SuperHyperSoft Set with MCDM Methods for Assessment of Ideological and Political Education in Colleges and Universities Under the Background of Big Data

The integration of big data into ideological and political education (IPE) in colleges and universities has reshaped traditional teaching methodologies, offering new ways to analyze student behavior, personalizing learning experiences, and enhance educational outcomes. As digital transformation accelerates, the need for an effective evaluation system to assess the impact of big data-driven IPE becomes essential. This paper uses the multicriteria decision making (MCDM) methodology for evaluating this problem. Two MCDM methods are used such as CRITIC methodology to compute the criteria weights and the MABAC methodology to rank the alternatives. We use the SuperHyperSoft set to deal with criteria and sub criteria. This study uses eight criteria and seven alternatives. We introduce the ranking of alternatives into four HyperSoft sets and in each set, we obtain the rank of alternatives.