Overview of Sustainability Professional Development and Evaluation: As part of the National Science Foundation funded "Sustainability: Promoting Sustainable Decision Making in Informal Education" project, the Oregon Museum of Science and Industry (OMSI) and its partners developed a professional development website and workshop. The goal of this and other project deliverables was to promote sustainable decision making by building skills that allow participants to weigh their choices and choose more sustainable practices. ExhibitSEED (Exhibit Social Environmental and Economic Development) refers
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Oregon Museum of Science and IndustryRenee B. Curtis
This paper presents ten guiding principles for designing construction kits for kids: design for designers; low floors and wide walls; make powerful ideas salient - not forced; support many paths, many styles; make it as simple as possible - and maybe even simpler; choose black boxes carefully; a little bit of programming goes a long way; give people what they want - not what they ask for; invent things that you would want to use yourself; and iterate, iterate - then iterate again.
This REAL project arises from the 2013 solicitation on Data-intensive Research to Improve Teaching and Learning. The intention of that effort is to bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term and to revolutionize learning in the longer term. This project addresses the issue of how to represent and communicate data to young people so that they can track their learning and weaknesses and take advantage of what they learn through that tracking. The project team aims to address this challenge by giving young people (middle schoolers) the tools and support to create, manipulate, analyze, and share representations of their own understanding, capabilities, and participation within the Scratch environment. Scratch is a programming language and online community in which youngsters (mostly middle schoolers) engage in programming together, sometimes to make scientific models and sometimes to express themselves artistically using sophisticated computer algorithms. Scratch community participants are often interested in keeping track of what they are learning, so this population is a good one for exploring ways of helping young people make sense of data that records their participation and learning. The team will extend the Scratch programming language with facilities for manipulating, analyzing, and representing such data, and Scratch participants will be challenged to make sense of their learning and participation data and helped to use the new facilities to do write programs to carry out such interpretation. Scratch participants will become visualizers of their participation patterns and learning trajectories; research will address how such data explorations influence their learning trajectories. Scratch and its community are the place for the proposed investigations, but what is learned will apply far more broadly to construction of tools for allowing learners to understand their participation and learning across a broad range of environments. This project addresses the sixth challenge in the program solicitation: how can information extracted from large datasets be represented and communicated to maximize its usefulness in real-time educational stings, and what delivery mechanisms are right for that? The PIs go right to the learners; rather than looking for delivery mechanisms for communicating the data representations, they give young people tools and support to create manipulate, analyze, and share those representations, bringing together approaches to quantitative evidence-based learning analytics with the constructionist tradition of learning through design experiences. In addition to helping us learn about how to help youngsters analyze data about their perforance and self-assess, the PIs expect that their endeavor will help us better learn how to help young people become data analyzers, an important part of computational thinking. Learners will, in the process of engaging with data representing their development and participation, interact with visualizations, model and troubleshoot data sets, and search for patterns in large data sets. In addition, the tools being developed as part of this project will be applicable for analysis of other types of data sets. The results that will transfer beyond Scratch and the Scratch community, are (1) the kinds of tools that make such analysis possible for youngsters, (2) the kinds of challenges that will get youngsters interested in doing such analyses, (3) the kinds of data youngsters can handle, and (4) the kinds of scaffolding and coaching youngsters need to make sense of that data.
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Benjamin Mako HillMitchel ResnickNatalie Rusk
This collaborative project between Tufts University and the Massachusetts Institute of Technology is researching and developing a new version of the Scratch programming language to be called ScratchJr, designed specifically for early childhood education (K-2). The current version of Scratch, which is widely implemented, is intended for ages 8-16 and is not developmentally appropriate for young children. This work will provide research-based evidence regarding young children's abilities to use an object-oriented programming language and to study the impact this has on the children's learning of scientific concepts and procedures. The team will develop ScratchJr in an iterative cycle, testing it in both in the Devtech lab at Tufts and the Eliot Pearson lab school and with a wider network of early childhood partners. At the end of the three-year project, ScratchJr will have been tested with approximately 350 students in K-2, 40 parents, and 58 early childhood educators. ScratchJr will have three components: 1) a developmentally appropriate interface, with both touch screen and keyboard/mouse options; 2) an embedded library of curricular modules with STEM content to meet federal and state mandates in early childhood education; and 3) an on-line resource and community for early childhood educators and parents. The research questions focus on whether ScratchJr can help these young children learn foundational knowledge structures such as sequencing, causality, classification, composition, symbols, patterns, estimation, and prediction; specific content knowledge; and problem solving skills. This interdisciplinary proposal makes contributions to the fields of learning technologies, early childhood education and human computer interaction. ScratchJr has the potential for broad implementation in both formal and informal settings.