Search for: All records

Innovative dynamic data tools afford opportunities for K-12 students and teachers to explore multivariate data and create linked data representations. These tools also support engagement in data moves, which are transnumerative actions to process, organize, and visualize data. The current study sought to understand how prospective K-12 mathematics teachers (PMTs) use data moves in the Common Online Data Analysis Platform (CODAP) to create and interpret visualizations and statistical measures to make sense of state-level data about education in the United States. Extending the work of Erickson et al. (Citation2019), a framework is presented to characterize data moves and provide examples of actions within CODAP that illustrate each data move. Based on analysis of thirty screencasts created by PMTs, four examples highlight PMTs’ use of data moves to investigate data in CODAP. 

Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning.