Analysis of Big Data Literacy Skills of Prospective Mathematics Educators Through Case Method-Based Learning

Abstract

The aim of this research is to analyze the big data literacy skills that a prospective mathematics educator needs to master. Specifically, the ability to read, analyze, make conclusions, and think based on data and information, especially large amounts of data, so that mathematics education students have a broad mindset, and can carry out new innovations. Kemmis and McTaggart's Classroom Action research model was used as the research method. Its stages have four, namely planning, action, observation, and reflection. The planning and action stages took the form of preparing and validating case method-based learning tools in the Operations Research course and were implemented in 3 classes. Lectures are divided into 3 stages: problem-based learning, case method learning from the business world, and case-based learning (Case Method). During the learning process, observation and reflection are carried out. Learning involving the business world, namely Astra Financial and an expert in data sciences, provides opportunities for students to process company big data. The data literacy ability indicators used are the ability to group data, check data, clean data, do qualitative coding, eliminate data, and develop models. The learning results show that students have carried out all big data processing steps in the very good category (> 80%), except for the developing model phase (65.38%). Mini research field studies at partner institutions for the application of operations research in the community. Learning completeness reached 83.11%, and learning activeness reached 82.14% in the very active category. Learning results show an increase in the Big Data Literacy skills of prospective educators and lecturers, even though mastery of big data processing software still needs to be improved.