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The Effects of Rhetorical and Content Subgoals on Writing and Learning

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Title: The Effects of Rhetorical and Content Subgoals on Writing and Learning
Language: English
Authors: Klein, Perry D., Haug, Katrina N., Arcon, Nina
Source: Journal of Experimental Education. 2017 85(2):291-308.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 18
Publication Date: 2017
Document Type: Journal Articles
Reports - Research
Education Level: Grade 5
Intermediate Grades
Middle Schools
Elementary Education
Grade 6
Grade 7
Junior High Schools
Secondary Education
Descriptors: Writing (Composition), Persuasive Discourse, Cues, Content Area Writing, Grade 5, Grade 6, Grade 7, Control Groups, Path Analysis, Learning Processes, Science Instruction, Classification, Scaffolding (Teaching Technique), Biodiversity, Foreign Countries, Pretests Posttests, Statistical Analysis, Multigraded Classes, Multivariate Analysis, Likert Scales
Geographic Terms: Canada
DOI: 10.1080/00220973.2016.1143795
ISSN: 0022-0973
Abstract: Argument writing is challenging for elementary students. Previous experimental research has focused on scaffolding rhetorical goals, leaving content goals relatively unexplored. In a randomized experiment, 73 students in grades 5, 6, and 7 wrote persuasive texts about difficult-to-classify vertebrates. Each student received one of three sets of writing prompts: a persuasive goal only (control); a persuasive goal + rhetorical-subgoal prompts; or a persuasive goal + content-subgoal prompts. Rhetorical subgoals increased text quality, variety of rhetorical moves, number of complex propositions, and classification knowledge. Content subgoals increased the number of simple propositions in text. A path analysis indicated that content-subgoal prompts and rhetorical-subgoal prompts elicited different paths to writing and learning.
Abstractor: As Provided
Number of References: 56
Entry Date: 2017
Accession Number: EJ1126572
Database: ERIC
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  Value: <anid>AN0121040059;jxe01apr.17;2019Mar08.13:35;v2.2.500</anid> <title id="AN0121040059-1">The Effects of Rhetorical and Content Subgoals on Writing and Learning. </title> <p>Argument writing is challenging for elementary students. Previous experimental research has focused on scaffolding rhetorical goals, leaving content goals relatively unexplored. In a randomized experiment, 73 students in grades 5, 6, and 7 wrote persuasive texts about difficult-to-classify vertebrates. Each student received one of three sets of writing prompts: a persuasive goal only (control); a persuasive goal + rhetorical-subgoal prompts; or a persuasive goal + content-subgoal prompts. Rhetorical subgoals increased text quality, variety of rhetorical moves, number of complex propositions, and classification knowledge. Content subgoals increased the number of simple propositions in text. A path analysis indicated that content-subgoal prompts and rhetorical-subgoal prompts elicited different paths to writing and learning.</p> <p>Keywords: Argumentation; cognitive processes; science learning; writing; writing goals; writing to learn</p> <hd id="AN0121040059-2">The problem</hd> <p>ARGUMENT WRITING CAN be an effective means of communicating and learning in the subject areas (Wiley & Voss, [<reflink idref="bib51" id="ref1">51</reflink>]). However, elementary students find argument writing challenging (Ferretti, Lewis, & Andrews-Weckerly, [<reflink idref="bib15" id="ref2">15</reflink>]; Means & Voss, [<reflink idref="bib36" id="ref3">36</reflink>]). The difficulty of argument writing for many students affects both their text quality and their ability to use argumentation as a means of learning (Klein & Rose, [<reflink idref="bib29" id="ref4">29</reflink>]; Klein & Kirkpatrick, [<reflink idref="bib28" id="ref5">28</reflink>]). Several studies have shown that scaffolding elementary students by prompting rhetorical goals can contribute to the holistic quality of their texts (Ferretti, Lewis, & Andrews-Weckerly, [<reflink idref="bib15" id="ref6">15</reflink>]; Graham, McKeown, Kiuhara, & Harris, [<reflink idref="bib19" id="ref7">19</reflink>]); however, to date, experimental research on the effects of prompting rhetorical goals on learning has been limited (Hebert, Gillespie, & Graham, [<reflink idref="bib22" id="ref8">22</reflink>]). Additionally, initial research suggests that prompting content goals may contribute to writing and learning (Butcher & Kintsch, [<reflink idref="bib9" id="ref9">9</reflink>]; McNeill & Krajcik, [<reflink idref="bib35" id="ref10">35</reflink>]); however, nearly all experimental research on elementary writing has focused on prompting rhetorical goals rather than content goals (see Graham, McKeown, Kiuhara, & Harris, [<reflink idref="bib19" id="ref11">19</reflink>]). The present study investigated the effects of rhetorical- and content-goal prompting on writing, cognitive load, and learning in elementary science.</p> <hd id="AN0121040059-3">Argumentation in content area writing and learning</hd> <p>In the content areas, argumentation can allow students to communicate their ideas, to think critically about controversial topics, and to improve their understanding of these topics (Cavagnetto, [<reflink idref="bib10" id="ref12">10</reflink>]; Schwarz, [<reflink idref="bib44" id="ref13">44</reflink>]; Wiley & Voss, [<reflink idref="bib51" id="ref14">51</reflink>]). In science for example, argumentation is an important part of professional discourse. It has also become an important part of science education (Cavagnetto, [<reflink idref="bib10" id="ref15">10</reflink>]; Kuhn, [<reflink idref="bib31" id="ref16">31</reflink>]; Osborne, [<reflink idref="bib41" id="ref17">41</reflink>]). Argumentation can allow students to consider various conceptions, and choose among them on the basis of evidence (Asterhan & Schwarz, [<reflink idref="bib2" id="ref18">2</reflink>]; Nussbaum & Sinatra, [<reflink idref="bib39" id="ref19">39</reflink>]; Nussbaum, Sinatra, & Poliquin, [<reflink idref="bib40" id="ref20">40</reflink>]). To date, most studies of argumentation in science have focused on face-to-face or online dialogue (e.g., Asterhan & Schwarz, [<reflink idref="bib2" id="ref21">2</reflink>]; Chen & She, [<reflink idref="bib11" id="ref22">11</reflink>]; see Cavagnetto, [<reflink idref="bib10" id="ref23">10</reflink>], for a review). However, writing can also be an effective modality for learning (Bangert-Drowns, Hurley, & Wilkinson, [<reflink idref="bib3" id="ref24">3</reflink>]; Hebert, Gillespie, & Graham, [<reflink idref="bib22" id="ref25">22</reflink>]). Argument writing has been incorporated into several science education methods (e.g., Bell & Linn, [<reflink idref="bib4" id="ref26">4</reflink>]; Chen & She, [<reflink idref="bib11" id="ref27">11</reflink>]; Hand, Wallace, & Yang, [<reflink idref="bib20" id="ref28">20</reflink>]).</p> <p>However, the difficulty of argument writing for elementary and secondary students affects their ability to use argumentation as a means of learning (Gil, Bråten, Vidal-Abarca, & Strømsø, [<reflink idref="bib16" id="ref29">16</reflink>]; Klein & Kirkpatrick, [<reflink idref="bib28" id="ref30">28</reflink>]; Means & Voss, [<reflink idref="bib36" id="ref31">36</reflink>]). Argument writing includes two dimensions, which will be referred to here as <emph>rhetorical</emph> and <emph>content</emph> (Bereiter & Scardamalia, [<reflink idref="bib5" id="ref32">5</reflink>]; Butcher & Kintsch, [<reflink idref="bib9" id="ref33">9</reflink>]; for related distinctions, see Means & Voss, [<reflink idref="bib36" id="ref34">36</reflink>]; Schworm & Renkl, [<reflink idref="bib45" id="ref35">45</reflink>]). For example, in the present study, students were asked to write persuasive texts about animals that are difficult to classify. The top-level goal for this task included both a rhetorical aspect (persuasion) and a content aspect (biological taxonomy). In this paper, we will refer to the top-level goal that guides a piece of writing as the "goal" and to the particular goals that are subordinate to this top-level goal, which guide more local writing decisions, as "subgoals."</p> <hd id="AN0121040059-4">Content subgoals, writing, and learning</hd> <p>Content subgoals refer to the conceptual (i.e., topical) knowledge that the writer could address in a text. Topic knowledge affects planning, text production, and revision (Hayes, [<reflink idref="bib21" id="ref36">21</reflink>]; see McCutchen, Teske, & Bankston, [<reflink idref="bib34" id="ref37">34</reflink>], for a review). One approach to scaffolding nonfiction writing is to provide students with conceptual subgoals in the form of subtopics to be addressed. For example, the National Council of Teachers of English (n.d.) provides an online resource that allows students to create an inquiry project on animals; it prompts students to select a graphic organizer that includes animal facts, babies, interactions, and habitats; in turn, each of these organizers embeds prompts to write about specific concepts; for example, the "Facts" organizer leads to prompts to write sentences about animal appearance, movement, and diet.</p> <p>It can be theorized that content-goal prompts elicit the writer's attention to the conceptual matter of a text. Bereiter and Scardamalia's model of writing (1987; Chuy, Scardamalia, & Bereiter, [<reflink idref="bib12" id="ref38">12</reflink>]; Kellogg, [<reflink idref="bib25" id="ref39">25</reflink>]) has been influential in the literature on writing goals (e.g., Butcher & Kintsch, [<reflink idref="bib9" id="ref40">9</reflink>]; McNeill & Krajcik, [<reflink idref="bib35" id="ref41">35</reflink>]). This model includes a content problem space in which the writer addresses the question "What do I mean?" and a rhetorical problem space in which the writer addresses the question, "What do I say?" Writers who are relative novices may rely on a <emph>knowledge-telling strategy</emph>. In this strategy, the writer retrieves or constructs knowledge in the content space and then translates this knowledge into a goal in the rhetorical space to express this content. Conversely, more expert writers may rely on a knowledge-transforming strategy. This is a dialectical strategy, in which the writer may initially adopt a rhetorical goal. The writer translates this into a content subgoal, which is pursued in the content space, using operations such as inference and decision-making. The resulting proposition may then be translated back into the rhetorical space for expression in writing.