In this study, we propose Euclidean best–worst method (Euclidean BWM), which does not require any other extra calculations and analysis compared to nonlinear Chebyshev BWM. Using numerical examples, we illustrate and discuss the efficiency of the Euclidean BWM based on minimizing Euclidean norm instead of Chebyshev norm and using the consistency index matrix. Obtained results show that Euclidean BWM is an efficient tool resulting in reliable unique solutions, regardless of the number of the criteria, comparing with the linear and nonlinear model of the Chebyshev BWM. Moreover, we develop a MAPLE package “BWM” using only pairwise comparison vectors as the arguments to obtain the unique solution of a given problem by both the Euclidean BWM and linear model of Chebyshev BWM.

Purpose Learning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS. Design/methodology/approach The paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations. Findings The analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones. Originality/value The study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

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The most important feature of decision problems is that they contain alternatives and criteria expressed both objectively and subjectively. Such problems are solved by multi-criteria decision-making (MCDM) methods. The difficulty, however, is that qualitative criteria cannot be modeled and measured quantitatively. There are many tools, fuzzy set, intuitionistic fuzzy set, neutrosophic set, and so on, to analyze the incompleteness and uncertainty in the data. The most important characteristic that distinguishes neutrosophic sets from these sets is that they use three membership values as truth, indeterminacy, and false. In this sense, it is superior to classical fuzzy sets. Therefore, in this study, a novel-integrated solution method based on Neutrosophic Criteria Importance Through Inter-Criteria Correlation (N-CRITIC) and Neutrosophic Additive Ratio ASsessment (N-ARAS) methods is developed for the MCDM problems by integrating Single-Valued Neutrosophic Numbers (SVNNs) into CRITIC and ARAS methods. A case study from the literature concerning the most appropriate technology forecasting method selection has been applied to present the computational details. First, N-CRITIC method is performed to find the weights of selection criteria. Then, N-ARAS method is used to determine the ranking order of technology forecasting methods and select the optimal one. The sensitivity and comparative analyses have also proved that the novel-integrated solution method gives a consistent ranking for the alternatives.

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