Gemma F. Mojica (NC State), Emily Thrasher (NC State), Hollylynne Lee (NC State)
Abstract
Background. The NSF-funded Invigorating Statistics and Data Science Teaching through Professional Learning (InSTEP) project developed an online professional learning platform to support 6-12+ grade teachers in developing expertise to teach data science and statistics (DS&S). This free platform provides customized recommendations (through surveys) to personalize teachers’ learning experience based on their own goals. A major goal is to develop teachers’ technological pedagogical statistical knowledge (Lee & Hollebrands, 2011) through learning about seven interconnected dimensions of effective DS&S environments (Lee et al., 2021; Ben Zvi et al., 2018). Teachers can choose their own path to engage in learning materials within 7 dimensions, with resources and technology tools curated, designed and vetted by DS&S experts. Teachers also have the opportunity to engage in data investigations using technology tools and learn about innovative teaching practices related to DS&S. Teachers can also demonstrate their competency in teaching DS&S by engaging in microcredentials, performance assessments to demonstrate competency with ideas presented in InSTEP modules and data investigations. The InSTEP team used an iterative design-based approach to both the development of the platform and research. In the Fall of 2022, the InSTEP platform was field tested with 83 middle and high schools teachers.
Methods. We investigated the extent to which different aspects of the InSTEP platform supported teachers’ in developing expertise to teach statistics. We used mixed methods where we collected, analyzed, and integrated multiple sources of data: 3 surveys, datalogs, microcredential responses, and interviews. For example, the self-efficacy survey (SETS, Harrell-Williams et al., 2021) was administered using a pre-/post- design to measure possible changes in teachers’ confidence to teach DS&S (n=41). To understand teachers’ learning experiences, we analyzed datalogs for all participants (n=83), and examined users’ experience through a survey (n=45) and interviews (n=12).
Findings. While data analysis is in progress, preliminary findings suggest: Teachers’ increased their confidence to teach DS&S with significant gains overall as measured with SETS. 78% of teachers found the personalization recommendations as effective in supporting their choice of learning pathways. 69% teachers indicated that data investigations and use of CODAP were critical in helping them apply what they learned to their practice. Teachers viewed microcredentials as a tool for reflection and professional growth. Teachers did not view discussion boards as effective in their growth and they desired to have more opportunities to increase their statistics knowledge.
Implications. Our results have several implications for DS&S professional learning and designing DS&S learning environments. Key features that supported asynchronous online learning that we identified could be used to design other online learning environments, whether focused on undergraduate or graduate education. Features of our design that teachers’ found effective, such as engaging in data investigations with CODAP, can be applied to other professional learning contexts, whether online or not. While perhaps more difficult to implement in face-to-face settings, personalizing teachers’ learning opportunities based on their goals and prior experiences and practices could also be implemented in other environments.