</p> <p>The study that has most directly investigated the effects of content-subgoal prompts on writing involved having university students compose introduction sections for psychology articles (Butcher & Kintsch, [<reflink idref="bib9" id="ref42">9</reflink>]). First, students studied guidelines for writing an introduction section and background information about the topic of the article and the text sections that would follow the introduction and constitute the remainder of the article. In a 2 × 2 design, participants received either rhetorical or content prompts, no prompt, or both rhetorical and content prompts. In the first experiment, participants received the prompts after reading the sources—that is, immediately prior to writing. The results were complex: Briefly, the content prompts led the writers to invest more time in planning and drafting and to write holistically better texts. In a second experiment, the prompts were provided prior to reading the source material. Rhetorical prompts somewhat increased recall of source material; content prompts increased recall of source material further and increased planning and drafting time and the quality of students' texts. The authors interpreted these results in terms of Bereiter and Scardamalia's ([<reflink idref="bib5" id="ref43">5</reflink>]) knowledge-telling model, proposing that for these writers, who were novices in the science-report genre, the content prompts elicited problem solving in the content space and that the participants translated the content generated into text; the writers were less able to translate the rhetorical goals into content goals and content problem solving (cf. McNeill & Krajcik, [<reflink idref="bib35" id="ref44">35</reflink>]).</p> <p>Whereas Butcher and Kintsch ([<reflink idref="bib9" id="ref45">9</reflink>]) focused on the introduction to the psychology article, in the present study, the focus was argumentation, because of its role in learning in science (Kuhn, [<reflink idref="bib31" id="ref46">31</reflink>]; Osborne, [<reflink idref="bib41" id="ref47">41</reflink>]). We hypothesized that content subgoals, compared to a control condition (goal only, with no subgoal prompts), would result in more concepts being addressed, a larger number of complex propositions in text, higher text quality, and higher learning as operationalized by a posttest of content understanding.</p> <hd id="AN0121040059-5">Scaffolding rhetorical subgoals</hd> <p>"Rhetoric" is defined here as the communicative dimension of a text—that is, its intended effect on the reader. In argumentation, a common type of rhetorical goal is that of persuading the reader (Walton, [<reflink idref="bib50" id="ref48">50</reflink>]). The types of rhetorical subgoals that can serve as means to persuasion include the following: giving reasons to support a claim, acknowledging an alternative claim, counterarguing against the alternative claim or reasons, rebutting a counterargument to the writer's claim, and creating a conclusion (e.g., Ferretti, Lewis, & Andrews-Weckerly, [<reflink idref="bib15" id="ref49">15</reflink>]; cf. Kuhn, [<reflink idref="bib31" id="ref50">31</reflink>]). Many elementary and secondary students create arguments that are brief, comprising only an opinion and a few reasons; they typically omit dialectical aspects of argumentation, such as counterarguments and rebuttals, and neglect evidence for claims that differ from their own (Felton & Kuhn, [<reflink idref="bib14" id="ref51">14</reflink>]; Ferretti, Lewis, & Andrews-Weckerly, [<reflink idref="bib15" id="ref52">15</reflink>]; Means & Voss, [<reflink idref="bib36" id="ref53">36</reflink>]; Wolfe, Britt, & Butler, [<reflink idref="bib54" id="ref54">54</reflink>]).</p> <p>To scaffold elementary writers, several researchers have investigated the effect of providing prompts for rhetorical subgoals. In a comprehensive meta-analysis of experimental research on elementary writing instruction, it was found that prompting "product goals," which frequently are equivalent to what have been called here rhetorical-argument subgoals, reliably improved holistic text quality (Graham, McKeown, Kiuhara, & Harris, [<reflink idref="bib19" id="ref55">19</reflink>]). For example, Ferretti, Lewis, and Andrews-Weckerly ([<reflink idref="bib15" id="ref56">15</reflink>]) asked fifth- and sixth-grade students to write arguments. Students in an elaborated goal condition received grade-appropriate prompts to address the following: reasons, an alternative claim, reasons for the alternative claim, rebuttals to these reasons, and a conclusion. The elaborated-goal group, compared to a group that received only the general-argument goal, produced texts that were significantly higher in holistic quality (cf. Golanics & Nussbaum, [<reflink idref="bib17" id="ref57">17</reflink>]; Nussbaum & Kardash, [<reflink idref="bib38" id="ref58">38</reflink>]).</p> <p>A question concerns the role of rhetorical subgoals in writing to learn. According to the knowledge-transforming model discussed above (Bereiter & Scardamalia, [<reflink idref="bib5" id="ref59">5</reflink>]), skilled writers engage in a dialectic between rhetorical problem solving and content problem solving. As noted above, several studies have demonstrated that rhetorical subgoals increase text quality but very few have investigated the effect of rhetorical subgoals on learning. The knowledge-transforming model implies that argumentation subgoals could elicit reasoning about content in several ways: evaluating claims on the basis of evidence, considering alternative claims, counterarguing against alternative claims, and rebutting counterarguments.</p> <p>Previous research has shown that the variety of argument moves that students include in text predicts their learning during argument writing (Klein & Samuels, [<reflink idref="bib30" id="ref60">30</reflink>]). In a quasi-experimental study with middle school students, McNeill and Krajcik ([<reflink idref="bib35" id="ref61">35</reflink>]) scaffolded scientific "explanations" (arguments) using either domain-general prompts (similar to "rhetorical subgoals" here) or context-specific prompts (similar to "content subgoals" here). They found that context-specific prompts supported explanation writing more effectively than domain-general prompts but only when the teacher also provided domain-general support. Other studies have included argument or explanation writing with rhetorical and content goals, but the effects of these two kinds of goals have not been investigated separately (Lee & Songer, [<reflink idref="bib32" id="ref62">32</reflink>]; Sandoval & Reiser, [<reflink idref="bib43" id="ref63">43</reflink>]).</p> <p>The results of the two studies discussed above (Butcher & Kintsch, [<reflink idref="bib9" id="ref64">9</reflink>]; McNeill & Krajcik, [<reflink idref="bib35" id="ref65">35</reflink>]) are similar in providing initial support for the effects of content goals on writing and learning. However, further research is needed. In these studies, the rhetorical goals and content goals were not entirely comparable. For example, Butcher and Kintsch ([<reflink idref="bib9" id="ref66">9</reflink>]) implemented content goals by providing five specific content prompts, but implemented rhetorical goals by providing two general rhetorical prompts, so the conditions differed with respect to the type of goals, the number of goals, and the goals' specificity.</p> <p>In order to control for specificity, we make a distinction between goal setting, which is relatively global and concerns the overall purpose of a text, and subgoal setting, which is more local. By focusing on the subgoal level, it is possible to compare content prompts and rhetorical prompts, which are equal in number and equally focused on a relatively local level of discourse. For example, in an argument on the question, "Is a dolphin a fish or a mammal?" the rhetorical-subgoal prompt of "giving a reason" in a rhetorical-subgoal condition can be paralleled by the content-subgoal prompt of "respiration" in the content-subgoal condition. In response to either type of prompt, a student could write, "A dolphin is mammal because it breathes with lungs."</p> <p>Consequently, based on the notion that rhetorical-subgoal prompts can elicit critical reasoning about content, we hypothesized that rhetorical-subgoal prompts, compared to a control condition (persuasive goal prompt with no subgoals), would result in a greater variety of rhetorical moves, more frequent complex propositions in text, higher text quality, and a higher posttest score on classification.</p> <hd id="AN0121040059-6">Writing subgoals and cognitive load</hd> <p>The theory above implies that content-subgoals prompt the retrieval of conceptual information from long-term memory or text sources relatively directly (Bereiter & Scardamalia, [<reflink idref="bib5" id="ref67">5</reflink>]; cf. Kellogg, [<reflink idref="bib25" id="ref68">25</reflink>]). This theory is consistent with the findings of previous comparisons of content and rhetorical goals discussed above (Butcher & Kintsch, [<reflink idref="bib9" id="ref69">9</reflink>]; McNeill & Krajcik, [<reflink idref="bib35" id="ref70">35</reflink>]). The knowledge-transforming model implies that, in contrast, a rhetorical-subgoal prompt such as "provide reasons" would only cue the retrieval of content knowledge indirectly; that is, the learner must first interpret what kind of content could constitute a "reason" and then search for it.</p> <p>The effects of subgoal setting in learning activities have been extensively theorized and investigated in cognitive load theory (e.g., Sweller, Ayres, & Kalyuga, [<reflink idref="bib48" id="ref71">48</reflink>]). Cognitive load theory recognizes three sources of working-memory load in learning activities. Intrinsic cognitive load is determined by the number of interacting elements that the learner must relate to one another and by the learner's prior knowledge. Extraneous cognitive load is imposed by inefficient instructional methods, such as unnecessary means-end problem solving. Germane cognitive load arises from activities that contribute to learning, such as self-explanation. (Currently, an issue is whether germane cognitive load should be conceptualized as a component of intrinsic cognitive load or as a separate source of cognitive load that combines additively with it, e.g., Kalyuga, [<reflink idref="bib23" id="ref72">23</reflink>]).