College Composition Weekly: Summaries of research for college writing professionals

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Abba et al. Students’ Metaknowledge about Writing. J of Writing Res., 2018. Posted 09/28/2018.

Abba, Katherine A., Shuai (Steven) Zhang, and R. Malatesha Joshi. “Community College Writers’ Metaknowledge of Effective Writing.” Journal of Writing Research 10.1 (2018): 85-105. Web. 19 Sept. 2018.

Katherine A. Abba, Shuai (Steven) Zhang, and R. Malatesha Joshi report on a study of students’ metaknowledge about effective writing. They recruited 249 community-college students taking courses in Child Development and Teacher Education at an institution in the southwestern U.S. (89).

All students provided data for the first research question, “What is community-college students’ metaknowledge regarding effective writing?” The researchers used data only from students whose first language was English for their second and third research questions, which investigated “common patterns of metaknowledge” and whether classifying students’ responses into different groups would reveal correlations between the focus of the metaknowledge and the quality of the students’ writing. The authors state that limiting analysis to this subgroup would eliminate the confounding effect of language interference (89).

Abba et al. define metaknowledge as “awareness of one’s cognitive processes, such as prioritizing and executing tasks” (86), and explore extensive research dating to the 1970s that explores how this concept has been articulated and developed. They state that the literature supports the conclusion that “college students’ metacognitive knowledge, particularly substantive procedures, as well as their beliefs about writing, have distinctly impacted their writing” (88).

The authors argue that their study is one of few to focus on community college students; further, it addresses the impact of metaknowledge on the quality of student writing samples via the “Coh-Metrix” analysis tool (89).

Students participating in the study were provided with writing prompts at the start of the semester during an in-class, one-hour session. In addition to completing the samples, students filled out a short biographical survey and responded to two open-ended questions:

What do effective writers do when they write?

Suppose you were the teacher of this class today and a student asked you “What is effective writing?” What would you tell that student about effective writing? (90)

Student responses were coded in terms of “idea units which are specific unique ideas within each student’s response” (90). The authors give examples of how units were recognized and selected. Abba et al. divided the data into “Procedural Knowledge,” or “the knowledge necessary to carry out the procedure or process of writing,” and “Declarative Knowledge,” or statements about “the characteristics of effective writing” (89). Within the categories, responses were coded as addressing “substantive procedures” having to do with the process itself and “production procedures,” relating to the “form of writing,” e.g., spelling and grammar (89).

Analysis for the first research question regarding general knowledge in the full cohort revealed that most responses about Procedural Knowledge addressed “substantive” rather than “production” issues (98). Students’ Procedural Knowledge focused on “Writing/Drafting,” with “Goal Setting/Planning” in second place (93, 98). Frequencies indicated that while revision was “somewhat important,” it was not as central to students’ knowledge as indicated in scholarship on the writing process such as that of John Hayes and Linda Flower and M. Scardamalia and C. Bereiter (96).

Analysis of Declarative Knowledge for the full-cohort question showed that students saw “Clarity and Focus” and “Audience” as important characteristics of effective writing (98). Grammar and Spelling, the “production” features, were more important than in Procedural Knowledge. The authors posit that students were drawing on their awareness of the importance of a polished finished product for grading (98). Overall, data for the first research question matched that of previous scholarship on students’ metaknowledge of effective writing, which shows some concern with the finished product and a possibly “insufficient” focus on revision (98).

To address the second and third questions, about “common patterns” in student knowledge and the impact of a particular focus of knowledge on writing performance, students whose first language was English were divided into three “classes” in both Procedural and Declarative Knowledge based on their responses. Classes in Procedural Knowledge were a “Writing/Drafting oriented group,” a “Purpose-oriented group,” and the largest, a “Plan and Review oriented group” (99). Responses regarding Declarative Knowledge resulted in a “Plan and Review” group, a “Time and Clarity oriented group,” and the largest, an “Audience oriented group.” One hundred twenty-three of the 146 students in the cohort belonged to this group. The authors note the importance of attention to audience in the scholarship and the assertion that this focus typifies “older, more experienced writers” (99).

