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to keep text analysis categories and student responses as two separate entities. After all, a researcher could interpret citation functions, but she could never be sure whether this is the real intention of the writer (Swales, 2014). As found in Willett (2013), considerable discrepancies existed between readers’ interpretation of the reasons for citing sources and those reported by the writers. I thus decided to define rhetorical functions in the text analysis as what the citations appear to achieve, based only on the formal features of the citations. For example, a group citation with multiple sources suggests links between sources; a source introduced by e.g., suggests the exemplification function. On the other hand, student responses were treated simply as self-reported reasons for using the citation features. There was no expectation that the self-reports would conform to text analysis. In the end, the two types of data were used to address two separate research questions: 1. What citation features do students use? and 2. What are the reasons for students’ use of citation features?


Even so, some link between the two types of data analysis could enable a comparison between the researcher’s perspective and the students’ perspective. To do this, I used the categories in my text analysis framework (integral/non-integral citation forms, links between sources, evaluation) to identify preliminary codes in the interviews. Within each preliminary code, I then used an inductive approach to thematic analysis (Braun & Clarke, 2006) to identify further codes. For example, when I asked the participants’ reasons for using the integral or non-integral citation form in their texts, several codes emerged from their responses: to highlight the content or the author; to distinguish commonly accepted knowledge


Qingyang Sun


from particular findings; to aim for variety; and to consider sentence construction. A wider range of reasons were given by participants to explain their use of a particular feature. Many of the reasons developed the text analysis framework further by generating sub-categories (see Sun, 2019). For example, a salient category for many citation features was the participants’ tendency to focus on language form instead of rhetorical purposes. This framework of student-articulated intentions added to the complexity of the text analysis framework alone, in line with previous studies (Petrić & Harwood, 2013; Wette, 2017).


CONCLUSION


This paper has illuminated issues in researching students’ texts and has discussed possible solutions. Firstly, when the interview time is limited, texts need to be informative enough to elicit rich data in interviews, and this involves difficult decisions around selection of extracts. Secondly, while discourse-based interview questions require the use of non-technical terms and should remain open-ended, they also require appropriate prompts for participants to focus on the citation features investigated. Thirdly, as previous research has shown (e.g., Willett, 2013), students’ reasons for textual choices are not likely to match the researcher’s interpretation. Despite the lack of match-up between reader interpretation and writer intentions, exploring and comparing the differences can be valuable in showing the expected and unexpected reasons for students’ textual choices. This can also help us understand how such writers develop citation and source use competence.


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