaller, denser wooden block of equal weight (42 cc, labeled 50 g); and an alu- minum can with a hole in the bottom (200 cc, 32 g empty, 230 g filled with water). Students also received a piece of lined paper with the questions, "What makes ob-

jects float or sink? How did you learn this?" printed at the top, and additional pages of lined paper.


Procedure. Students were interviewed individually, and the interviews were

audiotaped. First, students participated in a warm-up activity to familiarize them with thinking aloud while problem solving (cf. Bereiter & Scardamalia, 1987). The interviewer provided five sentences printed on strips of paper and asked participants to sequence them to make a story. The interviewer said, "As you sort the sentence strips, please talk aloud about what you are doing. Just say whatever comes to mind, even if it seems unimportant. From time to time, I might remind you to tell me about what you are thinking." The interviewer encouraged students to maintain a continuous stream of speech, by prompting them to "tell me about what you are

thinking now" if they fell silent. Next, the interviewer asked students, "In your own words, what makes objects

float or sink?" If the student used terms such as, "light," "dense," "surface area," and so on, the interviewer asked further open-ended questions for clarification.

The interviewer then asked students to test one object at a time by placing them in the bucket of water, in the order listed earlier, and asked, "What do you think this will do? Why will it float or sink?" Then, the students tested the buoyancy of each of the objects and placed it on the appropriate section of the sorting mat. After the task, the interviewer asked, "Now that you have done the experiment, what do you think makes objects float or sink?"

Then, the interviewer said the following:

Now, I am going to ask you to write about your experiment. Please include answers to the questions [pointing to the page], "What makes objects float or sink? How did you learn this?" If you want to change something that you write, just cross it out and keep going, or write above it. Do not worry about whether or not your writing is neat, or how the words are spelled. As you write about the experiment please talk aloud about what you are doing, just like before, when you were making the story. Just say whatever comes to mind, even if it seems unimportant. From time to time, I might remind you to tell me about what you are thinking.

After the students finished writing, the interviewer asked, "Now that you have finished writing about the experiment, what do you think makes objects float or sink?"

Scoring of buoyancy explanations. The students' written records were re- tained, and the audiotape of students' explanations of buoyancy prior to experimenting, immediately after experimenting, and after writing, were transcribed. Two raters independently read these explanations and categorized each according to six ordinal levels and an additional residual category, other (see Table 1). The validity of these ordinal levels was supported by previous research concerning con- ceptions of buoyancy (Halford et al., 1986; Inhelder & Piaget, 1958; Kohn, 1993; Smith et al., 1985). Students who stated one level of explanation immediately after the experiment (before writing) and a higher level of explanation after writing were

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considered to show explanatory gains during writing. Exact agreement concerning level of explanation between two independent raters was 80%. Remaining phases of the analysis are discussed later.

The Balance Beam Task

The procedure for this task paralleled the procedure for the buoyancy task.

Participants. The participants were 34 elementary students from Grade 4, 6, and 8 classrooms in a rural school that served a population that varied widely in so- cioeconomic status. Students were randomly selected from two classrooms at each grade level; those whose parents consented by letter participated in the study. The sample included 19 girls and 15 boys.

Materials. Materials included a pivot arm balance beam, with four pegs on each arm, and nine disk-shaped weights that could be placed on these pegs. Mate- rials also included a worksheet with 15 diagrams illustrating arrangements of these

weights on the beam. These diagrams were arranged in order of approximate diffi-

culty, based on the four rules described earlier, ranging from trials for which the outcome could be accurately predicted using weight alone, through to trials that re-

quired a comparison of the products of weight and distance on each arm of the balance. For cases in which the beam balanced, the worksheet also included a table in which students recorded the number of weights and their distances from the fulcrum.

Procedure. Students completed the science and writing tasks individually; procedures were analogous to those for the buoyancy task. First students completed the think-aloud training task. Then they verbally answered the question, "What makes the beam tilt in one direction or the other, or balance?"

Next, the students received the worksheet representing 15 experimental trials. For each trial, the interviewer asked the student, "Do you think this will tilt in one direction or the other or balance?" After answering, the students ar-

ranged the weights on the balance beam to match the diagrams, and recorded the results on the worksheet. Then, the students completed a table that recorded the weights and their distances from the fulcrum for the trials in which the beam balanced. Following this, students were asked, "Now that you have done the

experiment, what do you think makes the beam tilt in one direction or the other, or balance?"


After students completed the writing task, the researcher asked, "Now that you have finished writing about the experiment, what do you think makes the beam tilt in one direction or the other, or balance?"

Scoring of balance explanations. Scoring procedures were similar to those for the buoyancy task. Students' explanations of the balance beam prior to experimenting, immediately after experimenting (before writing), and after writing, were transcribed. Two independent raters classified each explanation according to five ordinal levels based primarily on the rules described by Siegler (1985; see Ta- ble 2). In addition to Siegler's four rules, I followed Hardiman et al. (1986) in not- ing that some participants gave quantitative rules less general than the full torque rule; for example, stating that if the weight is twice as great on one side of the fulcrum, but the distance is twice as great on the other side, then the beam will balance. Valid multiplication rules simpler than the full torque rule were coded as Level 4-A and the full torque rule was coded as Level 4-B (see Table 2). Students who stated one level of explanation after the experiment (before writing) and a higher level of explanation after writing were considered to show explanatory gains during writing. Exact agreement between independent raters concerning level of explanation was 87%.

Analysis

As described earlier, the dependent variable of interest for both science tasks was the presence or absence of explanatory gains during the writing interval. As the following results show, the number of independent variables was large and many correlated with one another and with explanatory gains. Therefore, to examine the relations among these variables and their effects on the dependent variable of explanatory gains, it was necessary to maximize statistical power. The two science tasks were treated as different levels of a dichotomous between-subjects variable. The writing variables, to be described later, were simplified by reducing their number from 24 to 7 through factor analysis. Logistic regression analysis was then applied to determine the relation of these seven writing factors and their possible interactions with grade level and science task, to the dependent variable of explanatory gains. The details of the analysis follow.

Writing operations. For both science tasks, the verbal protocols for the writing interval were transcribed and segmented into meaning units, defined as a principal clause, together with any other clauses subordinate to it. Each meaning unit was classified with respect to operation using categories adapted from Flower and

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TABLE 3 Definitions and Examples of Writing Operations

Writing Operation

Goal setting and organizing

Generating

Generating and transcribing (simultaneously)

Transcribing Reviewing text Reviewing prior

knowledge Reviewing experiment Evaluating and

revising Questioning and

commenting

Definition and Example

Stating intention to address text structure, content, or operation (e.g., "I'll talk about the ones that sank now.")

Producing content, including language or ideas (e.g., "There is air in it, and the air is lighter than the water, so that brings it up.")

Transcribing words as they are uttered

Writing words that were generated during a previous meaning unit Rereading text that was written earlier in the session Reviewing an experience that occurred or belief that was held prior to

the session Reviewing the experimental trials Assessing or changing text (e.g., "I left out block" [uttered as the

student writes "BLOCK" with a caret indicating its place in the text]) Remarking on the task or other topics, or asking questions (e.g., "OK,

I'm kind of lost here." "Bill Nye the science guy does experiments on TV." "Can I cross it out if its wrong?")