The final question about the impact of metaknowledge on writing quality was addressed through the Coh-Metrix “online automated writing evaluation tool” that assessed variables such as “referential cohesion, lexical diversity, syntactic complexity and pattern density” (100). In addition, Abba et al. used a method designed by A. Bolck, M. A. Croon, and J. A. Hagenaars (“BCH”) to investigate relationships between class membership and writing features (96).

These analyses revealed “no relationship . . . between their patterns knowledge and the chosen Coh-Metrix variables commonly associated with effective writing” (100). The “BCH” analysis revealed only two significant associations among the 15 variables examined (96).

The authors propose that their findings did not align with prior research suggesting the importance of metacognitive knowledge because their methodology did not use human raters and did not factor in student beliefs about writing or questions addressing why they responded as they did. Moreover, the authors state that the open-ended questions allowed more varied responses than did responses to “pre-established inventor[ies]” (100). They maintain that their methods “controlled the measurement errors” better than often-used regression studies (100).

Abba et al. recommend more research with more varied cohorts and collection of interview data that could shed more light on students’ reasons for their responses (100-101). Such data, they indicate, will allow conclusions about how students’ beliefs about writing, such as “whether an ability can be improved,” affect the results (101). Instructors, in their view, can more explicitly address awareness of strategies and effective practices and can use discussion of metaknowledge to correct “misconceptions or misuse of metacognitive strategies” (101):

The challenge for instructors is to ascertain whether students’ metaknowledge about effective writing is accurate and support students as they transfer effective writing metaknowledge to their written work. (101)


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Kellogg et al. Verbal Working Memory and Sentence Composition. J of Writing Research, Feb. 2016. Posted 04/24/2016.

Kellogg, Ronald T., Casey E. Turner, Alison P. Whiteford, and Andrew Mertens. “The Role of Working Memory in Planning and Generating Written Sentences.” The Journal of Writing Research 7.3 (2016): 397-416. Web. 04 Apr. 2016.

Kellogg et al. conduct an experiment designed to shed light on how working memory (WM) relates to the sequence of the processes that go into generating written sentences. They draw on a body of research like that of Linda Flower and John Hayes that posits a conceptual “planning” phase that is largely nonverbal and a phase of grammatical and orthographical translation that leads to writing or typing (398).

The models propose that the planning stage draws on a visual component when the information to be translated involves concrete nouns that evoke images (399). The researchers hypothesized that a visual working-memory load (that is, a non-verbal load) during the writing task would interact with the planning, or non-verbal, stage, while imposing a verbal working-memory load would impact transcription of the written sentences during the phase when grammatical processing was taking place.

To test these hypotheses, Kellogg et al. provided participants with two different kinds of prompts and asked for two different kinds of sentences using these prompts. The prompts were paired words to be used in sentences. One set of pairs involved words that were semantically related, such as “door” and “knob.” The other set provided words that were not related, such as “ice” and “jail.” All word pairs were concrete and taken from a scale tested to calculate how easily they would be recognized (401).

The rationale for these choices was that more planning was needed to link unrelated word pairs in a sentence, so that the load on visual memory would also increase (399). But because the models specified that planning takes place before “grammatical encoding,” the kind of word pair required should have no effect on this latter process (400).

To investigate the relationship of verbal memory to the sentence-generating process, the researchers imposed another manipulation by asking half of the subjects to compose active-voice sentences, while the other half composed passive-voice sentences. In this case, the rationale was research showing that passive-voice constructions are more complex than active and thus should burden “verbal working memory” but not visual (400). Subjects were screened for their ability to produce the kinds of sentences required as instructed in the prompts (403).

Thus the protocol required subjects to produce sentences, either in the passive or active voice as instructed, in three conditions: while being asked to perform a “concurrent” visual working-memory task, a concurrent verbal working-memory task, or no concurrent task (401). The visual concurrent task involved studying and learning a set of images chosen from “the SPSS Marker Set in Microsoft Word” that were “designed to be not readily named” (401). In contrast, the verbal concurrent task was composed of nine digits that “could be coded verbally” (401). In both cases, after completing the required sentence, the participants were asked to determine whether a set of symbols or digits matched the ones they had previously seen (402-03).

