Selection and Construction of Evaluation Indicators and System for Geography Graduate Courses Based on Teaching Big Data

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Educational big data analysis is one of the current focal points in academia. The involvement of big data has enriched the functions of educational evaluation, expanding and deepening the connotations and functionalities of education and teaching itself. This development directly supports scientific decision-making in education, gradually becoming an important technological force driving the advancement of higher education. As a crucial component of higher education, the quality of course instruction at the graduate level directly affects the overall quality and standards of graduate education. This study focuses on the evaluation of graduate classroom teaching based on educational big data, examining the current issues in graduate course evaluation systems. By concentrating on geography graduate courses, it builds a comprehensive framework for data collection, quantification, and analysis, addressing specific teaching challenges. The research explores the selection of evaluation indicators and the establishment of a systematic framework, developing a big data-mining model based on various statistical methods. Using "Frontiers in Geographic Information Systems" as a case study, the research demonstrates favorable outcomes.