Implementing meaningful statistics and data science instruction in middle and high school classrooms has long been a challenge in the United States, despite the growing importance of these domains for STEM careers and for developing data-literate citizens. The InSTEP project was designed to address this challenge by strengthening grades 6–12 statistics and data science teaching through the development and study of an online professional learning environment for in-service teachers. The project aimed to support teachers in building both deeper statistical content knowledge and the pedagogical expertise needed to enact high-quality instruction in their classrooms. In addition, the project sought to examine the effectiveness of a professional development model that integrates personalized, self-guided online learning to support sustained teacher learning and growth (see for more details Lee et al., 2025).
Building on prior research and development work by the project team, InSTEP assembled a suite of research-based online learning materials that engage teachers in doing authentic statistics and data science work, analyzing classroom video, learning pedagogical frameworks for task implementation, and applying guidelines for identifying and designing high-quality data-rich tasks. Central to this work was the development and integration of the Data Investigation Process framework (Lee et al., 2022), which draws on research in statistics education, data science practices, and empirical studies of professional data scientists. This framework provided a coherent structure for both teacher learning experiences and classroom implementation, emphasizing practices such as framing investigable questions, exploring and visualizing data, and making and communicating claims grounded in evidence.
The project unfolded through iterative design and research phases. Early work included focus groups with middle and high school teachers and district leaders, followed by cognitive interviews with teachers engaging in the emerging microcredential ecosystem. These formative studies informed revisions to both content and platform design. Subsequent field testing engaged cohorts of teachers in scaffolded professional learning experiences that included self-paced modules, hands-on data investigations, and opportunities to demonstrate learning through microcredentials. Throughout the project, multiple sources of data (e.g., platform usage logs, surveys, microcredential submissions, lesson plans, and interviews) were collected to examine teacher learning, engagement, and pathways through the materials.
Findings from the field test and subsequent analyses indicate that teachers used the platform in diverse, goal-directed ways and demonstrated meaningful growth in confidence and instructional practice related to statistics and data science (Lee et al., 2024; Mojica et al., 2023). Teachers showed statistically significant gains in self-efficacy for teaching statistics and reported that hands-on data investigations and technology tools were particularly supportive of their learning. Analyses of lesson plans and classroom enactments revealed that teachers most consistently supported phases of the Data Investigation Process related to framing problems in authentic contexts, while phases such as exploration and visualization were less fully developed—especially when technology was not leveraged (Thrasher et al., 2025). Lessons that incorporated tools such as CODAP were more likely to support deeper student reasoning and engagement, underscoring the importance of aligning professional learning with technological supports.
The project also yielded insights into how teachers engage with personalized, online professional learning. Teachers demonstrated sustained engagement over time, though participation followed multiple patterns shaped by individual goals, prior knowledge, and time constraints (Lee et al., 2023). Features designed to support self-regulated learning (e.g., personalized recommendations, progress tracking dashboards, and flexible pathways) were widely used and perceived as valuable, while more social features such as discussion forums were less frequently utilized. These findings informed ongoing refinements to the platform, including increased emphasis on data investigations, expanded support for technology use, and improvements to backend systems for tracking and feedback.
Together, the InSTEP project contributes a research-informed model for online professional learning in statistics and data science that balances flexibility with coherence, supports teacher agency, and is grounded in best practices. The resulting professional learning platform, made freely available as an open online resource, offers both practical tools for teachers and empirical insights for the field about how personalized, asynchronous professional learning environments can support sustained teacher growth in emerging and critical content areas.