</p> <p>Previous research has shown that an important source of extraneous cognitive load is means-end problem solving. For example, Sweller ([<reflink idref="bib46" id="ref73">46</reflink>]) found that mathematics tasks that require means-end problem solving resulted in significantly higher cognitive load and lower learning than tasks that did not require means-end problem solving (cf. Sweller, [<reflink idref="bib47" id="ref74">47</reflink>]; Wirth, Künsting, & Leutner, [<reflink idref="bib52" id="ref75">52</reflink>]). Based on the analysis above, we proposed that content-subgoal prompts relatively directly elicit content that would support inferences and learning; conversely, rhetorical-subgoal prompts would only indirectly elicit content that would support inferences and learning; that is, rhetorical goal setting is based on an additional level of subgoaling, or means-end problem solving. Therefore, we hypothesized that content subgoals would result in lower extraneous cognitive load than rhetorical subgoals, and produce higher posttest scores on classification knowledge.</p> <hd id="AN0121040059-7">The present study</hd> <p>In the present study, the effects of rhetorical and content subgoals were investigated in the context of writing to learn about the classification of vertebrates. Classification is a common elementary science topic, which provides a foundation for understanding anatomy, physiology, and evolution. Classification lends itself to argumentation as a means of critical thinking due to the prevalence of student misconceptions about this topic (e.g., Braund, [<reflink idref="bib6" id="ref76">6</reflink>]; Burgoon & Duran, [<reflink idref="bib8" id="ref77">8</reflink>]; Kattmann, [<reflink idref="bib24" id="ref78">24</reflink>]). Many students classify animals based on surface features or behaviors rather than essential or internal characteristics; they classify nonhierarchically; and they have limited knowledge of the characteristics of each class of organisms (e.g., amphibians). These misconceptions lead students to misclassify specific animals: for example, many elementary students believe that dolphins are fish; turtles are amphibians; and penguins are mammals (Prokop, Kubiatko, & Fančovičová, [<reflink idref="bib42" id="ref79">42</reflink>]; Yen, Yao, & Chiu, [<reflink idref="bib55" id="ref80">55</reflink>]; Yen, Yao, & Mintzes, [<reflink idref="bib56" id="ref81">56</reflink>]).</p> <p>In the current study, students completed argument-writing activities about vertebrates that are difficult to classify. Each student was randomly assigned to write in one of three conditions: Students in the argument goal-only (control) condition received a prompt asking them to classify an animal and persuade the reader of their opinion; students in the rhetorical-subgoal prompt condition received the same argument goal prompt, plus six rhetorical-subgoal prompts (to include several reasons for the opinion, an alternative claim, reasons for the alternative claim, and a counterargument to the alternative claim); students in the content-subgoal condition received the argument goal prompt, plus six content-subgoal prompts concerning attributes used for classification: respiration; skin or body covering; warm-blooded or cold-blooded; birth; feeding of young; and movement.</p> <p>To summarize, the hypotheses introduced above were the following:</p> <p></p> <ulist> <item> First, the content-subgoal condition, relative to the argument goal-only (control) condition, would result in higher scores on the following: number of attributes addressed, holistic text quality, number of complex propositions, and posttest learning about classification.</item> <p></p> <item> Second, the rhetorical-subgoal condition, relative to the argument goal-only condition, would result in higher scores on the following: variety of rhetorical moves, number of complex propositions, holistic text quality, and posttest learning about classification.</item> <p></p> <item> Third, the content-subgoal condition, relative to the rhetorical-subgoal condition, would result in lower cognitive load, which would mediate higher posttest scores on learning about classification.</item> </ulist> <hd id="AN0121040059-8">Method</hd> <p></p> <hd id="AN0121040059-9">Participants and curriculum context</hd> <p>The science topic for this study, vertebrate classification, was based on the biodiversity unit of the Grade 6, Ontario, Canada, curriculum in science. Multigrade grouping is common in this province, so Grade 5 and Grade 7 students in multigrade classes also participated in the study. The activities took place at the beginning of the unit so as to avoid the complicating effects of classroom instruction. A letter of information was distributed to parents and students; every student who consented and whose parent/guardian also consented was included in the study. Two students, who had consented but did not complete the writing activities due to prolonged absences from school, were deleted from the study.</p> <p>The final sample comprised 17 Grade 5 students, 36 Grade 6 students, and 20 Grade 7 students—38 girls and 35 boys. Age-appropriate placement is used in this province, so students ranged from 10 years, 1 month, for Grade 5 students to 13 years, 5 months, for Grade 7 students. The distribution of previous writing achievement as defined by most recent report grade was the following: 23 of the students achieved below-grade-level expectations of a 70% average; 41 were in the expected grade range of 71% to 79%; and 9 earned above the expected grade range.</p> <hd id="AN0121040059-10">Procedures and materials</hd> <p></p> <hd id="AN0121040059-11">Pretest quiz on the classification of vertebrates</hd> <p>Participants completed a multiple choice pretest of classification knowledge. The purpose of the pretest was to provide a covariate that would increase the power of the hypothesis tests concerning the effects of treatment conditions on posttest classification scores. Test items were based on previous research concerning classification (e.g., Burgoon & Duran, [<reflink idref="bib8" id="ref82">8</reflink>]; Prokop, Kubiatko, & Fančovičová, [<reflink idref="bib42" id="ref83">42</reflink>]; Yen, Yao, & Mintzes, [<reflink idref="bib56" id="ref84">56</reflink>]). Topics included the characteristics of each class of vertebrates, the classification of particular species, criteria for classification, and hierarchical classification. The test was made up of 15 items, each of which offered 4 options. Items were illustrated with color photographs of organisms.</p> <hd id="AN0121040059-12">First writing activity: Classifying dolphins</hd> <p>Approximately 5 days after pretesting, students completed the first writing activity. Each received a package of three brief documents titled "Mammals," "Fish," and "Dolphins." These documents did not tell students the correct classification of dolphins, but provided information that could allow students to validly infer the correct classification. The source materials used grade-appropriate language; the text was brief; and the materials were extensively illustrated with color photographs.</p> <p>Each participant was randomly assigned to one of three writing goal prompt conditions: (<reflink idref="bib1" id="ref85">1</reflink>) persuasive goal only (control) condition; (<reflink idref="bib2" id="ref86">2</reflink>) persuasive goal + content-subgoal prompt condition; or (<reflink idref="bib3" id="ref87">3</reflink>) persuasive goal + rhetorical-subgoal prompt condition. Students within each class were randomly assigned to conditions using digits ranging from 1 to 3, generated by an electronic random number generator. Consequently, each of the three conditions was approximately equally represented within each class.</p> <hd id="AN0121040059-13">Persuasive goal only (control) condition</hd> <p>One third of the students were randomly assigned the persuasive goal prompt only: "Is a dolphin a fish or a mammal? Give your opinion and persuade the reader."</p> <hd id="AN0121040059-14">Content-subgoal prompt condition</hd> <p>One third of the students were randomly assigned the persuasive goal prompt, plus the following content-subgoal prompts:</p> <p>"What is your opinion? To persuade the reader, please write about ...</p> <p></p> <ulist> <item> how it breathes</item> <p></p> <item> its skin or covering</item> <p></p> <item> warm-bloodedness or cold-bloodedness</item> <p></p> <item> how it is born</item> <p></p> <item> if it feeds its young or how it feeds its young</item> <p></p> <item> how it moves</item> </ulist> <p>The first five of these prompts provided the opportunity for students to construct valid reasons, e.g., the fact that dolphins are born "alive" rather than hatched from eggs supports the claim that they are mammals. The sixth prompt, "how it moves," provided the opportunity for students to identify the possible alternative claim that a dolphin is a fish and to write a counterargument.</p> <hd id="AN0121040059-15">Rhetorical-subgoal prompt condition</hd> <p>One third of the students were randomly assigned the persuasive goal prompt, plus the rhetorical-subgoal prompts, similar to Ferretti, Lewis, and Andrews-Weckerly ([<reflink idref="bib15" id="ref88">15</reflink>]):</p> <p>"What is your opinion? To persuade the reader, please remember to ...</p> <p></p> <ulist> <item> Give a reason for your opinion.</item> <p></p> <item> Give a second reason.</item> <p></p> <item> Give a third reason.</item> <p></p> <item> Give a fourth reason.</item> <p></p> <item> Give a fifth reason.</item> <p></p> <item> What opinion could someone have that is different from yours? What reason could they give for their opinion? Explain why their reason is not a good one."</item> </ulist> <p>It is notable that these rhetorical prompts could be answered by providing reasons referring to the same attributes listed in the content-subgoal prompts (e.g., respiration, skin covering).