Hayes (1981; cf. Breetvelt et al., 1994). Operations included goal setting, organizing, generating, transcribing, generating and transcribing simultaneously, review-

ing, evaluating, revising, and questioning or commenting. Additionally, reviewing was subdivided into three foci: text, experiment, or prior knowledge. Two raters in-

dependently analyzed all protocols. To increase reliability, goal setting and organizing were collapsed to form one category, and evaluating and revising were col-

lapsed to form one category, creating nine categories in all (see Table 3). Overall reliability was 81% exact agreement, with agreement for specific categories ranging from 76% for questioning or commenting to 90% for generating and transcrib-

ing simultaneously.

Transitional probability analysis. In addition to the simple frequency of each writing operation, the sequences into which writers organized these operations and the possible effects of these sequences on learning were examined. The relation between successive operations can be expressed using transitionalproba- bility, the likelihood that any given operation will be followed by a specific target operation. For example, for 100 instances of the given operation of generating, if the target operation of transcribing follows in 25 instances, then the transitional probability of generating to transcribing is 25% or .25.

Transitional probabilities validly describe the sequence of students' writing operations. However, for inferential purposes, additional analysis was necessary be-


cause raw transitional probabilities are affected by the simple probability of the target operation. For example, because the simple probability ofgenerating is higher than that of revising, the frequency of the transition from reviewing experiment to generating will probably be higher, by chance, than the probability of the transition from reviewing experiment to revising. Therefore, az score was generated for each operation for each participant, taking into account both the frequency ofa given transition and the simple probability ofthe target operation. If the probability ofa transition is consistent with the simple probability of the target operation, then this z score equals zero; if the probability of the transition sufficiently exceeds that implied by the simple probability of the target operation, then this z score differs significantly from zero. For a discussion of significance testing in transitional probability analysis, see Bakeman and Gottman (1986).

Analysis of text features. The structures of students' texts were examined using a method adapted from Langer (1986; cf. Newkirk, 1987). First, each clause within a given text was numbered. Then the relations among clauses were organized to form a tree diagram: A numbered node representing the first clause in the passage was recorded. If the second clause comprised a rhetorical predicate related to the first clause, for example by providing evidence for its claim, a descending line was drawn from the first clause's node to the second clause's node, and this link was labeled with the type of rhetorical predicate. Rhetorical predicates included description, comparison, explanation, evidence, and summary (see Table 4). If the second clause was not related to the first, then the former was represented by a second node at the upper level of the tree diagram, parallel to the first clause's node. This procedure was repeated for all subsequent clauses within a given text. Conse- quently, if several clauses each elaborated on the same earlier clause, this generated a descending "bush." The number of clauses with two or more such descending

TABLE 4 Definitions and Examples of Rhetorical Predicates

Rhetorical Predicate Definition and Example

Description Report of an event or its attributes (e.g., "The container with the hole in the center sunk.")

Comparison Juxtaposition of alternatives with respect to similarity or difference (e.g., "The smaller 50 g box sank, but the larger 50 g box floated.")

Explanation Causal antecedent and consequent (e.g., " I think that this [sinking] happened because it was very heavy.")

Evidence Reason to believe claim (e.g., " I learned this by doing an experiment.") Summary Restatement of content from several earlier text statements, may be in

more abstract or condensed form (e.g., "In conclusion I found that smaller and heavier things sink.")

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TABLE 5 Definitions and Examples of Content Generating Strategies

Content Generating Search Definition Example

No-search No statement of goals or subgoals; no "OBJECTS SINK IF THEY ARE HEAVY explicit logical operations prior to AND FLOAT IF THEY ARE LIGHT" transcribing [generated and transcribed simultaneously,

student stated same explanation previously] Search from Explicit use of experimental trials to "Just looking at the experiment to try to get

experiment generate content; content may be an idea of what to say." literal report or inference

Search from text Explicit rereading of text previously "When I run out of ideas, I usually read it written, for purpose of generating over again" [then reread text]. content; new content may be deduction, critique, etc.

Search from genre Explicit use of genre elements to "I'm using a science summary ... question ... organize generation of content procedure ... results ... conclusion."

Backward search Explicitly setting subgoal, subordinate "I'm looking for a pattern to write about." to overall goal of the text

clauses was the measure of how deep-linked each text was. If a series of clauses each related to the immediately preceding one, this generated a long, descending "vine." The depth of the text was the number of clauses in the longest such vine. All texts were analyzed by two independent raters. Exact agreement was 78% overall; reliability for specific rhetorical predicates ranged from 67% for description (the next lowest was 75% for explanation) to 88% for summary.

Analysis of content-generating strategies. In the fourth step of the analysis, the raters read each protocol to identify the means by which students generated content. These were classified based on the participants' explicit goal-setting statements: Search From Experiment, Search From Text, Genre Search, Backward Search, and No-Search (see Table 5 for descriptions and examples of each of these strategies).

First, content generating strategies were coded at the local level. Local refers to the explicit use of the strategy to generate content for at least one clause in the text. Because most participants used each type of strategy only once, or not at all, each of the five strategies were coded as present or absent, so up to five strategies could be coded as present for each student. Interrater agreement concerning local content

generating strategies was 9 1% overall and ranged from to 83% for Search From Text to 97% for No-Search. Second, content generating strategies were coded at the

global level. Global refers to the strategy that the participant used to organize the writing ofthe text as a whole, or if there was no explicit overall strategy, the strategy


that the student explicitly used most frequently. For example, a student who com-

posed the entire note by reviewing each trial in the order that it had been carried out, and writing a sentence about it (a common strategy) was classified as using search from experiment; a student who explicitly planned to use conventional lab report subheadings (purpose, method, observations, conclusions), then generated content for each subheading was coded as using a genre strategy. Each protocol could be assigned only one code for global strategy. Exact interrater agreement for global content generating strategies was 84% overall, and agreement for specific categories ranged from 75% for backward search to 100% for genre search.

Integration of independent variables. The preceding analyses generated a

large number of independent variables, several of which correlated with one another, with grade level, or with explanatory gains. Therefore, the independent variables were entered into a factor analysis. This rendered the data more interpretable by simplifying them and by identifying relations among writing operations, text features, and content generating strategies. The resulting writing factors were then entered into a logistic regression analysis. This was used to test the effects of writing factors, grade level, science tasks, and their interactions on explanatory gains during the writing interval.

RESULTS

Explanations

Buoyancy. Prior to experimenting, most students at all three grade levels explained buoyancy on the basis of weight alone (see Table 1). Immediately after experimenting, Grade 4 and Grade 6 students offered a range of explanations, and most Grade 8 students referred to weight relative to volume, a significant improvement, Wilcoxon signed ranks, z = -3.50,p < .01. Later, following writing, most students in Grade 6 and Grade 8 explained buoyancy by referring to weight relative to volume. Ten students made explanatory gains during the writing interval, including 8 who generated more complex explanations and 2 who rejected their earlier, mistaken explanations, z = -2.56,p < .01. Grade level correlated with postwriting level of explanation, Kendall's tau-b nonparametric correlation coefficient (34) = .35,p < .05, but did not correlate with gains during the writing interval tau-b (36) = .10, ns.

