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Under the background of big data, the evaluation of college students' satisfaction with online ideological and political education (IAPE) is primarily achieved through data mining and analysis techniques, allowing for a more comprehensive and accurate reflection of students' attitudes and feedback. By collecting data through online surveys, social media interactions, and other channels, educators can adjust the content and methods of teaching in real-time to better meet students' needs and improve the effectiveness and satisfaction of IAPE. The satisfaction evaluation of college students' online IAPE in the context of big data is a multiattribute group decision-making (MAGDM) problem. Recently, VIKOR method have been applied to address MAGDM challenges. Single-valued neutrosophic sets (SVNSs) are employed as a tool to represent uncertain data in the satisfaction evaluation of college students' online IAPE within the big data context. In this paper, we propose the single-valued neutrosophic number VIKOR (SVNN-VIKOR) method to solve MAGDM problems under SVNSs. Finally, a numerical case study is presented to validate the effectiveness of the proposed method in evaluating the satisfaction of college students' online IAPE in the context of big data. [ABSTRACT FROM AUTHOR]

Knowledge flow is an explicit behavior of college students during their Collaborative Innovative Learning (CIL). Existing studies on the management mechanism of knowledge flow on CIL platforms generally lack a systematic theoretical analysis on the action mechanism of the influencing factors, few scholars have viewed the problems of the management mechanism of knowledge flow, the action mechanisms of the influencing factors of knowledge flow, the efficiency of knowledge flow, and the relationship among them from the perspective of the platform, and few studies have discussed the impact of the management mechanism of CIL platforms on the knowledge flow from a dynamic angle. To fill in these research blanks, this paper aims to analyze the multi-factor action mechanism of knowledge flow on CIL platforms for college students. At first, this paper elaborated on the formation mechanism of the CIL network, and measured model variables of the proposed network; then, it analyzed the robustness of the functions of the proposed network based on knowledge flow; at last, this paper gave the multi-factor variable analysis results of the model. [ABSTRACT FROM AUTHOR]