</p> <hd id="AN0121040059-16">Second writing activity: Classifying sea turtles</hd> <p>The second writing activity took place 3 days later; it was structured identically to the dolphin activity. Students wrote on the question, "Is a sea turtle a reptile or an amphibian?" Each student received information sheets about reptiles, amphibians, and sea turtles. Each also received the same type of writing prompt that he or she had received in the first activity—that is, (<reflink idref="bib1" id="ref89">1</reflink>) a persuasive goal prompt only, or (<reflink idref="bib2" id="ref90">2</reflink>) a persuasive goal prompt plus rhetorical-subgoal prompts, or (<reflink idref="bib3" id="ref91">3</reflink>) a persuasive goal prompt plus content-subgoal prompts.</p> <hd id="AN0121040059-17">Transfer writing activity: Classifying the penguin</hd> <p>The purpose of this activity was to assess students' transfer of rhetorical and conceptual elements from the first two writing activities to a novel topic. Five days after the second writing activity, students completed an activity on the question, "Is a penguin a bird or a mammal? What is your opinion? Persuade the reader." All students received information booklets about penguins, birds," and mammals. As with previous writing tasks, the information in the sources was sufficient to allow students to infer the correct classification of the animal, and to construct claim-evidence relationships, but the sources did not provide these relationships for the student. It is important to note that for the transfer task, all students received only the argument goal prompt with no rhetorical or content-subgoal prompts.</p> <hd id="AN0121040059-18">Ratings of cognitive load</hd> <p>Immediately after completing each writing activity, students rated their cognitive load. Previous research indicates that Likert-scale ratings of effort and perceived difficulty are valid measures of this construct (Brünken, Seufert, & Paas, [<reflink idref="bib7" id="ref92">7</reflink>]; DeLeeuw & Mayer, [<reflink idref="bib13" id="ref93">13</reflink>]). The first Likert scale asked, "How easy or difficult was this writing activity? Please circle a number." Numbers ranged from "1, Very, very easy" to "9, Very, very difficult." The second scale asked, "How much effort did you put into this writing activity? Please circle a number." The scale ranged from "1, Very, very little effort" to "9, Very, very much effort."</p> <hd id="AN0121040059-19">Posttest quiz on classification</hd> <p>Finally, students completed the posttest quiz on classification. The purpose of the posttest score was to allow a between-subjects test of the effect of treatment conditions. The content was based on previous research on classification, and included the following topics: classification of difficult animals; preference for taxonomic classification; preference for using defining characteristics to classify animals; and the hierarchical nature of classification (e.g., Burgoon & Duran, [<reflink idref="bib8" id="ref94">8</reflink>]; Prokop, Kubiatko, & Fančovičová, [<reflink idref="bib42" id="ref95">42</reflink>]; Yen, Yao, & Mintzes, [<reflink idref="bib56" id="ref96">56</reflink>]). The test included 20 items with a maximum total score of 31 points. All items were multiple choice questions; this format was selected to avoid the problem common in the writing-to-learn literature, of using posttests comprising writing activities similar in form to the experimental treatment (see Hebert, Gillespie, & Graham, [<reflink idref="bib22" id="ref97">22</reflink>], for a review). Each item presented 4 options. Items were illustrated with color photographs of vertebrates. Eighteen points were based on two-tiered multiple choice questions: The first tier of each question pair required students to make a judgment concerning classification; the second tier of each pair required students to select a reason to justify this judgment (Treagust, [<reflink idref="bib49" id="ref98">49</reflink>]; Yen, Yao, & Chiu, [<reflink idref="bib55" id="ref99">55</reflink>]). Students could receive a point for the first part of a two-tiered question, even if the second part was incorrect. The post-test score was not intended to be directly compared to the pretest score—for example, as a gain score; consequently, it was not necessary for the pretest and posttest to have the same total maximum score. Inter-item reliability was high, α =.83.</p> <hd id="AN0121040059-20">Qualitative coding</hd> <p>The condition of texts was masked prior to rating and analysis. All texts were rated and coded by two independent reviewers. Text quality was rated by an experienced elementary teacher unaware of the purpose of the study and a research officer experienced in writing research. Differences in ratings of continuous variables (e.g., text quality) were resolved by averaging; differences in categorical variables (e.g., type of rhetorical move) were resolved by a third rater who was a graduate research assistant.</p> <hd id="AN0121040059-21">Validity of classification</hd> <p>For each writing activity, this variable dichotomously coded whether the student classified the organism correctly.</p> <hd id="AN0121040059-22">Holistic text quality</hd> <p>To avoid biasing the assessment in favor of any writing condition (e.g., rhetorical subgoals versus content subgoals), a purely holistic rating of text quality was elicited using the question, "How good is this as a persuasive text?" Each rater sorted the texts into nine levels, ranging from 1 for <emph>well below average</emph>, to 5 for <emph>average</emph>, to 9 for <emph>well above average</emph>. Inter-rater reliability for each of the three texts was good (Spearman <emph>r</emph> =.75, <emph>p</emph> <.001).</p> <hd id="AN0121040059-23">Complexity of propositions</hd> <p>For each text, each of the attributes (e.g., respiration) listed in the hypotheses was coded with respect to the proposition constructed about it. Levels of complexity included the following: a <emph>complex proposition</emph> connected all three concepts (attribute, family, and class)—for example, <emph>a dolphin</emph> [family] <emph>is a mammal</emph> [class] <emph>because it breathes air using lungs</emph> [attribute]; a <emph>simple proposition</emph> connected two of the three concepts of attribute, family, and class; <emph>missing</emph> meant that no proposition was written about a given attribute. For each level of complexity (e.g., "simple"), the number of propositions in a given text was counted, producing a variable that ranged from zero to six propositions. Inter-rater reliability using Spearman correlation was excellent; across the three writing topics and three categories of complexity, it ranged from <emph>r</emph> =.72 to <emph>r</emph> =.86, with all <emph>p</emph> <.001.</p> <hd id="AN0121040059-24">Rhetorical moves</hd> <p>Each sentence in the text was classified by type of rhetorical move. The categories for rhetorical moves were the following: claim; reason; counterargument (against writer's claim); alternative claim; reason for alternative claim; rebuttal of alternative claim or rebuttal of reason for alternative claim; conclusion; other (i.e., not an argument move). As expected, reasons were relatively frequent and the number of reasons per text was normally distributed; inter-rater reliability was <emph>r</emph> =.74, with <emph>p</emph> <.001. All other rhetorical operations (e.g., claim, conclusion) were Poisson distributed, so they were coded dichotomously (present versus absent). The number of different <emph>types</emph> of argument moves was summed for each text, yielding a normally distributed variable, <emph>variety of rhetorical moves</emph>, ranging from 0 to 8.</p> <hd id="AN0121040059-25">Results</hd> <p></p> <hd id="AN0121040059-26">Data screening and assumption testing</hd> <p>Data were screened for consistency with the assumptions of multivariate analysis of covariance. Three variables were positively skewed, so each was normalized using a square root or square root (<emph>x</emph> + 1) transformation. Two participants constituted univariate outliers, and one participant constituted a multivariate outlier; MANCOVA is sensitive to outliers, so these cases were deleted from the analysis. Because the number of participants in each class was not evenly divisible by three, the number of participants was 24 in the general goal condition, 25 in the content-subgoal condition, and 24 in the rhetorical-subgoal condition. With these adjustments, all assumptions of MANCOVA were met. Additionally, five participants were missing one or more data points, most often a Likert scale rating of effort or difficulty. To avoid listwise deletion of these cases during MANCOVA, values were imputed using the SPSS missing data regression procedure. Imputed values constituted less than 1% of the total data.</p> <p>In the MANCOVA for each writing activity, the independent variable was writing-subgoal condition. The covariates were previous writing achievement and classification pretest score. The dependent variables were effort, perceived difficulty, text quality, variety of rhetorical moves in text, number of complex propositions, number of simple propositions, and number of reasons. In an initial analysis, grade level did not affect the dependent variables, so it was omitted from subsequent analyses. In the presentation below, the terms small, medium, and large are used to characterize magnitude of effects; these terms should be considered only approximations. Because classification accuracy was a dichotomous variable, it was analyzed using chi-squared tests.</p> <hd id="AN0121040059-27">Pretest</hd> <p>The three writing conditions did not differ significantly with respect to the classification pretest, <emph>F</emph> (<reflink idref="bib2" id="ref100">2</reflink>, 70) =.13; persuasive goal condition <emph>M</emph> = 11.58, <emph>SD</emph> = 2.19; content-subgoal condition <emph>M</emph> = 11.88, <emph>SD</emph> = 2.19; and rhetorical-subgoal condition <emph>M</emph> = 11.88, <emph>SD</emph> = 2.58. The groups also did not differ significantly with respect to grade level, <emph>F</emph> (<reflink idref="bib2" id="ref101">2</reflink>, 70) =.08 nor previous writing achievement <emph>F</emph> (<reflink idref="bib2" id="ref102">2</reflink>, 70) = 1.04.