Balance beam. Prior to experimenting, about half of Grade 4 and Grade 6 students and most Grade 8 students explained the behavior of the balance beam using Rule 3, qualitatively coordinating weight and distance (see Table 2). Following the science task, 15 students offered more complex explanations, so that the major- ity at all grade levels used Rule 3, and 1 student regressed to a simpler explanation, a

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TABLE 6 Frequency of Writing Operations by Science Task and Correlation of Writing Operations With Grade

Level and Explanatory Gains

Comparison of Tasks Correlationsa

Buoyancyb Mdn Balancec Mdn Mann- Grade Gains Writing Operation (Interquartile Range) (Interquartile Range) Whitney z (tau-b) (tau-b)

Goal setting and organizing 1.00 (0.00; 2.00) 1.00 (0.00; 1.25) -1.04 .10 .18 Generating 7.00 (3.25; 11.00) 5.00 (2.00; 7.00) -2.09* .04 .41** Generate and transcribe 4.00 (2.00; 6.75) 3.50 (2.00; 6.00) -0.59 .19* .00 Transcribing 3.00 (1.00; 5.75) 1.00 (0.00; 4.00) -2.29* .02 .36** Reviewing experiment 4.50 (2.00; 6.00) 1.00 (0.00; 2.00) -3.91** .18* .44** Reviewing text 0.00 (0.00; 2.00) 1.00 (0.00; 2.00) -1.46 .00 .30** Review prior knowledge 1.00 (0.00; 1.75) 0.00 (0.00; 0.00) -4.07** .00 .02 Evaluating and revising 2.00 (0.00; 5.00) 1.00 (0.00; 3.25) -0.68 .28** .29** Question or comment 3.00 (2.00; 5.00) 3.50 (2.00; 6.00) -0.31 .02 .15 Total operations 26.50 (14.75; 40.75) 20.00 (13.75; 28.00) -1.89 .14 .41**

aN= 70. bn = 36. n = 34.

*p < .05. **p < .01.

significant overall improvement, Wilcoxon matched pairs signed ranks test, z =

-3.40,p < .01. Later, after writing the journal note, Rule 3 continued to be the most common explanation at all grade levels; 5 students constructed more complex ex-

planations, and 2 regressed to simpler explanations, a marginal overall improvement, z = - 1.26,p = .10. Postwriting explanations did not correlate with grade level, because of the high frequency of the Rule 3 explanations across all three grades, tau-b (34) = .17, ns; nor did gains during the writing interval correlate with grade level, tau-b (34) = .03, ns.

Writing Operations

Table 6 represents the relation of writing operations with science task, grade level, and explanatory gains. The frequencies of most writing operations were positively skewed, so nonparametric statistics are reported. The most frequent writing operations were those concerned with production, such as generating, and generating and

transcribing simultaneously. In the buoyancy task, students generated, transcribed, reviewed prior knowledge, and reviewed experimental results significantly more

frequently than in the balance task. The operations that correlated significantly with

grade level were generating and transcribing simultaneously, reviewing the experiment, and evaluating and revising. The operations that predicted explanatory gains during writing were generating, transcribing, reviewing experimental results, reviewing text, and evaluating and revising. Consistent with the fact that the fre-


quency of generating operations and transcribing operations each correlated with explanatory gains, whereas generating and transcribing simultaneously did not, it was also noted that students who made explanatory gains generated a median of 7.00 meaning units more than they transcribed (IQR = 6.00); those who did not make explanatory gains generated a median of only 2.00 more units than they transcribed (interquartile range [IQR] = 3.00), Mann-Whitney UWilcoxon rank sum Wtest, z = -2.52, p < .05. The total number of operations also correlated significantly with explanatory gains. The effects of these operations were later explored further through factor analysis and logistic regression.

Transitional Sequence Among Writing Operations

Recall that transitional probability is the likelihood that a given operation will be followed by a specific target operation. Figure 1 shows the transitional probabilities among these writing operations. Note that this figure represents transitional sequences between individual writing operations, not the position of these operations in the overall time frame of the protocol; for example, a generating-to-transcribing transition could occur at the beginning, middle, or end ofa protocol. Because the total number ofpossible transitions among 9 operations is 92 or 81,1 followed the con- vention of diagramming those that were relatively frequent, that is, greater than or equal to 25% or .25.

Figure 1 shows that goal-setting or organizing was most often followed by either questioning or commenting, or by generating and transcribing simultaneously. Reviewing prior knowledge and reviewing the experiment were both

FIGURE 1 Transitional probabilities, greater than or equal to .25, among writing operations. N=70. *p < .05.

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followed by either generating, or generating and transcribing simultaneously. Generating led to transcribing. Reviewing text was followed by evaluating and re-

vising. All of these transitions exceeded the chance level, except the transition from evaluating and revising to generating. These transitions were generally consistent with process theories of writing: Planning operations were followed by production operations, which were followed by revision operations.

The next question ofprincipal interest in this study was whether transitional probabilities were related to conceptual gains during writing. Testing all 81 possible transitions would have inflated the probability of Type I errors; moreover, participants who did not include a given operation produced missing values for transitions from this operation to all target operations, so that less frequent operations generated large numbers of missing cases. Therefore, only the 10 transitions with probabilities of 25 % or more were selected for further analysis (see Figure 1). First, z scores were cal- culated to represent the extent to which the frequency of each transition departed from the probability of the target operation (see Bakeman & Gottman, 1986). Then, because the number of gainers and nongainers was unequal, a Mann-Whitney nonparametric U test was used to test for significant differences between the two

groups. None of the 10 transitions differed significantly in probability between students who made explanatory gains and those who did not.

To summarize, the frequencies of generating, transcribing, reviewing experimental results, reviewing text, and evaluating and revising were significantly greater for students who made explanatory gains than for those who did not. Both

gainers and nongainers showed above-chance probabilities for transitions consistent with process writing theories: Planning operations were followed by production operations, which were followed by revision operations. However, these transitional probabilities did not differ between gainers and nongainers.

Textual Analysis

Table 7 reports features of students' texts. Because the distributions of most of these measures were positively skewed, nonparametric statistics are reported. Most students' texts included several instances of both explanation and comparison. As the upper half of the table indicates, the frequencies of most rhetorical predicates were significantly greater in the buoyancy task than in the balance task. The grade level of students correlated significantly with the frequency of description predicates. Explanatory gains correlated with the frequencies of three of the rhetorical

predicates: explanations, comparisons, and descriptions. As the lower half of Table 7 shows, the extensive features of the text, including

the number of deep-linked clauses (i.e., those elaborated by multiple descending links), the deepest level (of clauses linked sequentially), and the total number of clauses, were significantly greater for the buoyancy task than for the balance task. All three of the extensive dimensions of text correlated with grade level and with


TABLE 7 Text Features by Science Task and Correlation of Text Features With

Grade Level and Explanatory Gains

Comparison of Tasks Correlationa

Buoyancyb Mdn Balancec Mdn Mann- Grade Gains Text Feature (Interquartile Range) (Interquartile Range) Whitney Z (tau-b) (tau-b)

