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Knowledge discovery through higher education census data

机译：通过高等教育普查数据发现知识

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Universities have three core missions: teaching, researching, and public service. Even though the Education environment has become more marketized, to survive, the Higher Education Institutions (HEIs) must behave like non-profit organizations, prioritizing revenue creation, the public good and serving as providers of value for society through creation and dissemination of knowledge and educational development Kaplan (Kaplan, 2016; Huggins and Prokop, 2016; Huggins and Thompson, 2014). Concerning that knowledge is the main driver for future social and economic development in the Knowledge Economy and Society, this paper analyzes the data from Brazilian Higher Education Census based on HEIs, their undergraduate courses, professors, and students. It uses Self-Organizing Maps (SOM), a type of neural network which deals with a massive volume of data, to explore patterns hidden in the data. The goal of the paper is to discover knowledge innovatively in the Education Area. As a result, it assesses the HEIs internal dynamics and, according to the Resource-Based View (RBV) theory, it presents HEIs with similar, dissimilar or complementary resources. This identification raises new forms of relationships based on the combination of resources among institutions, which allow them to become more entrepreneurial and behave more collaboratively. This new knowledge plays a significant role in the implementation of competitive responses or decisions to take and contributes to advance the RBV Theory.
机译：大学的三个核心使命是：教学，研究和公共服务。即使教育环境已经市场化，为了生存，高等教育机构（HEI）必须像非营利组织那样行事，优先创造收入，公益和通过创造和传播知识与知识为社会提供价值。教育发展卡普兰（Kaplan，2016； Huggins和Prokop，2016； Huggins和Thompson，2014）。考虑到知识是知识经济和社会未来社会和经济发展的主要驱动力，本文根据高校，其本科课程，教授和学生对巴西高等教育普查数据进行了分析。它使用自组织映射（SOM）（一种处理大量数据的神经网络）来探索隐藏在数据中的模式。本文的目的是在教育领域创新地发现知识。结果，它评估了HEI的内部动态，并根据基于资源的视图（RBV）理论，向HEI展示了具有相似，相异或互补的资源。这种识别基于机构之间的资源组合而产生了新的关系形式，这使它们变得更具企业家精神，并且表现出更加协作的行为。这些新知识在实施竞争性对策或采取的决策中起着重要作用，并为推进RBV理论做出了贡献。

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