</p> <hd id="AN0121040059-28">Results of first writing activity: Classifying the dolphin</hd> <p>Classification accuracy showed a statistically significant difference across conditions (<emph>X</emph><sups>2</sups> (<reflink idref="bib2" id="ref103">2</reflink>, 73) = 9.00, <emph>p</emph> =.01); 23 students in the rhetorical condition, 18 students in the goal-only condition, and 19 students in the content-subgoal condition inferred the correct classification in their written text.</p> <p>Writing condition had a statistically significant, large effect on the combined dependent variable, (Wilks's Λ =.45, <emph>F</emph> (<reflink idref="bib14" id="ref104">14</reflink>, 124) = 4.34, <emph>p</emph> <.001, partial η<sups>2</sups> =.33). The classification pretest covariate did not account for significant variance in the combined dependent variable (Wilks's Λ =.81, <emph>F</emph> (<reflink idref="bib7" id="ref105">7</reflink>, 62) = 2.08, <emph>p</emph> =.06, partial η<sups>2</sups> =.19). The previous writing achievement covariate did not significantly predict the combined dependent variable (Wilks's Λ =.85, <emph>F</emph> (<reflink idref="bib7" id="ref106">7</reflink>, 62) = 1.59, <emph>p</emph> =.16, partial η<sups>2</sups> =.15). See Table 1 for the descriptive statistics and the results of Bonferroni post hoc comparisons among the writing conditions for the dolphin activity.</p> <p>Table 1. Dolphin writing activity, descriptive statistics, and Bonferroni comparisons*.</p> <p> <ephtml> <table><thead><tr><td /><td>Condition</td></tr><tr><td /><td>Goal only<italic>M</italic>(<italic>SD</italic>)</td><td>Content<italic>M</italic>(<italic>SD</italic>)</td><td>Rhetorical<italic>M</italic>(<italic>SD</italic>)</td></tr></thead><tbody><tr><td>Holistic text quality</td><td char=".">4.47</td><td char=".">(1.09)</td><td char=".">4.74</td><td char=".">(1.51)</td><td char=".">5.30</td><td char=".">(1.10)</td></tr><tr><td>Perceived difficulty</td><td char=".">4.04</td><td char=".">(1.46)</td><td char=".">4.08</td><td char=".">(1.47)</td><td char=".">3.33</td><td char=".">(1.99)</td></tr><tr><td>Effort</td><td char=".">6.75</td><td char=".">(1.39)</td><td char=".">7.36</td><td char=".">(1.41)</td><td char=".">6.50</td><td char=".">(2.00)</td></tr><tr><td>Reasons</td><td char=".">3.41</td><td char=".">(1.68)</td><td char=".">3.46</td><td char=".">(2.28)</td><td char=".">4.22</td><td char=".">(1.02)</td></tr><tr><td>Variety of rhetorical moves</td><td char=".">2.59</td><td char=".">(1.28)</td><td char=".">1.72</td><td char=".">(1.24)</td><td char=".">3.20</td><td char=".">(1.18)</td></tr><tr><td>Simple propositions</td><td char=".">1.68</td><td char=".">(1.50)</td><td char=".">4.16</td><td char=".">(1.77)</td><td char=".">2.22</td><td char=".">(1.34)</td></tr><tr><td>Complex propositions</td><td char=".">1.20</td><td char=".">(.96)</td><td char=".">.82</td><td char=".">(1.00)</td><td char=".">1.62</td><td char=".">(1.20)</td></tr><tr><td char="." /><td>Adjusted Mean (<italic>SE</italic>)</td></tr><tr><td>Holistic text quality</td><td char=".">4.47<sub>a</sub></td><td char=".">(.25)</td><td char=".">4.69<sub>ab</sub></td><td char=".">(.25)</td><td char=".">5.36<sub>b</sub></td><td char=".">(.26)</td></tr><tr><td>Perceived difficulty</td><td char=".">4.00</td><td char=".">(.33)</td><td char=".">4.06</td><td char=".">(.33)</td><td char=".">3.39</td><td char=".">(.33)</td></tr><tr><td>Effort</td><td char=".">6.74</td><td char=".">(.33)</td><td char=".">7.31</td><td char=".">(.33)</td><td char=".">6.56</td><td char=".">(.33)</td></tr><tr><td>Variety of rhetorical moves</td><td char=".">2.60<sub>b</sub></td><td char=".">(.25)</td><td char=".">1.68<sub>a</sub></td><td char=".">(.25)</td><td char=".">3.24<sub>b</sub></td><td char=".">(.25)</td></tr><tr><td>Reasons</td><td char=".">3.45</td><td char=".">(.35)</td><td char=".">3.46</td><td char=".">(.35)</td><td char=".">4.18</td><td char=".">(.35)</td></tr><tr><td>Simple propositions</td><td char=".">1.70<sub>a</sub></td><td char=".">(.32)</td><td char=".">4.19<sub>b</sub></td><td char=".">(.31)</td><td char=".">2.17<sub>a</sub></td><td char=".">(.32)</td></tr><tr><td><xref ref-type="fn" rid="t1fn0001" />Complex propositions</td><td char=".">.97<sub>ab</sub></td><td char=".">(.12)</td><td char=".">.65<sub>a</sub></td><td char=".">(.12)</td><td char=".">1.16<sub>b</sub></td><td char=".">(.12)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>10001 A square root transformation was applied prior to analysis.</item> <item>10002 Bonferroni-corrected multiple comparison; means that do not share a common subscript differ significantly, <emph>p</emph> <.05.</item> </ulist> <p>Writing condition had a statistically significant effect, which was small in magnitude, on the quality of written texts (<emph>F</emph> (<reflink idref="bib2" id="ref107">2</reflink>, 68) = 3.27, <emph>p</emph> =.04, partial η<sups>2</sups> = 09); quality was significantly higher in the rhetorical-subgoal condition than the goal-only condition. The perceived difficulty of the writing task did not differ significantly by writing condition (<emph>F</emph> (<reflink idref="bib2" id="ref108">2</reflink>, 68) = 1.25, <emph>p</emph> =.30, partial η<sups>2</sups> = 04). Reported effort also did not differ significantly across conditions (<emph>F</emph> (<reflink idref="bib2" id="ref109">2</reflink>, 68) = 1.40, <emph>p</emph> =.25, partial η<sups>2</sups> =.04).</p> <p>Writing condition had a statistically significant, large effect on the variety of rhetorical moves in text (<emph>F</emph> (<reflink idref="bib2" id="ref110">2</reflink>, 68) = 9.80, <emph>p</emph> <.001, partial η<sups>2</sups> =.22); it was significantly higher in the rhetorical-subgoal and goal-only conditions than the content-subgoal condition. The number of reasons did not differ significantly across conditions (<emph>F</emph> (<reflink idref="bib2" id="ref111">2</reflink>, 68) = 1.40, <emph>p</emph> =.25, partial η<sups>2</sups> =.04). Writing condition had a statistically significant, large effect on the number of simple propositions in text (<emph>F</emph> (<reflink idref="bib2" id="ref112">2</reflink>, 68) = 17.82, <emph>p</emph> <.001, partial η<sups>2</sups> =.34), such that it was higher in the content-subgoal condition than the other two conditions. The number of complex propositions showed a statistically significant, moderate difference across conditions (<emph>F</emph> (<reflink idref="bib2" id="ref113">2</reflink>, 68) = 4.65, <emph>p</emph> =.01, partial η<sups>2</sups> =.12), such that it was higher in the rhetorical-subgoal condition than the content-subgoal condition.</p> <hd id="AN0121040059-29">Results of the second writing activity: Classifying the sea turtle</hd> <p>Writing condition had a statistically significant effect on the number of students who classified the sea turtle correctly during writing (<emph>X</emph><sups>2</sups> (<reflink idref="bib2" id="ref114">2</reflink>, 73) = 4.96, <emph>p</emph> =.04); 21 students in the rhetorical-subgoal condition, 17 students in the goal-only condition, and 15 students in the content-subgoal condition made correct classifications.</p> <p>Writing condition had a statistically significant, large effect on the combined dependent variable (Wilks's Λ =.32, <emph>F</emph> (<reflink idref="bib14" id="ref115">14</reflink>, 124) = 4.91, <emph>p</emph> <.001, partial η<sups>2</sups> =.31). The pretest classification knowledge covariate did not account for significant variance in the combined dependent variable (Wilks's Λ =.95, <emph>F</emph> (<reflink idref="bib7" id="ref116">7</reflink>, 62) =.44, <emph>p</emph> =.87, partial η<sups>2</sups> =.05). The previous writing achievement covariate did not significantly predict the combined dependent variable (Wilks's Λ =.90, <emph>F</emph> (<reflink idref="bib9" id="ref117">9</reflink>, 57) =.98, <emph>p</emph> =.45, partial η<sups>2</sups> =.10). See Table 2 for descriptive statistics and Bonferroni comparisons for the turtle activity.</p> <p>Table 2. Turtle writing activity, descriptive statistics, and Bonferroni comparisons*.</p> <p> <ephtml> <table><thead><tr><td /><td>Condition</td></tr><tr><td /><td>Goal only<italic>M</italic>(<italic>SD</italic>)</td><td>Content<italic>M</italic>(<italic>SD</italic>)</td><td>Rhetorical<italic>M</italic>(<italic>SD</italic>)</td></tr></thead><tbody><tr><td>Holistic text quality</td><td char=".">4.77</td><td char=".">(1.32)</td><td char=".">4.44</td><td char=".">(1.23)</td><td char=".">5.38</td><td char=".">(1.31)</td></tr><tr><td>Perceived difficulty</td><td char=".">4.83</td><td char=".">(1.93)</td><td char=".">3.88</td><td char=".">(1.51)</td><td char=".">4.08</td><td char=".">(2.00)</td></tr><tr><td>Effort</td><td char=".">6.58</td><td char=".">(1.25)</td><td char=".">7.12</td><td char=".">(1.48)</td><td char=".">6.42</td><td char=".">(1.74)</td></tr><tr><td>Reasons</td><td char=".">3.46</td><td char=".">(1.16)</td><td char=".">3.16</td><td char=".">(2.52)</td><td char=".">3.67</td><td char=".">(1.32)</td></tr><tr><td>Variety of rhetorical moves</td><td char=".">2.82</td><td char=".">(.92)</td><td char=".">1.60</td><td char=".">(1.29)</td><td char=".">3.25</td><td char=".">(1.22)</td></tr><tr><td>Simple propositions</td><td char=".">1.14</td><td char=".">(1.19)</td><td char=".">3.94</td><td char=".">(2.06)</td><td char=".">1.08</td><td char=".">(1.33)</td></tr><tr><td>Complex propositions</td><td char=".">1.37</td><td char=".">(1.13)</td><td char=".">.72</td><td char=".">(1.13)</td><td char=".">1.54</td><td char=".">(1.50)</td></tr><tr><td char="." /><td>Adjusted Mean (<italic>SE</italic>)<xref ref-type="fn" rid="t2fn0002" /></td></tr><tr><td>Holistic text quality</td><td char=".">4.78<sub>ab</sub></td><td char=".">(.26)</td><td char=".">4.38<sub>a</sub></td><td char=".">(.26)</td><td char=".">5.43<sub>b</sub></td><td char=".">(.26)</td></tr><tr><td>Perceived difficulty</td><td char=".">4.83</td><td char=".">(.38)</td><td char=".">3.86</td><td char=".">(.37)</td><td char=".">4.11</td><td char=".">(.38)</td></tr><tr><td>Effort</td><td char=".">6.59</td><td char=".">(.31)</td><td char=".">7.08</td><td char=".">(.30)</td><td char=".">6.45</td><td char=".">(.31)</td></tr><tr><td>Variety of rhetorical moves</td><td char=".">2.83<sub>b</sub></td><td char=".">(.24)</td><td char=".">1.57<sub>a</sub></td><td char=".">(.23)</td><td char=".">3.28<sub>b</sub></td><td char=".">(.24)</td></tr><tr><td>Reasons</td><td char=".">3.47</td><td char=".">(.37)</td><td char=".">3.15</td><td char=".">(.36)</td><td char=".">3.68</td><td char=".">(.37)</td></tr><tr><td>Simple propositions</td><td char=".">1.11<sub>a</sub></td><td char=".">(.32)</td><td char=".">3.94<sub>b</sub></td><td char=".">(.32)</td><td char=".">1.11<sub>a</sub></td><td char=".">(.33)</td></tr><tr><td><xref ref-type="fn" rid="t2fn0001" />Complex propositions</td><td char=".">1.50<sub>b</sub></td><td char=".">(.08)</td><td char=".">1.25<sub>a</sub></td><td char=".">(.08)</td><td char=".">1.54<sub>b</sub></td><td char=".">(.08)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>20001 A square root (<emph>x</emph> + 1) transformation was applied prior to analysis.