This section will report the amount of reader feedback that writers incorporated into their
of this analysis, I reviewed peer response transcripts to identify all specific, revision-oriented comments, and then examined the twenty-six second drafts to determine if these comments were implemented. Only revision-oriented comments where it seemed possible to identify
implementation in the second draft were considered (a similar procedure was used in Liu and Sadler, 2003).
For example, during their second peer response session, Dan (the reader) had the following feedback for Alex, his partner: “I think summary, you need, um, to introduce the article, like the title of the article or the author”. This comment is specific and revision-oriented. I read Alex’s second draft to determine whether or not the comment was implemented during revisions; in the above example I looked for an added sentence or phrase mentioning the title and author of the article he was summarizing. The revised language was recorded on the form (this particular comment was considered to be implemented because Alex did add an attribution sentence in his second draft). Finally, stimulated recall transcripts were reviewed for any writer comments that may help explain why and how the writer incorporated that feedback item (or why the writer chose not to).
Not all revision-oriented comments are captured in this analysis, because some comments were too vague, or too general, for their implementation to be directly observable in the second draft. For example, during the same peer response session cited above, when Alex was reading Dan’s paper, he suggested that Dan should “talk about some vocabulary things the author writes”. Because Dan didn’t ask Alex to clarify what he meant by “vocabulary things”, we can’t know whether or how this comment played into Alex’s revision process. Other comments were too general to be examined in this phase of analysis. Looking again at Dan and Alex, when Dan was reading Alex’s paper, he suggested that Alex use “higher vocabularies” in his revision. Later
in the session, the two decide that using higher vocabularies means avoiding “simple, overused words”. While this suggestion is clearer than the one given at the beginning of this paragraph, it is still not appropriate to examine in this analysis because it would be difficult to identify its implementation. It would be a stretch for me to assume that Alex was trying to implement “higher vocabulary” every time he made a word substitution, unless this were something that Alex commented on in stimulated recall. Changes like this are better identified in the next section, where I discuss the amount and types of revisions that writers make.
For the reasons outlined above, I limited the analysis of comments during this phase of analysis to revision-oriented comments that are specific, and to those where the resulting revision could be observed in the second draft. Table 7-3 displays the results of this calculation, reporting the total number of specific comments, and the number and percent of those comments that were implemented in the second draft, by pattern of interaction role of the writer. The writer roles are: collaborative, novice, dominant, and passive. There is no expert group because in the
expert/novice pattern, the writers always assumed a novice role (and readers were experts). Likewise, in the dominant/passive pattern, the writer always assumed the passive role (and the readers were dominant). The numbers reported are an average of all peer response transcripts and corresponding second drafts that occurred for each role. For example, the collaborative pattern occurred ten times, and the average number of specific revision-oriented comments received was 5.1. Across all collaborative writers, an average of 3.9 (76.5%) were implemented.
Table 7.3 Implementation of specific, revision oriented comments per paper, by writer role
Writer role Comments Received Comments
Implemented Percent Implemented Collaborative (10) Mean SD 5.1 2.4 3.9 2.1 76.5 % Dominant (4) Mean SD 6 2.2 1.2 1.3 20 % Passive (5) Mean SD 12 6.8 7.8 2.1 64.6 % Novice (7) Mean SD 12.4 3.4 10.6 3.3 85.1 %
Examining Table 7-3 yields a couple of important observations about the number of comments that students in different roles receive, and the percentage of those that they
incorporate in their papers. First, when examining the average number of comments received per paper within each pattern, it appears that results correlate with the relative amount of equality in each pattern. That is, in patterns with relatively low equality (dominant/passive and
expert/novice) writers receive more comments (12 average per paper, and 12.4 average per paper, respectively). In collaborative and dominant/dominant patterns, on the other hand, where equality is higher, writers receive fewer comments (5.1 average per paper, and 6 average per paper, respectively). Second, the concept of mutuality may be related to the percentage of comments that writers implement. In patterns with high mutuality (collaborative and expert/novice), writers incorporate a higher percentage of feedback (76.5 percent and 85.1 percent, respectively) than in lower mutuality patterns (passive writers use 64.6 percent of the feedback they receive, and dominant writers, only 20 percent). Finally, while the amount of comments that collaborative and passive writers incorporate in their papers is generally in line
with previous research, novice writers incorporate more than students in other studies, and dominant writers, less. The next two sections will discuss each of these trends in turn, drawing on data from peer response and stimulated recall transcripts.