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The #iteachmsu Commons is an initiative that has been in the making for almost five years, driven by educators, for educators. Initially, Melissa McDaniels of The Graduate School and a group of graduate Teaching Assistants launched “Inside Teaching MSU” as a space for graduate student educators to share their teaching ideas and learn from each other through workshops. Inside Teaching MSU’s efforts eventually expanded to social media, using #iteachmsu (both the shortened version of the organization’s name and a public declaration of teaching practice) to continue conversations around teaching and learning at MSU without barriers of time and physical space. Ultimately, the group launching a blog with original graduate student content and began hosting twitter EdChats to expand opportunities for educators across MSU to connect. It became apparent that educators across campus roles, including but not limited to faculty, staff, advisors, specialists, librarians, instructional designers, teaching assistants, and undergraduate learning assistants, wanted a dedicated space to further their work in the name of MSU’s teaching and learning mission.
 
“It is important to have a space for all educators to connect at MSU because we are often dispersed and siloed in our respective colleges and departments. It is even more important for all of us here at MSU to identify ourselves as educators with vested interest in the work of teaching and learning at our pioneer land-grant institution,” says #iteachmsu Commons project team member and Learning Experience Designer Dave Goodrich. MSU is a big place and with educators situated in so many diverse roles across campus, making it difficult to fully recognize and leverage this community’s teaching and learning innovations. To better gather and guide the best of central support and decentralized innovation, a teaching commons was initiated. According to Huber and Hutchings (2016), a Teaching Commons is “an emergent conceptual space for exchange and community among faculty, students, and all others committed to learning as an essential activity of life in contemporary democratic society”. Erik Skogsberg, #iteachmsu Commons project lead, hopes that “The #iteachmsu Commons will provide the public gathering place to best center educator development in effective teaching practices across our campus.” We envision this centering as achieved by educator-driven space for sharing teaching resources, connecting across educator networks, and growing teaching practice.
 

Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques and frameworks to process. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, and velocity. You can learn more about Big Data, Hadoop and Mapreduce here In this tutorial we will learn, Testing Big Data application is more a verification of its data processing rather than testing the individual features of the software product. When it comes to Big data testing, performance and functional testing are the key. In Big data testing QA engineers verify the successful processing of terabytes of data using commodity cluster and other supportive components. It demands a high level of testing skills as the processing is very fast. Processing may be of three types Along with this, data quality is also an important factor in big data testing. Before testing the application, it is necessary to check the quality of data and should be considered as a part of database testing. It involves checking various characteristics like conformity, accuracy, duplication, consistency, validity, data completeness, etc.

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