Explanation 6.00 (4.00; 9.00) 4.00 (3.00; 5.25) -2.29* .08 .32** Comparison 4.00 (2.00; 6.75) 2.00 (1.00; 3.25) -2.49* .09 .25**

Description 2.50 (0.00; 5.00) 1.00 (0.00; 3.25) -2.13* .40** .25** Evidence 1.00 (1.00; 1.75) 1.00 (0.75; 1.00) -2.05* .03 .00 Summary 0.00 (0.00; 1.00) 0.00 (0.00; 1.00) -0.49 .11 .03 Deep-linked clauses 4.00 (2.00; 5.00) 2.00 (2.00; 4.00) -2.20* .26** .22* Deepest level of text 5.00 (4.00; 6.00) 5.00 (4.00; 5.00) -2.05* .35* .33** Total clauses 15.00 (11.25; 21.00) 9.50 (8.00; 12.25) -3.48** .28** .32**

aN= 70. bn = 36. Cn = 34. *p < .05. **p< .01.

explanatory gains during the writing interval. As with the writing operations, these data were later simplified through factor analysis, and their relation to explanatory gains was examined using logistic regression.

Content-Generating Strategies

The purpose of this phase of the analysis was to examine the effects of strategies for generating content on explanatory gains (Table 8). Recall that each strategy was coded at the local level as present or absent. The no-search strategy ap- peared at least once in most protocols; for example, many students employed this strategy first, by recording their initial explanation without explicit goal set-

ting or problem solving; they then proceeded to use at least one explicit content

generating strategy in the remainder of the session. Most students explicitly used the strategy of reviewing experimental results to generate content. Each of the other strategies was employed by a substantial minority of the students. As column 4 shows, none of the strategies differed significantly in frequency between the two science tasks. Column 5 shows that grade level correlated significantly with searching from text and searching from genre and correlated negatively with the use of no-search. The relation of each strategy to explanatory gains followed a somewhat similar pattern: Both searching from text and backward search correlated significantly with explanatory gains. The frequency of the no-search strategy correlated negatively with explanatory gains.

At the global level, because only 2 students relied primarily on searching from genre to organize their text production, this was collapsed with other kinds ofback- ward search for further analysis; because only 1 student relied on forward Search

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TABLE 8 Number of Students Who Used Each Local Content Generating Strategy and Correlation of

Each Strategy With Grade Level and Explanatory Gains

Comparison of Tasks Correlationa

Content Generating Strategies Buoyancy Balancec Phi Grade (tau-b) Gains (phi)

No-search 26 26 -.05 -.29** -.25* Search from experiment 30 24 .15 .13 .20 Search from text 14 10 .10 .21 .43** Genre search 8 3 .18 .20* .16 Backward search 15 14 .00 18 .41**

aN= 70. bn = 36. Cn = 34.

*p < .05. **p < .01.

From Text at the global level, this was collapsed with forward search from experiment. These categories were construed as three levels of an ordinal variable, global content-generating strategy, in which no-search was the lowest level, forward search was the middle level, and backward search was the highest level. This order-

ing was selected to reflect the differing complexity of these search strategies: No-search did not require explicit attention to any writing strategies; forward search

required students to attend to the current problem state (text and experimental re-

sults); and backward search required students to attend to both the current problem state and to a subgoal, such as a genre structure, that they generated to organize the text. Previous research shows that these three levels of strategy (no-search, forward search, and backward search) impose different cognitive loads, and that the more

complex strategies develop later (Bereiter & Scardamalia, 1987; Beringer &

Swanson, 1994; Beringer et al., 1996; Penningroth & Rosenberg, 1995). Overall, forward search was the most common global strategy, used by 42 of the 70 students to organize their text production, compared to no-search used by 22 of the students, and backward search used by 6 ofthe students. The buoyancy taskproduced marginally higher global text generating strategies, (mean rank = 39.28) than the balance task (mean rank = 31.50), z = -1.84,p = .07. Grade level correlated marginally with level of global text generating strategy, tau-b = .20, p = .07.

The global level of text generating strategy correlated significantly with ex-

planatory gains, tau-b = .42, p < .01. Further analysis showed that none of the 22 students who used no-search as their global (non)strategy made explanatory gains, whereas 11 of the 42 students whose global strategy was forward search made

gains, a significant difference, Mann-Whitney U Wilcoxon rank sum W test, z -2.62, p < .01. In turn, 4 of the 6 students who relied primarily on the backward search strategy made explanatory gains, a significantly greater proportion than for forward search, z = -1.98, p < .05.


Integration of Analyses

The analysis to this point has included writing operations, text features, and content generating heuristics, comprising 24 variables in all. Before testing the effects of the independent variables on explanatory gains, it was desirable to explore relations among them, to simplify them, and to reduce their number, to attenuate the probability of Type I errors, so factor analysis was applied. Because the measures of most writing operations and text features were significantly positively skewed, they were all subjected to a logarithmic transformation before being entered into the analysis. The transitional probabilities were not included in the factor analysis because these measures generated missing values for every case in which the frequency of a given operation was zero. It should also be noted that the five local content generating strategies (see Table 8) were dichotomous variables: On one hand, these variables violated the normality assumption of factor analysis. On the other hand, the categorical nature of variables sometimes makes no practical difference in the outcome of a factor analysis, and in this instance, they comprised a small proportion of the total number of variables. In any case, the primary purpose ofthis factor analysis was not to test a hypothesis about the structure of writing activity, but to simplify the data for the subsequent logistic regression analysis, so these variables were accepted for the factor analysis.

The Kaiser-Meyer-Olkin measure of sampling adequacy was .78, indicating that this data set was a good candidate for factor analysis. Seven factors had an eigenvalue of 1.0 or more, and the scree plot confirmed that this was an appropriate point to discontinue adding factors. These seven factors cumulatively accounted for a substantial 71.2% of the total variance. When a varimax rotation was applied, the number of negative factor loadings was substantially reduced, so the theme of each factor could be more readily interpreted. When a nonorthogonal (oblimin) rotation was applied, the results were similar to those of the varimax analysis, so the latter is reported here. The five dichotomous variables did not appear to distort the results of the analysis: None generated a unique factor, and the results of the analysis were largely similar when it was repeated with these factors excluded.

The rotated factor matrix, with variables listed in descending order by loading, appears in Table 9. For each variable, I have indicated in uppercase letters whether it was a writing operation (WO; see Tables 3 and 6), a text feature (TF; see Tables 4 and 7), or a content-generating strategy (CG; see Tables 5 and 8). The reader is encouraged to review the loadings of individual variables to clarify the meaning of the factor names (Table 9).

