Data Collection

Dance practitioners are a group in urgent need of attention. Reviewing the literature, there are problems in the professional status of dance practitioners. These include low and unstable financial income, limited professional development, multiple pressures and role conflicts faced by practitioners, social class and social acceptance, and the wider impact on the discipline of dance. As the core group that promotes the development of the professional field of dance, the contribution and return of dance practitioners do not match, which will largely frustrate the career expectations and sense of achievement of dance practitioners, leading to job burnout. This will not only lead to the loss of talent within the industry, but also affect the current and future development of the professional field of dance. Through induction, this study conducted semi-structured interviews with 10 qualified dance practitioners in Chinese universities, compared the cultural capital accumulated by dance practitioners in the early stage with the actual economic and social capital in the later stage, and investigated the status quo of cultural capital transformation of dance practitioners. Second, what challenges or obstacles do dance practitioners face in the transformation of cultural capital? And why they were able to hold on to their dance careers in the face of difficulties. Through the exploration of these stories, it can be seen that the transformation of cultural capital accumulated by dance practitioners into economic capital is difficult, and the transformation into social capital is more significant. Dance practitioners' love for dance and spiritual values are the main motivators that help them overcome challenges and obstacles. They now face the dual challenges of physical and mental health and job burnout. This study considers implications for future research and practical applications and hopes to provide a call for concern for the health and safety of dance practitioners, as well as relevant supporting materials.

Data Collection

The teacher interviews were focused on June to July 2024. A semi-structured interview was conducted with each participant in a library discussion room at the University of Edinburgh, UK. Due to geographic and temporal differences, all interviews were conducted online through Teams, and the timing of the interviews was random, referencing the time of the participants. Each interview lasts 45 to 60 minutes. In order to make the interview more smooth, each participant has access to interview questions in advance. Before each participant was interviewed, during the interview, there were videotaped video recordings and audio interview scripts, and the key points emphasized by the interviewer to the participant were marked and recorded. The semi-structured interviews in this study include four themes: (1) cultural capital accumulation; (2) The transformation of cultural capital into economic and social capital; (3) Challenges faced and coping strategies; (4) Reasons and prospects for persistence. The purpose of the interview is to understand whether the cultural capital of dance practitioners (college dance teachers) such as dance professional knowledge, academic background, accumulated professional certificates and honors can be transformed into actual economic and social capital; As well as the actual performance and some challenges and obstacles in the transformation process; In the face of difficulties and pressure, I chose to stick to my dance career. During the interview, I asked the participants 23 questions, including open questions, closed questions and leading questions. After the participant described specific events and feelings during the interview, the interviewer would again summarize what the participant had said in a general way, such as: "This is......... ? What are you trying to say?" "So you think......" Ensure data accuracy. In addition, interviewers focus primarily on open-ended questions. When the intervieee is unable to continue to answer in-depth questions, the interviewer will guide to a certain extent, reducing the deliberate guidance and intervention of the interviewer. For example, "What did you just share...... Can you tell me more?" Based on your personal experience, what do you think is the cause of...... While it's helpful to have a basic interview guide, it's also important for interviewers to "actively listen and move the interview forward as much as possible by building on what the participants have already begun to share" (Seidman, 2013). All participants' data is kept in onedrive's university account and can only be shared between the author and the tutor. All data will be destroyed within 30 days of the completion of the paper.

Data Analysis 

In my data analysis, I used the thematic analysis approach. Thematic analysis is a method of identifying and recording relevant patterns in methodological data that, despite multiple approaches, often follows the process from coding data to reporting and discussing analytical topics. By extracting statements from large amounts of qualitative data, thematic analysis can enable data analysis to become coherent and transparent to the reader, and thus can strongly support data analysis. Theme analysis consists of six steps: familiarization with the data, preliminary coding, finding topics, reviewing topics, defining and naming topics, and finally writing a report (Braun & Clarke, 2006; Miles & Huberman). Each participant interviews were audio-recorded and transcribed. First, I read the data of each participant and paid special attention to the exclusivity of the data during the second level of coding. To facilitate preliminary coding, interviews irrelevant to the study question were excluded, and single sentences were collated into complete paragraphs to match each interview question. Merriam (2009) believes that coding is the process of the researcher reading the data, noting interesting, potentially relevant or important parts, and conducting conversations, questions and comments with the data. In this study, I adopted open coding, which implies maintaining an open mind during coding. In the first level coding, I directly coded the participants' statements, marking the original data paragraphs or sentences that fully fit the research question; in the second level coding, I read the complete data and summarize words and phrases next to the text (Merriam, 2009). Merriam (2009) mentioned that "data analysis is a complex process involving repeated switching between concrete data and abstract concepts, between inductive and deductive reasoning, and between description and interpretation" (p.176). Therefore, I moved back and forth between data fragments, descriptions, and interpretations, looking for common clues to these themes (Fraser, 2004). Due to the open encoding of the data content, 53 secondary codes were generated, which posed challenges for my subsequent topic definition and naming. By analyzing the common clues of these contents, I continue to summarize the secondary coding into seven tertiary coding themes that can directly answer, define and name the research questions. Four-level coding corresponds to the four questions in this study, each corresponding to the three-level coding topic and answering the four questions of the study .