大数据作为科学教学中介：一项建议
Big Data as a Mediator in Science Teaching: A Proposal
摘要：
我们生活在一个数字世界，在2010年，超过了一个泽字节的数据。通过优化技术，在计算机上以极快的速度处理大量数据，使人们能够在新的和新兴的数据和内容类型中找到见解，并回答以前认为无法解决的问题。这就是大数据的概念。Google现在提供了Google Correlate分析公共工具，该工具根据搜索词或一系列时间或区域数据，提供了Google上的查询列表，其频率遵循与数据最相关的模式，根据Pearson决定系数R2。当然，相关性并不意味着因果关系。然而，我们相信，从教学甚至科学的角度来看，这些大数据工具有可能发现意想不到的相关性，这些相关性可能成为有趣现象的线索。据我们所知，这是在科学教学中使用大数据的第一个建议，具有建构主义的特征，将计算机和公共和免费工具（如Google Correlate）作为中介。它也有一个认识论的偏见，不仅仅是在计算基础设施或预测分析的培训，但旨在为学生提供更好地理解物理概念，如现象，观察，测量，物理定律，理论和因果关系。有了它，他们将能够成为优秀的大数据专家，解决大数据挑战的“数据科学家”。
英文摘要：
We live in a digital world that, in 2010, crossed the mark of one zettabyte data. This huge amount of data processed on computers extremely fast with optimized techniques allows one to find insights in new and emerging types of data and content and to answer questions that were previously considered beyond reach. This is the idea of Big Data. Google now offers the Google Correlate analysis public tool that, from a search term or a series of temporal or regional data, provides a list of queries on Google whose frequencies follow patterns that best correlate with the data, according to the Pearson determination coefficient R2. Of course, correlation does not imply causation. We believe, however, that there is potential for these big data tools to find unexpected correlations that may serve as clues to interesting phenomena, from the pedagogical and even scientific point of view. As far as we know, this is the first proposal for the use of Big Data in Science Teaching, of constructionist character, taking as mediators the computer and the public and free tools such as Google Correlate. It also has an epistemological bias, not being merely a training in computational infrastructure or predictive analytics, but aiming at providing students a better understanding of physical concepts, such as phenomena, observation, measurement, physical laws, theory, and causality. With it, they would be able to become good Big Data specialists, the so needed 'data scientists' to solve the challenges of Big Data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
