ESL Writers Voice in Academic Writing
[Name]
[Institution]
This paper presents a concise report bearing the results of a quantitative analysis of the textual data collected as part of the mixed-method longitudinal study of ESL adolescent writers’ voice in their academic essays as they transition from a High School Preparation (HSP) intensive English program to mainstream high schools in Sydney, Australia.
Introduction
Recent studies indicate that students in the transition from one educational setting to another experience challenges on multiple levels resulting in emotional insecurity and inability to demonstrate adequate skills in such essential areas as writing, nonetheless, research into writing in transition is nearly non-existent (Baker, 2013; Fong, 2013; Hanna & Saidy, 2014; Krause, 2001; Yi, 2009).
Within academic writing, it is vital not to underestimate the author’s voice and the students’ ability to use their voice effectively to their advantage. Even though it is not surprising that voice as learning and teaching concept is mentioned repeatedly in the Australian high school curriculum and the New South Wales English syllabus (ACARA, 2019; NESA, 2019), research into the authorial voice of adolescent writers in transition, especially ESL teenage writers moving from HSP to high school is non-existent. The current study purposes to abridge this gap and look at how the students’ authorial voice changes as they move to a more challenging educational setting.
The study takes a dialogic view of voice and recognises the inextricable connection between the writers’ voice and changes to the context in which writing takes place. It looks at the voice as a living thing, a reflection of the writer’s identity in the discourse, perpetually changing and developing within the intimate environment of the author as well as broader socio-cultural contexts (Matsuda, 2015; Matsuda, 2001; Matsuda & Tardy, 2007, Tardy, 2012).
Methods
The study adopted a quantitative approach. The study data comprised of the student essays; these had more than a thousand words per student, collected in the two settings, HSP and high school. The textual data analysis was performed using Hyland’s interactive model of voice. The coding categories for the analysis of the students’ essays were generated based on Hyland’s (2005) interactional model, which distinguishes between the dimensions of stance and engagement. Stance, or writer-oriented features of interaction, include the writer’s attitude and certainty and is realised through the categories of Hedges, Boosters, Attitude Markers and Self-mention (See Table 1).
[Insert Table 1 here]
Engagement in Hyland’s (2005) model builds a connection between the writer and the readers, stressing the solidarity with the readers, making predictions about what the readers likely to think or how they are likely to react and explicitly guiding their actions or thinking. This orientation towards the reader become exposed through features such as Reader Mention, Directives, Questions, Reference to Shared Knowledge and Personal Asides (See Table 2).
[Insert Table 2 here]
The first stage of textual data collection took place when the participants were still in HSP, going to Year 10 in their respective high schools. The second stage of data collection happened two years later for four out of six participants when they were doing their Higher School Certificate in term four, Year 11, and for two participants the second data collection happened one year later when they were in the last term of Year 10 in their high schools.
Important to note is that the essays the students produced in HSP and HS have the same rhetorical structure of an argumentative essay, but differ in the supporting evidence they use in the body paragraphs, The HSP essays relied on the writers’ general knowledge, the year 10 high school essays comprised information from external sources, while Year 11 essays being personal responses to prose fiction, film or poetry.
Findings
The results of the quantitative data analysis reveal the most and the least frequently used markers of voice in both settings. The results also reveal the overall frequencies of the voice markers and the stance. Moreover, the study similarly unveils the engagement for each participant in the two settings. All the frequencies were normalised per 1000 words.
[Insert Table 3 here]
Table 3 shows that the marker of voice that the participants relied on most in both HSP and HS is a marker of stance, Attitude Markers (20 per 1000 words in HSP and 10 in HS), followed by a marker of engagement, Reader Mention (12 in HSP and 6 in HS). The least frequently used marker was Questions (0.4 in HSP, and 0 in HS) and Personal Asides remained unused in either setting.
[Insert Table 4 here]
Table 4 shows that for five out of six participants, there are fewer expressions of total voice in HS than in HSP. When looking at the stance separately, it is apparent that all the participants used stance more often in HSP compared to HS. When comparing the use of stance and engagement, apparently, all six participants attempted to communicate their stance more often than they tried to engage their reader in both settings. While all the participants show similar patterns in the use of stance in the two settings, the use of engagement is similar for five participants but is different for Kimberly, who showed a significant increase in the frequency of the markers of engagement in HS. Due to that high number of attempts to engage her reader in HS, Kimberly’s overall markers of authorial voice showed only a slight increase in HS compared to HSP.
Implications
The initial plan for the current research project was a qualitative study approach because of the small sample size of only six participants. However, the results of the textual data coding revealed a distinct downward trend in the use of the markers of voice for most of the participants and a significant drop in the use of markers of stance, especially Attitude Markers, for all six participants in high school compared to HSP. Therefore, an in-depth qualitative data (comprising textual data and interviews with the participants and their teachers) analysis will shed light on the quantitative textual data results presented in this brief report.
[Name] is a teacher and coordinator at the [Institution]. She/he is particularly interested in extensive reading and motivation.
[Email]
References