The Text Production factor was dominated by text feature variables, including total number of clauses, deep-linked clauses (those elaborated by more than one subsequent clause), description predicates, and explanation predicates. Generating and transcribing simultaneously also loaded substantially on this factor. The second factor, Search From Experiment, loaded positively on the operation ofreview-

TABLE 9 Factor Analysis of Writing Operations (WO), Text Features (TF), and

Content Generating Strategies (CG)

Factor 1: Text Factor 2: Search Factor 3: Factor 4: Factor 5: Factor 6: Search Factor 7: Variable Production From Experiment Brainstorming Elaborative Genre Goal Setting From Text Review Beliefs

TF: Total clauses 83 20 24 29 9 12 10 TF: Deep-linked clauses 78 16 6 33 25 -6 0 WO: Generating and transcribing 68 25 -40 9 4 25 -3 TF: Description 66 26 02 -1 6 22 2 TF: Explanation 66 -16 41 21 12 7 3 CG: No search -9 -71 04 -12 -13 4 -15 WO: Review experiment 38 66 38 25 -2 8 11 CG: Search from experiment 10 65 38 24 1 6 -3 CG: Level of global CG 19 50 13 24 49 26 4

CG: Backward search 30 49 09 9 27 12 -1 WO: Transcribing 06 19 82 17 19 3 -2 WO: Generating 24 36 67 6 29 29 4 WO: Time on task 49 32 50 10 13 30 -3 TF: Comparison 16 24 18 81 10 2 14 TF: Summary 24 16 07 63 -10 19 -20 TF: Deepest level 49 21 15 60 21 4 18 WO: Question or comment 06 13 21 -11 76 -6 -9 WO: Goal setting and organizing 16 05 16 6 72 22 24

CG: Genre search 14 11 -09 45 59 29 5 CG: Search from text 10 20 03 16 5 82 -7 WO: Review text 16 -15 13 -3 15 79 9 WO: Evaluating and revising 21 33 3 34 38 50 5

WO: Review prior knowledge 0 02 24 4 11 -6 85 TF: Evidence 11 22 -44 0 -2 15 75

m m m m m _m _


ing experiments, the local content generating strategy of explicitly searching from the experiment, and the level of global content generating strategy. It also loaded heavily and negatively on no search as a local content generating strategy. The Brainstorming factor was dominated by generating, transcribing, and total time on task.

The Elaborative Genre factor loaded most heavily on the comparison and summary rhetorical predicates and on the deepest level of text (i.e., the number of con- secutive clauses in which each elaborated on the preceding one). Goal Setting included questioning and commenting operations, goal-setting or organizing operations, and the use of genre as a local content generating strategy. Search From Text included the local content generating strategy of searching from text, the operation of reviewing the text (regardless of whether or not this was explicitly stated to be a strategy for generating content), and the writing operation of evaluating and revising the text. Finally, the Review Beliefs factor loaded most heavily on reviewing prior knowledge and the frequency of evidence predicates in the text.

The central question of the study was, which factors, if any, predicted learning during writing. To answer this question, a logistic regression analysis was carried out. Logistic regression was selected because the dependent variable was dichotomous (explanatory gains vs. no explanatory gains during the writing interval), and because this method performs well whether or not the independent variables vio- late normality assumptions. The variables were entered into the model in three blocks. Because grade level and science task were determined prior to the writing task, they were entered in the first block. To keep the model as simple as possible, the seven writing factors were entered in the second block, then the interactions of writing factors with one another were entered in the third block. Within each block, the forward selection procedure was applied; the score statistic was used to select variables to enter into the model, then the likelihood ratio was used to test each of the variables then in the model for removal.

The resulting model discriminated 90% of cases successfully (see Table 10). It included a substantial negative constant, consistent with the fact that the overall likelihood of explanatory gains was considerably less than 50%. The residual chi-square for the first block, consisting of task, grade level, and the Task x Grade Level interaction, showed that these variables did not significantly increase the predictiveness of the model. In contrast, the residual chi-square of the second block, consisting of the seven writing factors, indicated that it significantly improved the predictiveness of the model. This was due to three writing factors: Brainstorming showed a partial correlation of .31 with explanatory gains, Search From Text showed a partial correlation of .28, and Search From Experiment showed a partial correlation of .24 with explanatory gains. In addition, Text Pro- duction marginally predicted explanatory gains, with a partial correlation of. 12, (p = .056), so it was included in the model, although the reader should treat this with

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TABLE 10 Logistic Regression of Explantory Gains on Seven Writing Factors

Variable B SEB Score -2LogLR R

Constant -1.30 0.29 Wald= 19.90** Block 1: Residual, X2(3, N= 70) = 2.22,p = .53

Grade 0.34 Task 1.77 Grade x Task 1.40

Block 2: Residual, X2(7, N= 70) = 25.49,p = .0006 Fl: Text production 0.64 0.37 3.54 (p = .060) 3.66 (p = .056) .12 F2: Search from experiment 1.38 0.55 7.60** 8.32** .24 F3: Brainstorming 1.85 0.61 9.60** 15.77** .31 F4: Elaborative genre 0.31 F5: Goal setting 1.89 F6: Search from text 1.29 0.47 8.30** 10.18** .28 F7: Review beliefs 0.02

Constant -2.41 0.58

*p <.05. **p < .01.

some caution. In total, these four factors predicted an acceptable 24.67% of the total variance in explanatory gains.

Because of the large number of possible interactions, even when these were re- stricted to two-way terms, the entire logistic regression was run twice. The first and second blocks were identical in these runs, but the interactions tested in the third block differed. In the first run of the analysis, the two-way interactions of each of the seven writing factors with both age and grade level were tested (Block 3a). The residual chi-square of this block was insignificant, X2(14) = 13.04,p = .52, so further testing of individual terms within the block was likely to produce Type I errors and was therefore inappropriate. In any case, none of these interactions were

statistically significant. In the second run of the analysis, all possible two-way interactions of the seven writing factors with one another were tested (Block 3b). The residual chi-square of this block was also insignificant, X2(21) = 23.01, p = .34. Therefore, further testing of interaction terms was inappropriate, so although Search From Experiment interacted negatively with Search From Text, R = -.25,p < .05, and Brainstorming interacted positively with Search From Text, R =. 16,p < .05, these terms were not included in the model.

DISCUSSION

In this study, a substantial minority of students met the stringent learning criterion of constructing an explanation of a scientific phenomenon more complex than that


they held prior to writing. The task elicited from students a wide range of writing operations, text features, and content generating strategies. Most of the variance among these independent variables was reduced to seven factors. A logistic regression analysis showed that three ofthese factors significantly predicted learning during writing: Brainstorming, Searching From Text, and Searching From Experi- ment. One additional factor, Text Production, marginally predicted learning during writing.

The features of the three significant factors suggest that each comprised a distinct strategy. First, two of the factors included explicit goal-setting statements: Search From Text included explicitly reviewing text for the purpose of generating ideas, and Search From Experiment included explicitly reviewing the experiment to generate ideas. The Brainstorming factor did not include explicit goal setting, but the fact that students who made explanatory gains generated substantially more statements than they recorded suggests a reflective selection among ideas. Second, the strategy interpretation of these factors was supported by the fact that each included operations subordinate to these goals: Search From Text included reviewing and evaluating and revising text; Search From Experiment included re-

viewing experimental trials. Moreover, each of these factors can be viewed as a discrete strategy, rather than

as a component of a single strategy, or a coordinated set of strategies. If the factors had been jointly necessary, this would have generated significant interaction effects among them. Instead, each of the factors singly predicted significant variance in the likelihood of explanatory gains; the block containing interactions among the factors did not add to the predictiveness of the model. In itself, the statistical nonsignificance of the interaction terms could be attributed to the modest power of the logistic regression test, due to the moderate number of participants rather than to a real absence of interactions. However, it may be further noted that of the two pairs of interacting factors that individually produced a significant score statistic, only the combination of Search From Text with Brainstorming showed a positive interaction; that is, joint necessity.

