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Open source software (OSS) is ubiquitous, serving as specialized applications nurtured by devoted user communities, and as digital infrastructure underlying platforms used by millions of people. OSS is developed, maintained, and extended through the contribution of independent developers as well as people from businesses, universities, government research institutions, and nonprofits. Despite its prevalence, the scope and impact of OSS are not currently well-measured. Recent policies of the U.S. Federal Government promote sharing of software code developed by or for the Federal Government. While the policy to promote reusing and sharing of software created with public funding is relatively new, public funding plays an important and not fully accounted role in the creation of OSS. This paper aims to measure the scope and value of OSS development in the U.S. Federal Government. We collect data from Code.gov, the government’s platform for sharing OSS projects, and study contributions of agencies. The dataset contains 17K repositories from 21 agencies, with the majority of contributions originating from the DOE, NASA and GSA. In addition, we collect data on development activity (e.g., lines of code, contributors) of the repositories on GitHub, the largest hosting facility worldwide. Adopting a cost estimation model from software engineering, we generate estimates of investment in OSS that are consistent with the U.S. national accounting methods used for measuring software investment. Finally, we generate and analyze collaboration network resulting from cross-agency contributions to repositories and explore the centrality of agencies in the network. 

A model that maps the requisite skills, or knowledge components, to the contents of an online course is necessary to implement many adaptive learning technologies. However, developing a skill model and tagging courseware contents with individual skills can be expensive and error prone. We propose a technology to automatically identify latent skills from instructional text on existing online courseware called Smart (Skill Model mining with Automated detection of Resemblance among Texts). Smart is capable of mining, labeling, and mapping skills without using an existing skill model or student learning (aka response) data. The goal of our proposed approach is to mine latent skills from assessment items included in existing courseware, provide discovered skills with human-friendly labels, and map didactic paragraph texts with skills. This way, mapping between assessment items and paragraph texts is formed. In doing so, automated skill models produced by Smart will reduce the workload of courseware developers while enabling adaptive online content at the launch of the course. In our evaluation study, we applied Smart to two existing authentic online courses. We then compared machine-generated skill models and human-crafted skill models in terms of the accuracy of predicting students’ learning. We also evaluated the similarity between machine-generated and human-crafted skill models. The results show that student models based on Smart-generated skill models were equally predictive of students’ learning as those based on human-crafted skill models— as validated on two OLI (Open Learning Initiative) courses. Also, Smart can generate skill models that are highly similar to human-crafted models as evidenced by the normalized mutual information (NMI) values.