A Scaffolded Data-Centric Approach to Improved Learning of Introductory Computing Concepts

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The significance and importance of this project is the availability of improved science for teaching fundamental computational concepts both to students pursuing degrees in computer science and to students where computation plays a secondary but increasingly vital role in the practice of their discipline. The fundamental problem addressed by this project is the limited methods currently available to motivate and sustain student engagement in challenging parts of the learning process. Through the use of relevant and authentic data combined with an improved learning environment the project will achieve improvements in learning and, more broadly, will provide the "data literacy" increasingly critical in the economy and society. While advantageous for all students, these characteristics of realism are especially engaging for students in under-represented populations. The "big data" resources are useful not only in introductory courses but also for emerging data science courses. The project will develop a deep understanding of the impact on student motivation and learning of the project's interventions across diverse student populations. This understanding will influence curriculum design and help shape better pedagogical practices. The goal and scope of this project will be enhanced knowledge of, and extended technologies for, improving motivation and cognitive gains of students learning introductory computing concepts. The project will extend the catalog of available data sets, add authoring and curation tools enabling the efficient creation and restructuring of data sets, improve the ability of a student to search for a relevant data set, and provide scaffolding to allow early visualization of "big data". The project will extend a block-based visual programming environment, BlockPy, allowing mutual translation between Blockly and Python. The extension will add an instructor authoring tool and run-time support for immediate feedback on algorithm-based exercises that improves student learning and encourages student exploration of alternative algorithms. New visualizations will support student learning in any programming class using big data. These resources will be combined with other course elements in a robust web framework supporting many forms of distributed instructional delivery. The project will apply the Dick and Carey Instructional Design method to facilitate adoption by others and integrate detailed assessment of the curriculum.