Therefore, the principal findings of this research are that for elementary students, writing-to-learn is strategic, and the strategies that contribute to it are diverse, independent of one another, and additive in their effects. As I noted in the introduction, a study with two samples of student writers, one genre of text, and two content tasks cannot be considered to test hypotheses about writing-to-learn. Future research could employ other writing tasks or more advanced students to explore the possibility that although using strategies individually contributes to learning, using several strategies in coordination may be yet more effective. How- ever, given the extreme paucity of empirical research in this area, it is worth comparing the results reported here to extant hypotheses about writing-to-learn.

Britton's (1982b) hypothesis that writing shapes knowledge "at the point of utterance," without conscious deliberation was not exemplified by these data. The

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factor most similar to shaping at the point of utterance was Brainstorming; it loaded heavily on generating, and operated independently of goal setting and organizing, and evaluating or revising. However, Britton's proposal implies that generating and transcribing simultaneously should predict learning during writing; instead, this was the only writing operation that did not correlate even slightly with learning. Moreover, not a single student who relied primarily on writing without explicitly setting goals and solving content problems (no-search) made explanatory gains. Instead, Brainstorming functioned selectively; students who made ex-

planatory gains generated substantially more meaning units than they recorded. Therefore, the significance of the Brainstorming factor fits somewhat better with Galbraith's (1992) model that includes a role for generating language and ideas spontaneously, but also allows local problem solving and subsequent revision.

The hypothesis that I referred to as forward search (e.g., Donald, 1991; Young & Sullivan, 1984) was consistent with the results reported here. The Search From Text factor, which contributed significantly to learning during writing, included students explicitly reviewing text to generate ideas, and evaluating or revising. Additionally, the transitional probability analysis showed that reviewing text most often led to evaluating or revising, which in turn most often led to generating. For- ward Search From Text is important for theorizing writing-to-learn, because it is one of the few hypotheses that specifically includes a role for writing, in the sense of transcribing, as such. In contrast, the processes identified in some other hypotheses, such as shaping at the point of utterance, or composing in analytic genre, could conceivably be implemented in speech. This makes them hypotheses about learning through discourse, rather than learning through writing specifically.

Conversely, the finding that searching from experimental trials contributed to explanatory gains was not directly anticipated by hypotheses about writ-

ing-to-learn. Recall that this factor included measures of students both explicitly stating their intention to review the experimental trials to generate ideas, and actu-

ally reviewing these trials, and that the transitional probability analysis showed that reviewing experimental results frequently led to generating. Searching from

experimental trials can be viewed as another instance of forward search, in that it was goal directed and consisted of students making inferences from given problem elements (i.e., experimental results). A reading of the protocols indicated that the results of the science task served both as content that students assimilated directly into their texts, and discrepant events that challenged their initial hypotheses, and sometimes evoked accommodation. The strategy of searching from experimental trials can be compared with students' use of textual sources in reading-to-write: A text can serve as a source of structure and content; it can also serve as an interlocu- tor with which students engage dialectically (Flower et al., 1990; Nash et al., 1993). The possibility that writing contributes to learning partially by prompting students to review sources of information is not a prominent part of theories of writing-to-learn, perhaps because it is too obvious to bear mention. However, this


very obviousness may help to explain why writing-to-learn can be effective for relatively young students.

The third hypothesis, that writing in elaborative genres contributes to learning, was not well exemplified by these results. Text Production only marginally predicted explanatory gains, and Elaborative Genre did not predict explanatory gains. These findings contrast with several previous studies that have shown that elaborative genres contribute to learning (e.g., Langer, 1986; Wiley & Voss, 1996). How- ever, other studies have shown mixed effects (e.g., Greene, 1993; Penrose, 1992). In the research reported here, the weak role of genre could be due to the fact that although students who made explanatory gains included elaborative rhetorical predicates, students who did not make gains also frequently included such structures (see Table 7). This suggests that the inclusion of elaborative genre structures is not suffi- cient for learning, but it does not exclude the possibility that such structures are necessary (or nearly necessary) for learning. In a previous study, Klein (1999a) found that university students made explanatory gains while writing elaborative rhetorical predicates about science experiments, but only if: (a) they selected experimental trials for discussion that bore critically on the variables named in their initial hypotheses, (b) the application of genre structures to these trials prompted valid inferences, and (c) students generalized these inferences to other instances ofthe phenomena.

The fourth hypothesis was backward search. Recall that according to Bereiter and Scardamalia's (1987) model, knowledge is transformed when writers pursue a dialectic between solving rhetorical problems and solving content problems. This hypothesis was partially consistent with the results reported here. First, explanatory gains during writing supported at least a weak version of the backward search hypothesis: The goal that students were assigned was a rhetorical one; that is, composing on a specific topic (the experiment) in a specific genre (explanation) for a given audience (the researcher). Any explanatory gains were presumably attained in the pursuit of this rhetorical goal. However, the more interesting question is whether students who explicitly set subgoals beyond those given in the assignment were more likely to make explanatory gains than other students. In favor ofthe backward search hypothesis, a significantly larger proportion of students who used backward search as a global content generating strategy made gains than students who used either forward search or no-search. Moreover, these students' goal-setting statements explicitly connected rhetorical goals and content goals (e.g., "[I'm] rethinking my conclusion," or"[I'm] looking for apattern to write about). On the other hand, backward search did not emerge as a distinct factor in the analysis, instead comprising a variable that loaded on the Search From Experiment factor. Also, although backward search was more effective than forward search, more students actually used forward search, and forward search was significantly more effective than no-search, so it actually accounted for most cases of explanatory gains.

In conclusion, the predominance of diverse, independent, forward search strategies as a means of writing-to-learn can be interpreted as a compromise between

344 KLEIN

two conflicting demands that writing-to-learn makes on students. On one hand, nonsearch operations such as shaping at the point of utterance impose a light cognitive load and require little compositional knowledge, but they probably do not support substantial knowledge change. On the other hand, complex approaches such as backward search or the coordinated use of multiple strategies might prove powerful, but they impose a heavy cognitive load on students and require sophisticated compositional knowledge. Upper elementary school writers have moderate cognitive resources and limited writing strategies. Therefore, the use of forward search from text, forward search from experiment, and brainstorming could be a compromise that permits young writers the generativity of genuinely strategic problem solving within the constraints of their moderate writing abilities.

Educationally, these results suggest that learning through writing could be an appropriate activity for elementary school, both because a substantial minority of students made large explanatory gains with little assistance, and because they made these gains using strategies of modest sophistication. These strategies enlist operations that develop during later elementary schooling, such as goal setting, reviewing, evaluating, and revising (Beringer et al., 1994; Beminger et al., 1996). Moreover, these strategies can be successfully taught to middle to upper elementary students, including low-achieving students (e.g., Bereiter et al., 1988; Englert & Raphael, 1989; Harris & Graham, 1996). A further benefit of such instruction is that these strategies improve the quality of students' writing, so that even if they do not lead students to construct new knowledge in every case, learning them will nonetheless have some educational value. Research is now needed concerning the effects of teaching writing strategies on students' abilities to learn through writing.