</item> <item>20002 Bonferroni-corrected multiple comparison; means that do not share a common subscript differ significantly, <emph>p</emph> <.05.</item> </ulist> <p>Writing condition had a statistically significant, medium-sized effect on the quality of written texts (<emph>F</emph> (<reflink idref="bib2" id="ref118">2</reflink>, 68) = 4.16, <emph>p</emph> =.02, partial η<sups>2</sups> =.11); texts in the rhetorical-subgoal condition were rated significantly higher than those in the content-subgoal condition. Writing condition did not significantly affect the perceived difficulty of the writing task, <emph>F</emph> (<reflink idref="bib2" id="ref119">2</reflink>, 68) = 1.83, <emph>p</emph> =.17, partial η<sups>2</sups> = 05, nor did it affect reported effort (<emph>F</emph> (<reflink idref="bib2" id="ref120">2</reflink>, 68) = 1.15, <emph>p</emph> =.32, partial η<sups>2</sups> =.03).</p> <p>Writing condition had a statistically significant, large effect on the variety of rhetorical moves (<emph>F</emph> (<reflink idref="bib2" id="ref121">2</reflink>, 68) = 13.97, <emph>p</emph> <.001, partial η<sups>2</sups> =.29), such that it was significantly higher in the rhetorical-subgoal condition and goal-only condition than in the content-subgoal condition. The number of reasons did not differ significantly by condition (<emph>F</emph> (<reflink idref="bib2" id="ref122">2</reflink>, 68) =.52, <emph>p</emph> =.60, partial η<sups>2</sups> =.02). Writing condition had a statistically significant, large effect on the number of simple propositions in text (<emph>F</emph> (<reflink idref="bib2" id="ref123">2</reflink>, 68) = 25.91, <emph>p</emph> <. 001, partial η<sups>2</sups> =.43); it was higher in the content-subgoal condition than the two other conditions. The number of complex propositions showed a statistically significant, small difference across conditions (<emph>F</emph> (<reflink idref="bib2" id="ref124">2</reflink>, 68) = 3.51, <emph>p</emph> =.04, partial η<sups>2</sups> =.09), such that it was significantly higher in the rhetorical-subgoal and goal-only conditions than the content-subgoal condition.</p> <hd id="AN0121040059-30">Results of transfer writing activity: Classifying the penguin</hd> <p>Recall that in the transfer activity, all students received the general persuasive goal prompt only. The number of valid classification inferences did not differ significantly across conditions (<emph>X</emph><sups>2</sups> (<reflink idref="bib2" id="ref125">2</reflink>, 73) = 1.32, <emph>p</emph> =.26); the correct classification was made by 22 students in the rhetorical-subgoal condition, 21 students in the goal-only condition, and 21 students in the content-subgoal condition. The MANCOVA showed that writing condition did not significantly affect the combined dependent variable (Wilks's Λ =.730, <emph>F</emph> (<reflink idref="bib14" id="ref126">14</reflink>, 124) = 1.51, <emph>p</emph> =.11, partial η<sups>2</sups> =.15). See Table 3 for descriptive statistics for the penguin activity. The pretest classification covariate did not significantly affect the combined dependent variable (Wilks's Λ =.974, <emph>F</emph> (<reflink idref="bib7" id="ref127">7</reflink>, 62) =.23, <emph>p</emph> =.98, partial η<sups>2</sups> =.03). The previous writing achievement covariate also did not significantly affect the combined dependent variable (Wilks's Λ =.915, <emph>F</emph> (<reflink idref="bib7" id="ref128">7</reflink>, 62) =.82, <emph>p</emph> =.57, partial η<sups>2</sups> =.09).</p> <p>Table 3. Transfer writing activity, descriptive statistics, and Bonferroni comparisons*.</p> <p> <ephtml> <table><thead><tr><td /><td>Subgoal condition</td></tr><tr><td /><td>Goal only <italic>M</italic>(<italic>SD</italic>)</td><td>Content <italic>M</italic>(<italic>SD</italic>)</td><td>Rhetorical <italic>M</italic>(<italic>SD</italic>)</td></tr></thead><tbody><tr><td>Holistic text quality</td><td char=".">4.83</td><td char=".">(1.09)</td><td char=".">4.59</td><td char=".">(1.29)</td><td char=".">4.80</td><td char=".">(1.33)</td></tr><tr><td>Perceived difficulty</td><td char=".">3.88</td><td char=".">(1.57)</td><td char=".">3.48</td><td char=".">(1.92)</td><td char=".">3.46</td><td char=".">(2.06)</td></tr><tr><td>Effort</td><td char=".">6.50</td><td char=".">(1.59)</td><td char=".">6.64</td><td char=".">(1.71)</td><td char=".">5.42</td><td char=".">(2.00)</td></tr><tr><td>Variety of rhetorical moves</td><td char=".">3.44</td><td char=".">(1.47)</td><td char=".">3.30</td><td char=".">(1.87)</td><td char=".">3.16</td><td char=".">(1.34)</td></tr><tr><td>Reasons</td><td char=".">2.79</td><td char=".">(1.14)</td><td char=".">2.46</td><td char=".">(1.00)</td><td char=".">2.91</td><td char=".">(.72)</td></tr><tr><td>Simple propositions</td><td char=".">.65</td><td char=".">(.98)</td><td char=".">1.59</td><td char=".">(1.36)</td><td char=".">1.14</td><td char=".">(1.04)</td></tr><tr><td>Complex propositions</td><td char=".">1.81</td><td char=".">(1.25)</td><td char=".">1.34</td><td char=".">(1.02)</td><td char=".">1.63</td><td char=".">(1.27)</td></tr><tr><td char="." /><td>Adjusted Mean (<italic>SE</italic>)</td></tr><tr><td>Holistic text quality</td><td char=".">4.84</td><td char=".">(.25)</td><td char=".">4.55</td><td char=".">(.25)</td><td char=".">4.85</td><td char=".">(.25)</td></tr><tr><td>Perceived difficulty</td><td char=".">3.87</td><td char=".">(.38)</td><td char=".">3.41</td><td char=".">(.37)</td><td char=".">3.54</td><td char=".">(.38)</td></tr><tr><td>Effort</td><td char=".">6.50<sub>ab</sub></td><td char=".">(.37)</td><td char=".">6.67<sub>b</sub></td><td char=".">(.36)</td><td char=".">5.39<sub>a</sub></td><td char=".">(.37)</td></tr><tr><td>Variety of rhetorical moves</td><td char=".">2.80</td><td char=".">(.20)</td><td char=".">2.43</td><td char=".">(.19)</td><td char=".">2.94</td><td char=".">(.20)</td></tr><tr><td>Reasons</td><td char=".">3.43</td><td char=".">(.33)</td><td char=".">3.26</td><td char=".">(.32)</td><td char=".">3.21</td><td char=".">(.33)</td></tr><tr><td><xref ref-type="fn" rid="t3fn0001" />Simple propositions</td><td char=".">1.24<sub>a</sub></td><td char=".">(.08)</td><td char=".">1.56<sub>b</sub></td><td char=".">(.08)</td><td char=".">1.42<sub>ab</sub></td><td char=".">(.08)</td></tr><tr><td>Complex propositions</td><td char=".">1.82</td><td char=".">(.24)</td><td char=".">1.30</td><td char=".">(.24)</td><td char=".">1.67</td><td char=".">(.24)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>30001 A square root (<emph>x</emph> +1) transformation was applied prior to analysis.</item> <item>30002 Bonferroni-corrected multiple comparison; means that do not share a common subscript differ significantly, <emph>p</emph> <.05.</item> </ulist> <hd id="AN0121040059-31">Posttest of classification</hd> <p>An overall <emph>F</emph> test did not show a statistically significant effect of condition on posttest classification score (<emph>F</emph> (<reflink idref="bib2" id="ref129">2</reflink>, 68) = 2.91, <emph>p</emph> =.06, partial <emph>η<sups>2</sups></emph> =.08). The planned comparison between the rhetorical condition and the control condition was statistically significant and small to medium in size (<emph>CE</emph> = 2.92, <emph>SE</emph> = 1.26, <emph>p</emph> =.02). The planned comparison between the content-subgoal condition and the goal-only condition was not statistically significant (CE = 2.15, <emph>SE</emph> = 1.24, <emph>p</emph> =.08). The pretest classification score strongly predicted the posttest classification score (<emph>F</emph> (<reflink idref="bib1" id="ref130">1</reflink>, 68) = 24.95, <emph>p</emph> <.001, partial <emph>η<sups>2</sups></emph> =.27). Prior writing achievement did not significantly affect classification post-test score (<emph>F</emph> (<reflink idref="bib1" id="ref131">1</reflink>, 68) = 1.63, <emph>p</emph> =. 20, partial <emph>η<sups>2</sups></emph> =.02).</p> <hd id="AN0121040059-32">Path analysis</hd> <p>The treatment conditions did not significantly affect the measures of cognitive load, ruling out cognitive load as a variable that could mediate the relationship between writing conditions and classification posttest knowledge. Consequently, a path analysis was conducted to investigate the possible mediating role of text variables indicative of argumentative reasoning. The treatment conditions were considered exogenous variables; the text measures were considered possibly mediating variables; and posttest classification score was considered the final criterion variable.</p> <p>Prior to the analysis, the writing-subgoal conditions were coded as indicator variables: A content-subgoal indicator variable was created in which the content-subgoal condition was coded as 1 and the goal-only (control) condition was coded as 0; and a rhetorical-subgoal indicator variable was created in which the rhetorical-subgoal condition was coded as 1 and the goal-only (control) condition was coded as 0.</p> <p>To keep the total number of parameters relative to the number of participants manageable, pairs of corresponding variables across the two writing activities that correlated (e.g., holistic quality of the turtle text and holistic quality of the dolphin text) were collapsed by being summed together. Then, a principle components analysis was used to reduce three of the mediating variables (variety of rhetorical moves, number of complex propositions, and holistic text quality) to a single variable. Only the first component had an Eigen value greater than 1; it accounted for 74% of the total variance. On this component, holistic argument quality had a loading of.90; variety of rhetorical moves had a loading of.86; and number of complex propositions had a loading of.82. This component was dubbed the "rhetorical quality factor." The other possible mediating variable to be tested for entry into the path analysis was the number of simple propositions. This reduced set of variables allowed a ratio of approximately 12 participants per parameter. This was considered acceptable, given that the parameter estimates were medium in size (Wolf, Harrington, Clark, & Miller, [<reflink idref="bib53" id="ref132">53</reflink>]).