The level of accuracy in this ability to match the symbols or digits displayed after the writing task with those seen previously constituted the main data for the study (409). The researchers wanted to test the hypothesis that impeding visual working memory would not interact with the verbal process of grammatical encoding, but that impeding verbal working memory would do so (399).

Additional data came from measuring factors such as the length of the sentences produced, the number of words produced per second in each trial, the time involved in producing the sentences, and the time before typing began. These factors were controlled for in the interpretation of the major findings.

The authors’ predictions, therefore, were that factors that made planning harder, such as efforts to work with unrelated nouns, would show up in less accurate results for the visual working-memory task, the symbols, while factors that impeded grammatical encoding, such as writing passive-voice sentences, would manifest themselves in less accuracy in recalling the verbal working-memory task components, the digits.

Unexpectedly, they found that even though, as predicted, passive voice sentences took longer to write, required more words, and resulted in fewer words per second, the type of sentence made “no reliable difference” on the verbal concurrent task (the digits): “if anything, actives produced more interference than passives” (410). They found also that, contrary to expectations, related word pairs “most disrupted the verbal WM task” (410). Thus, the operations assumed to be simplest required the most verbal working-memory effort, and factors that were expected to affect visual working memory because they were presumably involved in planning did not produce the hypothesized interference with the visual task (409-10).

That the presumably simpler effort involved in producing an active-voice sentence using a related pair of words demanded more verbal working memory led the researchers to consult the work of M. Fayol, who proposed that the “Kellogg (1996) model” may fail to take into account “an understanding of the temporal dynamics” involved in writing sentences (411). To explain their findings in the current study, Kellogg et al. posited that the conceptual work of planning for the simpler active-voice/related-pair resulted in “a single chunk that was then immediately cascaded forward to grammatical encoding” (411). In contrast, they suggest, the more difficult planning for a sentence using the unrelated pair occurred incrementally, possibly in parallel with grammatical encoding or during pauses, for example, after typing the first of the words. Thus, the grammatical encoding process that would have shown up as a demand on verbal working memory was broken up into “piecemeal” activities by planning phases rather than implemented all at once (411-12). Such intermittent planning/encoding has been demonstrated in studies of speech and typing (412). In short, easier planning can result in more pressure on verbal working memory.

The authors conclude that “the role of WM in written sentence production is markedly more complex than previously postulated” (414).

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Rice, Jenny. Para-Expertise in Writing Classrooms. CE, Nov. 2015. Posted 12/07/2015.

Rice, Jenny. “Para-Expertise, Tacit Knowledge, and Writing Problems.” College English 78.2 (2015): 117-38. Print.

Jenny Rice examines how views of expertise in rhetoric and composition shape writing instruction. She argues for replacing the definition of non-expertise as a lack of knowledge with expanded approaches to expertise open to what Michael Polanyi has called “tacit knowledge” (125). Rice proposes a new category of knowledge, “para-expertise,” that draws on tacit knowledge to enable students and other non-experts to do activities related to expertise.

Rice cites a number of approaches to expertise in rhet/comp’s disciplinary considerations. Among them is the idea that the field has content that only qualified individuals can impart (120). Further, she sees expectations in writing-across-the-curriculum and writing-in-the-disciplines, as well as the view that composition courses should inculcate students in “expert [reading and writing] practice[s]” (121), as indications of the rhetorical presence notions of expertise acquire in the field (120-21).

She opposes the idea of novice practice as a deficiency with other attitudes toward expertise. Within the field of composition studies, she points to the work of Linda Flower and John Hayes. These scholars, she writes, found that the expertise of good writers consisted not of specific knowledge but rather of the ability to pose more complex problems for themselves as communicators. Whereas weaker writers “often flatline around fulfilling the details of the prompt, including word count and other conventional details,” expert writers “use the writing prompt as a way to articulate and define their own understanding of the rhetorical situation to which they are responding” (121).