ACKNOWLEDGMENT

The author thanks Rena Sheskin and Pamela Silcox for assisting with this research.

REFERENCES

Ackerman, J. M. (1993). The promise of writing to learn. Written Communication, 10, 334-370.

Applebee, A. N. (1984). Writing and reasoning. Review of Educational Research, 54, 577-596.

Atwell, N. (Ed.). (1990). Coming to know: Writing to learn in the intermediate grades. Portsmouth, NH: Heinemann.

Audet, R., H., Hickman, P., & Dobrynina, G. (1996). Learning logs: A classroom practice for enhancing scientific sense making. Journal of Research in Science Teaching, 33, 205-222.

Bakeman, R., & Gottman, J. M. (1986). Observing interaction: An introduction to sequential analysis. Cambridge, England: Cambridge University Press.

Beins, B. C. (1993). Writing assignments in statistics classes encourage students to learn interpretation. Teaching of Psychology, 20, 161-164.


Benton, S. L., Kiewra, K. A., Whitfill, J. M., & Dennison, R. (1993). Encoding and external storage effects on writing processes. Journal of Educational Psychology, 85, 267-280.

Bereiter, C., Burtis, P. J., & Scardamalia, M. (1988). Cognitive operations in constructing main points in written composition. Journal of Memory and Language, 27, 261-278.

Bereiter, C., & Scardamalia, M. (1987). The psychology of written composition. Hillsdale, NJ: Law- rence Erlbaum Associates, Inc.

Berninger, V. W., Cartwright, A. C., Yates, C. M., Swanson, H. L., & Abbott, R D. (1994). Develop- mental skills related to writing and reading acquisition in the intermediate grades. Reading and Writ- ing: An Interdisciplinary Journal, 6, 161-196.

Beringer, V. W., & Swanson, H. L. (1994). Modifying Hayes and Flower's model of skilled writing to explain beginning and developing writing. Advances in Cognition and Educational Practice, 2, 57-81.

Beminger, V., Whitaker, D., Feng, Y., Swanson, H. L., &Abbott, R. D. (1996). Assessment ofplanning, translating, and revising in junior high writers. Journal of School Psychology, 34, 23-52.

Beminger, V., Yates, C., Cartwright, A., Rutberg, J., Remy, E., & Abbott, R. (1992). Lower-level developmental skills in beginning writing. Reading and Writing: An Interdisciplinary Journal, 4, 257-280.

Bourdin, B., & Fayol, M. (1994). Is written language production more difficult than oral language production? A working memory approach. International Journal of Psychology, 29, 591-620.

Boyles, M. P., Killian, P. W., & Rileigh, K. K. (1994). Learning by writing in introductory psychology. Psychological Reports, 75, 563-568.

Breetvelt, I., van den Bergh, H., & Rijlaarsdam, G. (1994). Relations of writing processes and text quality: When and how. Cognition and Instruction, 12, 103-123.

Bridges, L. (1997). Writing as a way of knowing. Los Angeles: The Galef Institute. Britton, J. (1982a). Notes on a working hypothesis about writing. In G. M. Pradl (Ed.), Prospect andret-

rospect: Selected essays of James Britton (pp. 123-129). Montclair, NJ: Boynton/Cook. (Original work published 1979)

Britton, J. (1982b). Shaping at the point of utterance. In G. M. Pradl (Ed.), Prospect and retrospect: Se- lected essays of James Britton (pp. 139-145). Montclair, NJ: Boynton/Cook. (Original work published 1980)

Britton, J., Burgess, T., Martin, N., McLeod, A., & Rosen, H. (1975). School councils research studies: The development of writing abilities (11-18). London: Macmillan Education.

Cameron, C. A., & Moshenko, B. (1996). Elicitation of knowledge transformational reports while children write narratives. Canadian Journal of Behavioural Science, 28, 271-280.

Case, R. (Ed.). (1992). The mind's staircase: Exploring the conceptual underpinnings of children's thought and knowledge. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Chambliss, M. J. (1995). Text cues and strategies successful readers use to construct the gist of lengthy written arguments. Reading Research Quarterly, 30, 778-807.

Copeland, K. A. (1987, May). The effect of writing upon good andpoor writers' learningfrom prose (Tech. Rep.). (ERIC Document Reproduction Service No. ED 276 993)

Davis, B. H., Rooze, G. E., & Runnels, M. K. T. (1992). Writing-to-learn in elementary social studies. Social Education, 56, 393-397.

Donald, M. (1991). Origins of the modern mind. Cambridge, MA: Harvard University Press. Emig, J. (1977). Writing as a mode oflearning. College Composition and Communication, 28, 122-128. Englert, C. S., & Raphael, T. E. (1989). Developing successful writers through cognitive strategy in-

struction. Advances in Research on Teaching, 1, 105-151. Englert, C. S., Stewart, S. R., & Hiebert, E. H. (1988). Young writers' use oftext structure in expository

text generation. Journal ofEducational Psychology, 80, 143-151. Flower, L., & Hayes, J. R. (1980). The cognition of discovery: Defining a rhetorical problem. College

Composition and Communication, 31, 21-32.

346 KLEIN

Flower, L. S., & Hayes, J. R. (1981). A cognitive process theory of writing. College Composition and Communication, 32, 365-387.

Flower, L., Stein, V., Ackerman, J., Kantz, M. J., McCormick, K., & Peck, W. C. (1990). Read-

ing-to-write: Exploring a cognitive and social process. New York: Oxford University Press. Foos, P. W. (1995). The effect of variations in text summarization opportunities on test performance.

Journal of Experimental Education, 63, 89-95. Galbraith, D. (1992). Conditions for discovery through writing. Instructional Science, 21, 45-72.

Galbraith, D. (1996). Self-monitoring, discovery through writing, and individual differences in drafting strategy. In G. Rijlaarsdam, H. van den Bergh, & M. Couzjin (Eds.), Theories, models and methodol-

ogy in writing research (pp. 121-141). Amsterdam: Amsterdam University Press.

Goody, J., & Watt, I. (1968). The consequences of literacy. In J. Goody (Ed.), Literacy in traditional so- cieties (pp. 27-68). New York: Cambridge University Press.

Greene, S. (1993). The role of task in the development of academic thinking through reading and writing in a college history course. Research in the Teaching of English, 27, 46-75.

Guthrie, J. T., & Cox, K. E. (1998). Portrait of an engaging classroom: Principles of concept-oriented reading instruction for diverse students. In K. R Harris, S. Graham, & D. Deshler (Eds.), Teaching every child ev-

ery day: Learning in diverse schools and classrooms (pp. 77-130). Cambridge, MA: Brookline.

Halford, G. S., Brown, C. A., & Thompson, R. L. (1986). Children's concepts of volume and floatation.

Developmental Psychology, 22, 218-222. Hardiman, P. T., Pollatsek, A., & Well, A. D. (1986). Learning to understand the balance beam. Cogni-

tion and Instruction, 3, 63-86. Harris, K. R., & Graham, S. (1996). Making the writingprocess work: Strategiesfor composition and

self-regulation. Cambridge, MA: Brookline.