</p> <p>For the path analysis, relationships were selected almost entirely empirically. For the first regression, the criterion variable was posttest classification; all other variables in the model were evaluated using a backward elimination algorithm with a criterion of <emph>p</emph> <.05 for entry and <emph>p</emph> ≥.10 for removal. For each subsequent regression, an endogenous variable was set as the criterion variable and the remaining variables were evaluated as predictors.</p> <p>See Figure 1 for the resulting path diagram. All relationships were significant at <emph>p</emph> <.01. The magnitude of most relationships was medium in size; partial correlations ranged from small (β =.27) to large (β =.68). See Table 4 for a comparison of the observed and implied correlations. Of the 10 implied correlations, eight were within.05 of the observed correlation; the other two implied correlations were within 0.10 of the observed correlations.</p> <p>Graph: Figure 1.</p> <p>Table 4. Observed and reproduced correlations for path model.</p> <p> <ephtml> <table><thead><tr><td /><td>z1</td><td>    z2</td><td>    z3</td><td>   z4</td><td>   z5</td></tr></thead><tbody><tr><td>z1. Content-subgoal condition</td><td char="." /><td char=".">−.51</td><td char=".">.68</td><td char=".">−.38</td><td char=".">.10</td></tr><tr><td>z2. Rhetorical-subgoal condition</td><td char="." /><td char="." /><td char=".">−.30</td><td char=".">.38</td><td char=".">.16</td></tr><tr><td>z3. Simple propositions</td><td char="." /><td char="." /><td char="." /><td char=".">−.45</td><td char=".">.14</td></tr><tr><td>z4. Rhetorical text quality</td><td char="." /><td char="." /><td char="." /><td char="." /><td char=".">.35</td></tr><tr><td>z5. Classification posttest</td><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /></tr><tr><td>z1. Content-subgoal condition</td><td char="." /><td char=".">−.51</td><td char=".">.68</td><td char=".">−.39</td><td char=".">.05</td></tr><tr><td>z2. Rhetorical-subgoal condition</td><td char="." /><td char="." /><td char=".">−.35</td><td char=".">.40</td><td char=".">.09</td></tr><tr><td>z3. Simple propositions</td><td char="." /><td char="." /><td char="." /><td char=".">−.46</td><td char=".">.22</td></tr><tr><td>z4. Rhetorical text quality</td><td char="." /><td char="." /><td char="." /><td char="." /><td char=".">.35</td></tr><tr><td>z5. Classification posttest</td><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /></tr></tbody></table> </ephtml> </p> <p>For a summary of the causal effects, see Table 5. The model accounted for 23% of the variance in posttest classification score, which was the final endogenous variable. Although the amount of variance accounted for in posttest classification was modest, recall that the purpose of the path analysis was specifically to test whether argument text characteristics mediated the effect of the writing conditions on the posttest classification score. The determinants of posttest classification comprised rhetorical text quality (which was medium in magnitude) and simple propositions, rhetorical-subgoal condition, and content-subgoal condition (which were small in magnitude). The model accounted for 27% of the variance in rhetorical text quality; rhetorical-subgoal condition was a positive determinant of rhetorical text quality; the number of simple propositions and the content-subgoal condition were both negative determinants. The model accounted for 46% of the variance in the number of simple propositions; this was strongly determined by the content-subgoal condition.</p> <p>Table 5. Summary of causal effects for path model.</p> <p> <ephtml> <table><thead><tr><td /><td /><td>Model causal effects</td></tr><tr><td>Outcome</td><td>Determinant</td><td>Direct</td><td>Indirect</td><td>Total</td></tr></thead><tbody><tr><td>Posttest classification</td><td>Rhetorical text quality</td><td char=".">.51</td><td>–</td><td char=".">.51</td></tr><tr><td>(<italic>R</italic><sup>2</sup> =.23)</td><td>Simple propositions</td><td char=".">.36</td><td char=".">−.19</td><td char=".">.17</td></tr><tr><td char="." /><td>Rhetorical-subgoal condition</td><td>–</td><td char=".">.14</td><td char=".">.14</td></tr><tr><td char="." /><td>Content-subgoal condition</td><td>–</td><td char=".">.12</td><td char=".">.12</td></tr><tr><td>Rhetorical text quality</td><td>Simple propositions</td><td char=".">−.37</td><td>–</td><td char=".">−.37</td></tr><tr><td>(<italic>R</italic><sup>2</sup> =.27)</td><td>Rhetorical-subgoal condition</td><td char=".">.27</td><td>–</td><td char=".">.27</td></tr><tr><td char="." /><td>Content-subgoal condition</td><td>–</td><td char=".">.25</td><td char=".">−.25</td></tr><tr><td>Simple propositions</td><td>Rhetorical-subgoal condition</td><td>–</td><td>–</td><td char=".">.00</td></tr><tr><td>(<italic>R</italic><sup>2</sup> =.46)</td><td>Content-subgoal condition</td><td char=".">.68</td><td>–</td><td char=".">.68</td></tr></tbody></table> </ephtml> </p> <hd id="AN0121040059-33">Discussion</hd> <p>This was the first randomized experimental study to investigate the effects of content-subgoal prompts and rhetorical-subgoal prompts on text quality and learning in elementary science argument writing. In these writing-to-learn activities, the source documents did not inform students of the correct classification of each vertebrate. Rather, the sources provided information about classes, families, and attributes of vertebrates; students were required to infer the correct classification and to compose an original argument by constructing valid relationships among these concepts. The results included many statistically significant effects, most of which were replicated across the two writing-to-learn activities; however, several effects differed from the initial hypotheses.</p> <hd id="AN0121040059-34">How do content subgoals affect writing?</hd> <p>The first hypothesis stated that content-subgoal prompting, compared to goal-only prompting, would result in higher scores on the dependent variables. Descriptively, the average text in the content-subgoal condition included a claim and three reasons, with no dialectical argument moves. The main positive effect of content prompts was an increase in the number of simple propositions that related two science concepts to one another. This means that the content condition resulted in students addressing a broader set of the key concepts than the other two conditions. However, the content-subgoal condition also resulted in a significantly lower variety of rhetorical moves and fewer complex propositions that connected three or more science concepts than the control condition and the rhetorical-subgoal condition. The writers in the content-subgoal condition offered a similar number of total reasons to writers in the other conditions; however, they typically did this by stating one reason in a fully explicit way (e.g., "A turtle is reptile because it hatches from eggs on land"); they then expressed additional reasons using simple propositions, leaving their rhetorical status as reasons implicit (e.g., "Also, young turtles breathe with lungs"), omitting that this was a characteristic of reptiles. Unlike rhetorical-goal prompting, content goal prompting did not have a significant effect on learning.</p> <p>Why did content prompting produce this pattern of effects? The results are consistent with the theory that, during writing, content goals lead to the retrieval of relevant concepts from long-term memory or selection of relevant concepts from source documents (Bereiter & Scardamalia, [<reflink idref="bib5" id="ref133">5</reflink>]; Butcher & Kintsch, [<reflink idref="bib9" id="ref134">9</reflink>]). Retrieval made this information available for inferences and claim-evidence relationships. However, the content prompts appeared to have partially distracted students from the rhetorical aspects of composing the text. This was evident in the fact that students in this condition employed less variety of rhetorical moves than those in the control group.</p> <p>These results are somewhat different from previous studies of the effects of content prompts, which found that they significantly increased text quality relative to rhetorical goals (Butcher & Kintsch, [<reflink idref="bib9" id="ref135">9</reflink>]; McNeill & Krajcik, [<reflink idref="bib35" id="ref136">35</reflink>]). Each of these former studies differed from the present one in one of several ways: educational level of the participants, text genre, science content, or time frame. Perhaps the most notable difference is that both of the former studies contrasted relatively specific content goals versus more abstract rhetorical goals. In the present study, the rhetorical subgoals and content subgoals were parallel, in both number and specificity. It is not claimed here that these previous studies presented methodological problems or that this difference in findings demonstrates that rhetorical subgoals are more effective than content subgoals. Rather, we suggest that it is necessary to consider both the type of goal prompt and its level of specificity, in predicting its effects on writing and learning.</p> <hd id="AN0121040059-35">How did rhetorical goals affect writing?</hd> <p>The second hypothesis stated that the rhetorical-subgoal condition, relative to the control condition, would result in higher scores on the dependent measures; this was partially supported. Descriptively, the average student in the rhetorical-subgoal condition produced a text with three different kinds of rhetorical moves, including a claim, four reasons for the claim, and one dialectical move such as a counterargument. The rhetorical-subgoal condition, relative to the goal-only control condition, resulted in a greater frequency of valid classification inferences during the first two writing activities, higher text quality on the first writing activity, and higher scores on the classification posttest. Relative to the content-subgoal condition, the rhetorical condition resulted in a greater variety of rhetorical moves and a greater number of complex propositions, and on the second writing activity, it resulted in greater holistic text quality.