Yiyun FAN1, Kah Loon NG1,*, and Amanda Wan Mei SOON2
1Department of Mathematics, Faculty of Science (FOS), National University of Singapore (NUS)
2Office of the Provost (PVO), NUS
Sub-Theme
Building Learning Relationships
Keywords
Project-based learning, data storytelling, career-readiness, data science, teamwork and collaboration
Category
Paper Presentation
Rapid technological achievements have profoundly reshaped our lives and transformed the labour market in an unpredictable way (Brynjolfsson & McAfee, 2011). Advancements in high-speed communications and collaborative technologies have made it easier for employers to access global talent in rapidly growing fields like programming and design (Bridglall, 2018). These fields increasingly require various data-driven skills to meet the demands of a constantly evolving job market. In the meantime, educators in higher education have long been concerned with the persistent gap between these skills and the knowledge provided through formal education (Stross, 2017; Suvedi et al., 2016). However, the reality remains challenging, and we still lack clarity on the most effective way to achieve this goal.
Career-readiness in higher education, in our own definition, refers to the general attributes that employers seek in graduates as they enter the workforce. Specifically, according to the U.S. National Association of College and Employers, career-readiness encompasses eight competencies: career and self-development, communication, critical thinking, equity and inclusion, leadership, professionalism, teamwork, and technology. As the employment landscape continues to change, graduates are increasingly expected to develop these competencies to more effectively navigate the skills required for career success.
Storytelling with data, or data storytelling (DS), similarly, employs the technique of “information compression” (Ryan, 2016) to emphasise key data insights to engage, reason with, and explain to target audience effectively. Project-based learning (PBL), on the other hand, is a commonly used practice in education that assigns real-world-alike and time-limited projects to students to achieve performative objectives and facilitate collaborative learning (Smith & Dodds, 1997). Krajick and Blumenfeld (2012) summarise five features of PBL classrooms which are: driving question, situated inquiry, collaborations, using technology tools to support learning, and creation of artifacts.
This empirical study addresses the gap between students’ career-readiness competencies and the forms of learning typically provided in the field of data science (e.g., lectures and structured coursework). We aim to harness the benefits of DS as a pedagogical enhancement targeted at fostering key career-readiness skills to better prepare students for the rapidly changing employment landscape.
We developed and implemented an intervention that integrates DS theories into a PBL framework, thus updating traditional PBL format into a DS-driven PBL classroom. This revised setting emphasises group project-oriented classroom settings, employing open-ended inquiry guided by data, real-world puzzles and problems, collaborative problem-solving, perspective-thinking, and group decision-making, which vividly reflects real-life teamwork in the workplace.
To understand how students experience and perceive this instructional approach, we conducted a comparative study involving four student groups: three experimental groups exposed to the data-driven PBL classroom and one control group in a traditional task-driven PBL classroom. Each group engaged in a ~40-minute instruction session, a 3.5-hour project collaboration and group presentation, and participated in pre- and post-study questionnaires of 12 skills, which include perception ratings and qualitative feedback. Through this design, we assess how this revised framework influences students’ collaboration, communication, and other career-readiness performance.
By comparing participants’ pre- and post-study perception ratings, this study found that the experimental groups consistently reported a higher proportion of positive perception changes in skills such as problem-solving, perspective-thinking, as well as team communications and collaborations, when compared to the control group’s changes. The overall trend suggests that the experiment groups experienced more positive shifts in perception and fewer negative shifts compared to the control group. These findings highlight the potential effectiveness of the DS in PBL approach in fostering positive changes in students’ building of career-readiness skills. We hope that in the future, we will have the opportunity to implement this framework in actual classroom settings through a longitudinal study conducted over at least one semester.
References
Bridglall, B. L. (2018). Preparing for disruption by creating future possible selves. Liberal Education, 104, 34-39.
Brynjolfsson, E., & McAfee, A. (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Digital Frontier Press.
Krajick, J., & Blumenfeld, P. C. (2012). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 317—334). Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.020
National Association of Colleges and Employers. (n.d.). What is career readiness? https://www.naceweb.org/career-readiness/competencies/career-readiness-defined
Ryan, L. (2016). The visual imperative: Creating a visual culture of data discovery. Elsevier Science.
Smith, B., & Dodds, R. (1997). Developing managers through project-based learning. Routledge. https://doi.org/10.4324/9781315258041
Stross, R. E. (2017). A practical education: Why liberal arts majors make great employees. Redwood Press.
Suvedi, M., Ghimire, R. P., & Millenbah, K. F. (2016). How prepared are undergraduates for a career? NACTA Journal, 60(1a), 13—20. https://nactaarchives.org/attachments/article/2392/7%20Suvedi_NACTA%20Journal%20Special%20May%202016.pdf