Harris, R. (1989). How does writing restructure thought? Language and Communication, 9, 99-106.

Hayes, D. A. (1987). The potential for directing study in combined reading and writing activity. Journal

ofReading Behavior, 19, 333-352.

Hayes, J. R., Flower, L., Schriver, K. A., Stratman, J. F., & Carey, L. (1989). Cognitive processes in revision. In S. Rosenberg (Ed.), Advances in applied psycholinguistics: Vol. 2. Reading, writing, and

language learning (pp. 176-240). Cambridge, England: Cambridge University Press.

Hinkle, S., & Hinkle, A. (1990). An experimental comparison of the effects of focused freewriting and other strategies on lecture comprehension. Teaching of Psychology, 17, 31-35.

Horton, P. B., Fronk, R. H., & Walton, R. W. (1985). The effect of writing assignments on achievement in college general chemistry. Journal of Research in Science Teaching, 22, 535-541.

Inhelder, B., & Piaget, J. (1958). The growth of logical thinkingfrom childhood to adolescence. New York: Basic.

Juel, C., Griffith, P. L., & Gough, P. B. (1986). Acquisition of literacy: A longitudinal study of children in first and second grade. Journal of Educational Psychology, 78, 243-255.

Klein, P. D. (1999a). Learning science through writing: The role of rhetorical structures. The Alberta Journal of Educational Research, 45, 132-153.

Klein, P. D. (1999b). Reopening inquiry into cognitive processes in writing-to-learn. Educational Psy- chology Review, 11, 203-270.

Kohn, A. S. (1993). Preschoolers' reasoning about density: Will it float? Child Development, 64, 1637-1650.

Konopak, B. C., Martin, S. H., & Martin, M. A. (1990). Using a writing strategy to enhance sixth-grade students' comprehension of content material. Journal of Reading Behavior, 22, 19-37.

Langer, J. A. (1986). Children reading and writing: Structures and strategies. Norwood, NJ: Ablex.

Langer, J. A., & Applebee, A. N. (1987). How writingshapes thinking: A study ofteaching and learning. Urbana, IL: National Council of Teachers of English.

Marshall, J. D. (1987). The effects of writing on students' understanding of literary text. Research in the

Teaching of English, 21, 30-63.


McCrindle, A. R., & Christensen, C. A. (1995). The impact of learning journals on metacognitive and cognitive processes and learning performance. Learning and Instruction, 5, 167-185.

Murray, D. M. (1978). Intenal revision: A process ofdiscovery. In C. R. Cooper & L. Odell (Eds.), Research on composing: Points ofdeparture(pp. 85-103). Urbana, IL: National CouncilofTeachers ofFnglish.

Nash, J. G., Schumacher, G. M., & Carlson, B. W. (1993). Writing from sources: A structure-mapping model. Journal of Educational Psychology, 85, 159-170.

Newell, G. E. (1984). Learning from writing in two content areas: A case study/protocol analysis. Re- search in the Teaching of English, 18, 265-287.

Newell, G. E. (1998). "How much are we the wiser?" Continuity and change in writing and learning in the content areas. In N. Nelson, R. C. Calfee, & K. J. Rehage (Eds.), The reading-writing connection: Ninety-seventh yearbook of the national society for the study of education, Part II (pp. 178-202). Chicago: University of Chicago Press.

Newell, G. E., & Winograd, P. (1989). The effects of writing on learning from expository texts. Written Communication, 6, 196-217.

Newkirk, T. (1987). The non-narrative writing of young children. Research in the Teaching ofEnglish, 21, 121-144.

Olson, D. R. (1996). Towards a psychology of literacy: On the relations between speech and writing. Cognition, 60, 83-104.

Olson, V. L. B. (1990). The revising processes of sixth grade writers with and without peer feedback. Journal of Educational Psychology, 84, 22-29.

Ong, W. J. (1982). Orality and literacy. New York: Methuen. Penningroth, S. L., & Rosenberg, S. (1995). Effects of high information-processing load on the writing

process and the story written. Applied Psycholinguistics, 16, 189-210. Penrose, A. M. (1992). To write or not to write: Effects of task and task interpretation on learning

through writing. Written Communication, 9, 465-500. Peturson, R. (Ed.). (1990). Innovations in science: One to Six. Toronto: HBJ-Holt. Rivard, L. P. (1994). A review of writing to learn in science: Implications for practice and research.

Journal of Research in Science Teaching, 31, 969-983. Rosaen, C. L. (1990). Improving writing opportunities in elementary classrooms. The Elementary

School Journal, 90, 418-434. Rowell, P. M. (1997). Learning in school science: The promises and practices of writing. Studies in Sci-

ence Education, 30, 19-56. Scardamalia, M., & Bereiter C. (1985). Development of dialectical processes in composition. In D. R.

Olson, N. Torrance, & A. Hildyard (Eds.), Literacy and language, and learning: The nature and consequences of reading and writing (pp. 307-329). Cambridge, England: Cambridge University Press.

Scardamalia, M., & Bereiter, C. (1989). Knowledge telling and knowledge transforming in written composition. In S. Rosenberg (Ed.), Advances in appliedpsycholinguistics: Vol. 2. Reading, writing and language learning (pp. 142-175). Cambridge, England: Cambridge University Press.

Scardamalia, M., Bereiter, C., & Steinbach, R. (1984). Teachability of reflective processes in written composition. Cognitive Science, 8, 173-190.

Schumacher, G. M., & Nash, J. G. (1991). Conceptualizing and measuring knowledge change due to writing. Research in the Teaching of English, 25, 67-96.

Siegler, R. S. (1985). Encoding and the development of problem solving. In S. F. Chipman, J. W. Segal, & R. Glaser (Eds.), Thinking and learning skills: Vol. 2. Research and open questions (pp. 161-185). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Smith, C., Carey, S., & Wiser, M. (1985). On differentiation: A case study of the development of concepts of size, weight and density. Cognition, 21, 177-237.

Spivey, N. N. (1990). Transforming texts: Constructive processes in reading and writing. Written Com- munication, 7, 256-287.

348 KLEIN

van Gelderen, A. (1997). Elementary students' skills in revising: Integrating quantitative and qualitative analysis. Written Communication, 14, 360-397.

Wadlington, E., Bitner, J., Partridge, E., & Austin, S. (1992). Have a problem? Make the writing-mathematics connection! Arithmetic Teacher, 40, 207-209.

Wallace, D. L., & Hayes, J. R. (1991). Redefining revision for freshmen. Research in the Teaching of English, 25, 54-66.

Wiley, J., & Voss, J. F. (1996). The effects of"playing historian" on learning in history. Applied Cogni- tive Psychology, 10, S63-S72.

Wright, R. E., & Rosenberg, S. (1993). Knowledge oftext coherence and expository writing: A developmental study. Journal of Educational Psychology, 85, 152-158.

Young, R., & Sullivan, P. (1984). Why write? A reconsideration. In R. J. Conners, L. S. Ede, & A. A. Lunsford (Eds.), Essays on classical rhetoric and modern discourse (pp. 215-225). Carbondale: Southern Illinois University Press.

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