</p> <p>To our knowledge, the present study is the first to show that rhetorical-subgoal prompts increase learning, relative to a goal prompt alone. The present results also confirm previous research in showing that rhetorical-subgoal prompting improves text quality, and extends this finding to science content (Ferretti, Lewis, & Andrews-Weckerly, [<reflink idref="bib15" id="ref137">15</reflink>]; Golanics & Nussbaum, [<reflink idref="bib17" id="ref138">17</reflink>]; Goldstein, Crowell, & Kuhn, [<reflink idref="bib18" id="ref139">18</reflink>]). Educationally, these results suggest that in classes where students have not yet mastered dialectical argumentation, argument tasks can be made more effective for learning by providing students with rhetorical-subgoal prompts.</p> <hd id="AN0121040059-36">What mediates learning during argument writing?</hd> <p>The third hypothesis stated that rhetorical and content goals would differ in their effects on cognitive load, which would in turn affect learning. This was based on the assumption that rhetorical subgoals elicit extraneous cognitive load by requiring writers to engage in an additional level of means-end analysis by translating rhetorical subgoals into content subgoals. However, the type of writing condition did not significantly affect either measure of cognitive load. Rather, participants rated all three conditions as moderate in difficulty; that is, the average ratings were within one standard deviation of the midpoint of the scale, which was labeled "medium." Participants identified their own level of effort, on average, approximately one standard deviation above the midpoint of the scale.</p> <p>Given that cognitive load theory has only recently been applied to writing, these results contain useful information. Previous research has shown that tasks that demand subgoaling (i.e., means-end analysis) produce higher cognitive load than those that do not require means-end analysis (Sweller, [<reflink idref="bib46" id="ref140">46</reflink>]). In this context, the present results suggest that the three conditions may not have differed in demand for means-end problem solving during writing. A plausible interpretation, consistent with the results of Butcher and Kintsch ([<reflink idref="bib9" id="ref141">9</reflink>], Experiment 2) is that because students are provided with rhetorical subgoals <emph>prior</emph> to writing, they may have selected relevant content during their initial reading of the sources, reducing the need to later derive content subgoals from rhetorical subgoals during the writing phase (cf. Klein, [<reflink idref="bib27" id="ref142">27</reflink>]).</p> <p>The path analysis suggests that rhetorical-subgoal prompts and content-subgoal prompts elicit two somewhat different paths to text quality and learning (Figure 1). One path is that which has been identified in a long-standing conception of writing to learn: The call for argumentation presents a rhetorical goal; writers pursue this goal by constructing complex propositions and this contributes to learning (Applebee, [<reflink idref="bib1" id="ref143">1</reflink>]; Bereiter & Scardamalia, [<reflink idref="bib5" id="ref144">5</reflink>]; see Klein, [<reflink idref="bib26" id="ref145">26</reflink>], for a review). This theory was supported by the path in which rhetorical-subgoal prompts increased the rhetorical quality of text. Recall that rhetorical text quality was a factor that was loaded with three variables: variety of rhetorical moves, holistic text quality, and number of complex propositions. This text factor can be considered a proxy for argumentative reasoning while writing. This factor in turn predicted posttest classification understanding.</p> <p>The second learning mechanism suggested by the path analysis could be called the content path: Content subgoals elicit attention to relevant concepts, making them available for inferencing and construction of propositions, which contributes to learning (Figure 1). This was apparent in the path in which content-subgoal prompts increased the number of simple (two-concept) propositions, which in turn accounted for a unique variance in learning. This content-subgoal path to learning is largely distinct from the rhetorical-subgoal path discussed in the previous paragraph, not only because of the difference in the conditions that elicited it, but also because of the negative correlation that cross-linked these paths: the number of simple propositions predicted negative variance in the rhetorical quality factor.</p> <hd id="AN0121040059-37">Limitations and further research</hd> <p>A result that requires further comment is performance on the transfer (penguin) activity. Although the-subgoal conditions differed in their effects on the first two writing activities, they did not differ in their effects on the transfer writing activity. These results are similar to those obtained by Schworm and Renkl ([<reflink idref="bib45" id="ref146">45</reflink>]). These researchers found that in learning about argumentation, students who were prompted to self-explain the learning domain (argumentation principles) learned more about argumentation and transferred their argumentation skills; students who were prompted to self-explain the exemplifying domain (conceptual content) of the argument writing task did not. The students in the present study carried out a writing task that was more similar to self-explaining the exemplifying domain (classification) rather than the learning domain (argumentation) itself. This suggests that students might have performed better on argumentation transfer tasks if they were presented with reflective questions about argumentation principles.</p> <p>Perhaps the most important limitation of this study was the focus on one genre: argumentation. Argumentation, by its nature, foregrounds rhetorical goals. It would be valuable to replicate this research in a genre such as a causal explanation or an informational report, in which the top-level goal of the text foregrounds conceptual rather than rhetorical demands. A second limitation of this study is that it focused on one topic, taxonomic classification; further research should investigate whether similar results occur with other science topics and with topics in other domains, such as history.</p> <p>A third limitation is that the measures of cognitive load were based on two Likert scales: effort and perceived difficulty. Although commonly used and well-validated as measures of cognitive load in general, it remains unclear whether these scales measure different components of total cognitive load and, if so, which scales measure which components (DeLeeuw & Mayer, [<reflink idref="bib13" id="ref147">13</reflink>]; Leppink, Paas, Van der Vleuten, Van Gog, & Van Merriënboer, [<reflink idref="bib33" id="ref148">33</reflink>]). Finally, the number of participants (<emph>N</emph> = 73) was modest for a path analysis. The size of the parameters was medium, increasing the reliability of the analysis; however, it would be preferable to have a larger sample with multiple measures per variable (Wolf, Harrington, Clark, & Miller, [<reflink idref="bib53" id="ref149">53</reflink>]).</p> <hd id="AN0121040059-38">Conclusions</hd> <p>This is one of the first studies of the effects of rhetorical- and content-argument-subgoal prompts on writing to learn, so it should be considered exploratory. The results suggest that argument writing in science can be supported by rhetorical or content subgoals and that these subgoals elicit different paths to text quality and learning. Pending further investigation, these results suggest the following educational implications:</p> <p></p> <ulist> <item> Teachers could consider assigning brief argument-writing activities to help students learn about classification.</item> <p></p> <item> Teachers could consider supporting content area argument writing with rhetorical subgoals in order to increase text quality and learning.</item> <p></p> <item> Teachers could consider assigning content subgoals to elicit engagement with a range of concepts.</item> </ulist> <hd id="AN0121040059-39">Acknowledgments</hd> <p>The researchers wish to thank the research officers, administrators, teachers, and students of the Thames Valley District School Board and the London Catholic District School Board for their help with this project.</p> <hd id="AN0121040059-40">Funding</hd> <p>This research was supported by the Social Science and Humanities Research Council of Canada, Insight Grant 435-2012-0687.</p> <ref id="AN0121040059-41"> <title> References </title> <blist> <bibl id="bib1" idref="ref85" type="bt">1</bibl> <bibtext> Applebee, A. 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  Data: The Effects of Rhetorical and Content Subgoals on Writing and Learning
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  Data: 10.1080/00220973.2016.1143795
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  Data: Argument writing is challenging for elementary students. Previous experimental research has focused on scaffolding rhetorical goals, leaving content goals relatively unexplored. In a randomized experiment, 73 students in grades 5, 6, and 7 wrote persuasive texts about difficult-to-classify vertebrates. Each student received one of three sets of writing prompts: a persuasive goal only (control); a persuasive goal + rhetorical-subgoal prompts; or a persuasive goal + content-subgoal prompts. Rhetorical subgoals increased text quality, variety of rhetorical moves, number of complex propositions, and classification knowledge. Content subgoals increased the number of simple propositions in text. A path analysis indicated that content-subgoal prompts and rhetorical-subgoal prompts elicited different paths to writing and learning.
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      – TitleFull: The Effects of Rhetorical and Content Subgoals on Writing and Learning
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            – Type: issn-print
              Value: 0022-0973
          Numbering:
            – Type: volume
              Value: 85
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Experimental Education
              Type: main
ResultId 1