To explore whether students focused on the meaning of the moves in their abstracts, their drafts before and after the use of the AWE tool were analyzed, and the addition and deletion of moves. Also, an analysis of the transcripts of the semi-structured interviews was conducted to answer this question.
By comparing their drafts before and after using the AWE tool, it was found that the majority of students (11, 84.6%) tended to add words and sentences in their final drafts. According to Table 3.31, students wrote 105.6 words on average in their first
draft, but increased to 171.8 words in their final draft. The sentence count on average was from 6.2 to 8.6.
Table 3.31
Descriptive statistics for word count and sentence count in students’ first and final drafts (N = 13)
Total Mean Median Min Max SD
Word count First Draft 1373.0 105.6 102.0 12.0 181.0 54.0 Final Draft 2234.0 171.8 160.0 109.0 276.0 52.1 Sentence count First Draft 80.0 6.2 5.0 2.0 13.0 3.6 Final Draft 112.0 8.6 9.0 4.0 15.0 3.6
Figure 3.18 visually presents the sentence count, and the sequence of the moves in the drafts before and after using the AWE tool. These moves were identified by me. Four moves were color-coded: Introduction is green, Methodology is blue, Results is red, and Discussion/Conclusion is yellow. The gray color represents a sentence that did not fit any moves and the color white means no sentence exists in students’ drafts. In general, there were more green and blue than pink and yellow. Also, most of the final drafts had a higher sentence count than their first drafts. It was also found that ten students (79.6%) improved their abstract structures by adding more sentences or revising their sentences for the moves they had not covered in their first draft.
Table 3.32 provides information about the sentence count of moves in their drafts before and after using the AWE tool. It was found that only the numbers of the
Sentence number
Student Move count Draft 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
S1* 2 1 1 1 2 2 4 2 1 1 2 2 2 2 3 4 S2* 2 1 5 5 5 1 2 1 5 5 5 3 2 5 5 5 1 2 1 5 5 5 1 1 1 2 4 4 S3 3 1 1 1 1 1 2 2 2 2 3 3 2 1 1 1 1 1 1 2 3 S4 3 1 1 2 3 2 3 2 1 2 3 3 S5* 2 1 1 1 2 2 2 2 4 2 1 1 1 1 1 1 1 1 1 1 2 2 3 4 S7* 3 1 1 2 2 2 3 2 2 4 2 1 1 2 2 2 3 3 4 2 S8* 3 1 1 1 1 1 1 1 1 1 1 2 2 3 4 2 1 2 1 1 1 1 1 1 1 2 3 4 S9* 1 1 1 5 4 2 1 1 1 2 2 2 3 3 4 S10 2 1 1 1 1 1 5 2 2 2 2 2 5 5 5 1 2 1 1 1 1 S11* 1 1 1 5 2 2 1 5 5 5 1 2 1 1 1 S12* 1 1 2 2 2 5 2 4 2 1 2 2 3 3 4 S14* 1 1 2 2 2 2 3 2 1 5 1 2 2 2 2 2 4 4 S15* 2 1 1 2 2 4 2 1 2 3 4
Figure 3.18. The sentence count, and the sequence of the moves in the drafts before and after using the AWE tool (N = 13)
Note. *the move counts improved.
Draft 1 = First Draft, and Draft 2 = Final Draft.
Green = Introduction move, Blue = Methodology move, Pink = Results move, Yellow = Discussion move, and Gray = unidentifiable sentence.
Table 3.32
Overall sentence counts in moves in students’ first and final drafts (N = 13)
First Draft Final Draft
Introduction 28 50
Methodology 35 28
Results 4 13
Discussion/Conclusion 0 11
Figure 3.19 shows the number of moves that students had in their drafts before and after using the AWE tool. As seen in Figure 3.19, four students (30.8%) had one move, five (38.5%) students have two moves, and four students (30.8%) have three moves, but none of the students have four moves in the first draft. On the other hand, in their final drafts, only one student (7.7%) has one move, one student (7.7%) has two moves, four students (30.8%) have three moves, and, surprisingly, seven students (53.8%) have four moves.
Figure 3.19. The number of moves students had in their drafts before and after using the AWE tool (N = 13) 4 moves (0) 0.0% 1 move (4) 30.8% 2 moves (5) 38.5% 3 moves (4) 30.8%
First Draft
4 moves (7) 53.8% 1 move (1) 7.7% 2 moves (1) 7.7% 3 moves (4) 30.8%Final Draft
As for students’ accuracy of move identification, I compared their identification of moves to my own judgments, used as ‘gold standard’. The accuracy of students’ identification averaged at 71.1%. Four students (30.8%) identified 100% of their moves correctly. Table 3.33 shows the descriptive statistics of the accuracy.