This discussion leads Rice to a view of expertise as meaningful problem-posing, an activity rather than a body of knowledge. In this view, students can do the work of expertise even when they have no field-specific knowledge (122). Understanding expertise in this way leads Rice to explore categories of expertise as laid out in “the interdisciplinary field of Studies of Expertise and Experience (SEE)” (123). Scholars in this field distinguish between “contributory experts” who “have the ability to do things within the domain of their expertise” (Harry Collins and Robert Evans, qtd. in Rice 123; emphasis original); and “interactional experts,” who may not be able to actively produce within the field but who are “immersed in the language of that particular domain” (123). Rice provides the example of artists and art critics (123).

Rice emphasizes the importance of interactional expertise by noting that not all contributory experts communicate easily with each other and thus require interactional experts to “bridge the gulf” between discourse communities addressing a shared problem (124). She provides the example of “organic farmers and agricultural scholars” who function within separate expert domains yet need others to “translate” across these domains (124-25).

But Rice feels these definitions need to be augmented with another category to encompass people like students who lack the domain-specific knowledge to be contributory or interactional experts. She proposes the category “para-expertise,” in which para takes on its “older etymology” as “alongside (touching the side of) different forms of expertise” (119).

In Rice’s view, the tacit knowledge that fuels para-expertise, while usually discounted in formal contexts, arises from “embodied knowledge” gleaned from everyday living in what Debra Hawhee has called “rhetoric’s sensorium” (cited in Rice 126). In Rice’s words, this sensorium may be defined as “the participatory dimension of communication that falls outside of simple articulation without falling outside the realm of understanding” (126). She gives the example of not being able to articulate the cues that, when implicitly sensed, result in her clear knowledge that she is hearing her mother’s voice on the phone (125)

Rice’s extended example of the work of para-expertise revolves around students’ sense of the effects of campus architecture on their moods and function. Interviews with “hundreds of college students” at “four different university campuses” regarding their responses to “urban legends” about dorms and other buildings being like prisons lead Rice to argue that the students were displaying felt knowledge of the bodily and psychological effects of window and hallway dimensions even though they did not have the expert disciplinary language to convert their sensed awareness into technical architectural principles (127-31). In particular, Rice states, the students drew a sense of a problem to be addressed from their tacit or para knowledge and thus were embarking on “the activity of expertise” (131).

In Rice’s discussion, para-expertise can productively engage with other forms of expertise through the formation of “strategic expertise alliances” (131). By itself para-expertise cannot resolve a problem, but those whose tacit knowledge has led them to identify the problem can begin to address it via coalitions with those with the specific disciplinary tools to do so. As a classroom example, she explains that students on her campus had become concerned about intentions to outsource food options, thus endangering connections with local providers and reducing choices. Lacking the vocabulary to present their concerns to administrators, a group of students and faculty joined with local community organizations that were able to provide specific information and guidance in constructing arguments (132-33).

Rice’s own writing students, participating in this campus issue, were asked to gather oral histories from members of a nearby farmers’ market. The students, however, felt “intimidated and out of place” during their visits to the farmers’ market (136), partly because, as students from other areas, they had seldom had any reason to visit the market. Rice considers this tacit response to the market the opening of a problem to be addressed: “How can a community farmers market reach students who only temporarily reside in that community?” (136; emphasis original).

Rice writes:

[T]he solution calls for greater expertise than first-year students possess. Rather than asking students to (artificially) adopt the role of expertise and pose a solution, however, we turned to a discussion of expert alliances. Who were the “pivot points” in this problem? Who were the contributory experts, and who had the skills of interactional expertise? (136)

Ultimately, alliances resulting from this discussion led to the creation of a branch of the farmers’ market on campus (136).

Rice argues that this approach to expertise highlights its nature as a collaborative effort across different kinds of knowledge and activities (134). It de-emphasizes the “terribly discouraging” idea that “discovery” is the path to expertise and replaces that “myth” with an awareness that “invention and creation” and how “[e]xperts pose problems” are the keys to expert action (122; emphasis original). It also helps students understand the different kinds of expertise and how their own tacit knowledge can become part of effective action (135).