YouTube for Transcribing and Google Drive for Collaborative Coding: Cost-Effective Tools for Collecting and Analyzing Interview Data

Cloud-based tools are increasingly used in research processes. In this paper, we illustrate the practice of one research team making use of multiple cloud-based applications in preparing, analyzing, and sharing research data, as well as in collaborative writing and display of results. Important research ethics considerations are also explored as a foundation for this practice. We believe that our detailed description of the steps involved can be of help to researchers, particularly novice researchers who may lack research funds to have qualitative interviews transcribed. This mashed-up use of free cloud-based software makes data preparation from qualitative interviews cost-effective, more efficient, thorough, and collaborative.

Fostering Computational Thinking in Homes and Other Informal Learning Spaces

This chapter provides theoretical and practical insights for fostering children's computational thinking (CT) in homes and other family-friendly spaces such as libraries, museums, and after-school programs. The family context—the kinds of roles, interactions, and opportunities afforded by parents, caregivers, and siblings—is essential for understanding how young children learn and engage in CT. This work is informed by research on how everyday activities and educational technologies (and the contexts in which they are used) can be designed to promote opportunities for CT and family engagement. This chapter discusses ways to support children's CT by co-engaging family members in collaborative coding activities in homes and other informal learning spaces.

BurstBiRank: Co-Ranking Developers and Projects in GitHub with Complex Network Structures and Bursty Interactions

With the wide adoption of social collaborative coding, more and more developers participate and collaborate on platforms such as GitHub through rich social and technical relationships, forming a large-scale complex technical system. Like the functionalities of critical nodes in other complex systems, influential developers and projects usually play an important role in driving this technical system to more optimized states with higher efficiency for software development, which makes it a meaningful research direction on identifying influential developers and projects in social collaborative coding platforms. However, traditional ranking methods seldom take into account the continuous interactions and the driving forces of human dynamics. In this paper, we combine the bursty interactions and the bipartite network structure between developers and projects and propose the BurstBiRank model. Firstly, the burstiness between each pair of developers and projects is calculated. Secondly, a weighted developer-project bipartite network is constructed using the burstiness as weight. Finally, an iterative score diffusion process is applied to this bipartite network and a final ranking score is obtained at the stationary state. The real-world case study on GitHub demonstrates the effectiveness of our proposed BurstBiRank and the outperformance of traditional ranking methods.

M-BiRank: co-ranking developers and projects using multiple developer-project interactions in open source software community

Social collaborative coding is a popular trend in software development, and such platforms as GitHub provide rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection, and collaboration. Thus, identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue, and watch are extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real-world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods, and multiplex ranking method.

M-BiRank: Co-ranking developers and projects using multiple developer-project interactions in open source software community

Social collaborative coding is a popular trend in software development and such platforms as GitHub provides rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection and collaboration. Thus identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue and watch is extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods as well as multiplex ranking method.