Table 3.33
Descriptive statistics of students’ accuracy of move identification in their final drafts (N = 13)
Mean Median Min Max SD
71.1% 73.3% 33.3% 100.0% 26.8%
As seen from the transcripts of the semi-structured interviews, 12 students (92.3%) acknowledged that they tried to improve their abstract structures. They added, revised, and changed the sequence of their sentences, and finally four students (30.8%) achieved all moves in their final drafts of abstracts.
First, four students (30.8%) added sentences because they originally did not have certain moves in their first draft. They acknowledged that they missed certain moves in their first draft during the interview. Additionally, six students (46.2%) tried to revise their sentences so they would be more similar to a certain move. S4 demonstrated his process of sentence revision for the Introduction move:
First I have a move, for example introduction, in mind. And I know what I want to express in Chinese… Yeah, I really want to make this sentence similar to a sentence in the Introduction move in an abstract of a journal article, so I revised it several times. This research is done so I clearly know what happened in my research.
It can be seen that S4 started with the Chinese meaning of his Introduction sentence, and then tried to express his meaning in English to achieve the Introduction move by going through several revisions.
Moreover, two students (15.4%) reported they shifted their sentences around to match the sequence of the moves: Introduction, Methodology, Results, and
Discussion/Conclusion. S7 expressed that he tried to revise and then change the sequence of the sentences to “achieve the similarity of the sequence of a professional abstract” (S4).
Finally, four students (30.8%) mentioned that they managed to achieve four moves at the end of their revision process. S12 recalled his abstract writing process when using this AWE tool.
At the beginning I only had the Methodology move, but then I added more sentences with other moves in mind. Later on, I revised every sentence and strived to make the meaning of each sentence match the intended move better. (S12).
In other words, to improve their abstracts, participants tried several ways to achieve the intended communicative purposes.
From the analysis of their drafts before and after the use of the AWE tool and the transcripts of the semi-structured interviews, it was determined through the use of the AWE tool, students were able to add, revise, or change the sequence of their sentences to focus on the meaning of their abstracts and to better present a complete abstract structure.
3.6.2.5. Meaning focus: Evidence indicating the students focus on the meaning of lexical bundles
To examine whether students focus on the meaning of the detected lexical bundles, their drafts before and after using the AWE tool were analyzed. Especially, the correctness of the use of lexical bundles was investigated. In addition, the transcripts of the semi-structured interviews were analyzed to answer this question.
A lexical bundle was considered correct only when it was used in the appropriate moves (as identified by students), and its meaning made sense in the sentence. Among 30 lexical bundles used in students’ final drafts, only three of them (10%) were used
incorrectly; one was because of a mismatched move, and the other two were because of a misunderstanding the meaning of the lexical bundles by the students. Table 3.34 provides detailed information about the lexical bundles used by students, whether students used them correctly in the corresponding moves, and whether the meaning of the lexical bundles made sense. In the total of 30 lexical bundles, it was found that students used 12 (40.0%) lexical bundles for the Introduction move, 5 (16.7%) for the Methodology move, 7 (23.3%) for the Results move, and 6 (20.0%) for the Discussion move. It seems that students were able to use more lexical bundles in the Introduction move than other moves. Interestingly, students only had problems when using lexical bundles in the last move.
The analysis of the transcripts of the semi-structured interviews revealed that 13 students attempted to add appropriate lexical bundles in their abstracts, but only six of them (46.2%) reported they were successful. They mentioned that they learned lexical bundles from the examples provided by the AWE tool and/or searched them on the Internet. On the other hand, the remaining seven students (53.8%) could not successfully perform this. Two difficulties were mentioned in their interviews. S1, S8, and S12 believed the task of adding appropriate lexical bundles was too demanding for them at this stage. At the same time, although S15 tried to learn lexical bundles from the
examples provided by the AWE tool, he was not successful and explained, “there are too many examples so I couldn’t find the suitable ones efficiently.” Therefore, he did not
continue this task. However, as S7 noted, adding appropriate lexical bundles is a beneficial approach towards success in writing professional abstracts.
From the results of students’ drafts before and after using the AWE tool and their responses to the interview, it appears students tried to add appropriate lexical bundles in corresponding moves, although for some of them it was a difficult task to perform. Despite the difficulties they encountered, students were able to add lexical bundles in their abstracts and use them in the corresponding moves. When analyzing students’ drafts, I also found that S14 listed possible lexical bundles and annotated them with corresponding moves in the end of his document. This behavior could be interpreted as an acknowledgement of the importance of the use of lexical bundles to achieve the intended communicative purposes.
3.6.2.6. Authenticity: Evidence indicating the similarity between this task to a task that