Search for: All records

With the emergence of self-tracking devices that collect and produce real-time personal data, it is becoming increasingly necessary to innovate data literacy frameworks and student curricula to address new competencies in data handling, visualization, and use. We examine the evolution of data literacy frameworks across the past 7 years, specifically focusing on the inclusion of self-data competencies. We analyzed existing data literacy frameworks to identify common phases of data engagement. A scoping review of published data literacy frameworks was conducted, and 23 studies were included for analysis. Results from this scoping review demonstrate the existence of at least eight sequential phases of data engagement to develop data literacy. Two of these phases address personal or self-data competencies. We then describe a curriculum that addresses these eight phases of data engagement by pairing biometric devices with online tools and educational materials to scaffold self-data knowledge, skills, and attitudes. Based on this, we conclude with the need to propose holistic data literacy education programmes, considering the curriculum as a model to guide similar materials aimed at fostering emerging data competencies. 

As a creative endeavor, scientific research requires inspiration, innovation, exploration, and divergent thinking. Yet, in K-12 settings, it is often viewed as rigid and formulaic. MindHive is a web-based platform designed to facilitate student-teacher-scientist partnerships in research on human behavior. Features support research phases (e.g., question finding, study design, peer review, iteration), and their creative dimensions, including exploration, expressiveness, collaboration, and enjoyment. Interviews with teachers and students who used MindHive show how learners describe their experiences as creative agents. This work illustrates how educational technologies can broaden STEM participation by being authentic to methodical and creative aspects of STEM research.