Big-data Inquiry: Thinking with Data By Allie Alayan for AACE Review, May 6th 2018 The Innovating Pedagogy 2017 Report included Big-data inquiry as one of its innovative trends that has the potential to provoke major shifts in educational practice. The 2017 Innovating Pedagogy Report stated that Big-data inquiry has a potential impact level of medium, and has a long timescale (4+ years). This post discusses big-data inquiry strategies for use with students in various educational formats, as well as challenges and concerns surrounding the big-data movement. Why is interacting with and educating on big data important? Since a vast amount of data is available through the internet, students need to be able to understand big data and its uses in order to be active citizens and learners, and to be able to ask the correct questions to be good consumers of science and be effectively data literate. Students need to be prepared to work with big data that is commonly found in a variety of settings, including academic and research settings. Educational Opportunities: What are the educational opportunities of the big data movement? Big-Data Inquiry Tasks: Since the sheer amount of data available can be so confusing, it is important for students to have opportunities to sort through this data in meaningful ways. In these tasks, students should be able to participate in ‘data moves’. These include: Integrating Data from various sources and cleaning it Reorganizing Data Making new or unusual visualizations Defining new variables and designing new measures Slicing, filtering, or otherwise choosing cases Data Education: Data Education needs to focus on providing students with the experiences and opportunities to develop conceptual understanding of data and data processes instead of simply focusing on computation. Purposeful Data Modelling Inquires: Data modelling allows for students to see the link between data and context in order to practice developing and generalizing different statistical tools and ideas. Interdisciplinary: When working with data, there are a variety of differentiated skills that are involved, including those in the fields of Statistics, Computer Science, and Mathematics. These skills include: Accessing and organizing data in databases Scraping data from websites Processing text into data that can be analyzed Ensuring secure and confidential data storage Educational Policy: Big Data has the potential to help to inform educational policy making and implementation. Through data collection processes, public opinion posts available online can be used to help implement change and inform policymakers of needed changes (Wang, 2016). What are concerns and challenges surrounding the big data movement? Bias and Discrimination: When algorithms are given certain jobs involving data, unintended discrimination against particular groups can arise. Data scientist Cathy O’Neil says “Big data doesn’t eliminate bias, we’re just camouflaging it with technology.” Cathy O’Neil has written a book on this topic entitled Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. An article introducing some of the main themes of her book can be found here: http://bit.ly/2eYuH4o. Informed Consent and Ethical Concerns: Accessing public data digitally can be used for educational or research purposes, but this data being collected without informed consent can raise concerns surrounding ethical boundaries of collecting data and conducting research with such data (Wang, 2016). When working with students, it is important to help them cultivate their ethical awareness related to big data (Chen, 2015). Projects and tools: The Common Online Data Analysis Platform (CODAP) is a tool that students (secondary level and above) can use to explore real data, and can be used by students to easily create various visualizations of their data. CODAP is free and is web-based. Data can be imported into the tool or a simulation that generates data can be used. CODAP can be used in various aspects of curriculum to help students gain experience working with data. More information about the CODAP tool and its use in schools can be found here: http://www.oceansofdata.org/projects/common-online-data-analysis-platform-codap. The Ocean Tracks project is a large dataset that includes data on the paths of animals living in the Pacific Ocean. Students who use the dataset available through the Ocean Tracks project, look at the ways that marine animals migrate across the Pacific Ocean, and how their migrations and movements are shaped by their surrounding environments. More information about the Ocean Tracks project and interface can be found here: http://oceantracks.org/about. Tableau is a tool that is focused on commercial data which enables paying customers to use visual analytics for data exploration. More information on Tableau can be found here: https://www.tableau.com. References: Chen, X. (2015). Developing STEM Students’ Big Data Ethics Awareness. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia 2015–World Conference on Educational Media and Technology (pp. 245-248). Montreal, Quebec, Canada: Association for the Advancement of Computing in Education (AACE). Wang, Y. (2016). Big Opportunities and Big Concerns of Big Data in Education. TechTrends: Linking Research and Practice to Improve Learning, 60(4), 381-384.