Opportunities for Students’ Wellbeing: Enhancing Perceptions of Data Science through Data Storytelling in a Diverse Classroom Context

Yiyun FAN1,*, Amanda Wan Mei SOON2, and Kah Loon NG1,*

1Faculty of Science, National University of Singapore (NUS)
2Office of Provost, NUS

*yiyunfan@nus.edu.sg; *kloon@nus.edu.sg

Fan, Y., Soon, A. W. M., & Ng, K. L. (2024). Opportunities for students’ wellbeing: Enhancing perceptions of data science through data storytelling in a diverse classroom context [Poster presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore.https://blog.nus.edu.sg/hecs/hecs2024-yyfan-et-al/

SUB-THEME

Opportunities from Wellbeing

KEYWORDS

Student wellbeing, data storytelling (DS), learning perceptions, data science, diverse classroom

CATEGORY

Paper Presentation

EXTENDED ABSTRACT

Data science has emerged as a prominent discussion topic in education. Increasingly, students from underrepresented majors in non-STEM fields are showing interest in the data science industry, recognising its potential to enhance their research or employment opportunities. This trend is underscored by workplace phenomena, for instance, where recruiters in the UK may be more inclined to favour STEM students over non-STEM ones due to the latter often lacking STEM skills such as mathematics application and programming and being more difficult/expensive for employers to train (Grinis, 2017). This has highlighted the growing importance of such skills and the advantages they confer in the job market. However, as more entry-level courses in data science are introduced at university levels to address this trend, students, particularly non-STEM ones, have reported experiencing difficulty in learning courses that seem unrelated to their professional fields. This increased workload exacerbates their academic challenges and adds to their overall stress levels. For instance, feedback from a general introductory data science course revealed that 5.8% of comments were negative. Students expressed their stress from learning data analysis software. This rate increased to 10.4% the following semester and 12.1% the semester after (personal communication, June 20, 2024).

 

Data Storytelling (DS) utilises storytelling elements to compress information and convey key elements through narratives and data visualisations (Ryan, 2016), and holds the potential for enhancing learning experiences. It has been reported by recent scholars that DS elements, albeit with limited pedagogical constructs, have a promising future in educational settings (e.g., Chen et al., 2019; Echeverria et al., 2018; Martinez-Maldonado et al., 2020).

 

Many previous scholarly works have tackled the challenge of developing course curriculum that not only attract students from diverse backgrounds (e.g. gender and ethnicity group disparities) but also foster “communication, reasoning and collaboration that cross disciplinary boundaries” (Dierker et al., 2017, p. 55). However, few studies have investigated the impact of students’ academic major backgrounds and their related concerns, which are becoming increasingly relevant in today’s job market. Building upon this gap, this study examines the role of DS as supplementary material in the curriculum to introduce elementary data science skills to students through engaging narratives and data visualisations. By integrating DS into the course, this study aims to help students, particularly those with non-STEM backgrounds, better adapt to the current educational trend, thereby reducing their stress and improving their perceptions of learning data science.

 

The primary analysis method in this study involves qualitative analysis of students’ written and interview feedback after engaging with data stories based on the content of a general data science course at a prestigious university in Singapore. This study explores students’ perceptions of DS and their expectations of its role in future application in educational settings. Notably, feedback from non-STEM students, collected after their review of DS based on a random dataset, reveals their overall positive perspectives on the use of DS to support and improve the curriculum. Recurrent feedback items include students’ desire for more concise data stories integrated into data science skill introduction and their interest in engaging with more stories like these. This feedback highlights the potential of DS to assist students from various academic backgrounds, particularly non-STEM ones, in understanding and appreciating data science, thus reducing their stress in learning.

REFERENCES

Chen, Q., Li, Z., Pong, T.-C., & Qu, H. (2019). Designing Narrative Slideshows for Learning Analytics. In Proceedings of the IEEE Pacific Visualization Symposium, PacificVis’19 (pp. 237–246). https://doi.org/10.1109/PacificVis.2019.00036.

Dierker, L., Ward, N., Alexander, J., & Donate, E. (2017). Engaging underrepresented high school students in data driven storytelling: An examination of learning experiences and outcomes for a cohort of rising seniors enrolled in the gaining early awareness and readiness for undergraduate program (GEAR UP). Journal of Education and Training Studies, 5(4), 54–63. https://doi.org/10.11114/jets.v5i4.2187

Echeverria, V., Martinez-Maldonado, R., Shum, S. B., Chiluiza, K., Granda, R., & Conati, C. (2018). Exploratory versus explanatory visual learning analytics: Driving teachers’ attention through educational data storytelling. Journal of Learning Analytics, 5(3), 72– 97. doi: http://dx.doi.org/10.18608/jla.2018.53.6

Grinis, I. (2017). The STEM Requirements of “Non-STEM” Jobs: Evidence from UK Online Vacancy Postings and Implications for Skills & Knowledge Shortages. Systemic Risk Centre.

Martinez-Maldonado, R., Echeverria, V., Nieto, G. F., Shum, S. B. (2020). From data to insights: A layered storytelling approach for multimodal learning analytics [Paper presentation]. In CHI ’20 Conference on Human Factors in Computing Systems, April 25–30, 2020, Honolulu, HI, USA.

Ryan, L. (2016). The Visual Imperative: Creating a Visual Culture of Data Discovery. Elsevier Science.

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