Synthesis
According to an article published by the American Educational Research Association's (AERA) Review of Research in Education, "big data" refers to the rapid collection of enormous volumes of data from several sources. Information is gathered through student information systems (SIS). Background, program, performance, and demographic data are just few of the categories that can be collected with this tool (Fischer et al., 2020). Learning management systems (LMS) like Moodle, Blackboard, and Canva are also a source of big data. The LMS records student activity and time spent on activities (Fischer et al., 2020).
Big data can be broken down in three levels and several components. Below is a chart that my also double as a resource that I created with the information from the “Mining Big Data in education: Affordances and challenges” by Fischer et al., 2020.
Obstacles
There are several obstacles to overcome when working with big data: acquiring big data from platforms that were not built for this kind of research, privacy concerns including ensuring that data is not used for marketing campaigns, stereotyping, or profiling students. Evaluating large amounts of data requires a unique set of research skills that are in short supply. The mistake rates and noise levels are problematic for those who are skilled in data analytics. Big data, for instance, can help explain a phenomenon but may not help predict its future occurrence. The risk of bias must also be considered (Fischer et al., 2020).
Recommendations
Universities should start teaching students how to gather, analyze, and use large amounts of data at the graduate level. There is a need for education and training that incorporates multiple disciplines, as well as computer or data science (Fischer et al., 2020).
Opinion and Interest
What are instructors' perspectives on big data? Although I recognize the potential benefits of using big data to tailor lessons to each student's requirements, I also recognize that instructors already have a lot on their plates. Data analytics shouldn't be part of a teacher's job, in my opinion. It may be more viable to have a specialized data analyst or team of analysts who work with teachers to give them the meaningful data insights. But educators have first-hand knowledge of their student' unique requirements, which data just cannot meet.
I'm interested in finding out how well artificial intelligence algorithms like Chat GPT perform when given a large dataset to evaluate.
Resources
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smith, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020, April 21). Mining Big Data in education: Affordances and challenges. Sage Journals. Retrieved April 11, 2023, from https://journals.sagepub.com/doi/full/10.3102/0091732X20903304
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