The linguistic summarization of data is one of the study trends in data mining because it has many useful practical applications. A linguistic summarization of data aims to extract an optimal set of linguistic summaries from numeric data. The blended learning format is now popular in higher education at both undergraduate and graduate levels. A lot of techniques in machine learning, such as classification, regression, clustering, and forecasting, have been applied to evaluate learning activities or predict the learning outcomes of students. However, few studies have been examined to transform the data of blended learning courses into the knowledge represented as linguistic summaries. This paper proposes a method of linguistic summarization of blended learning data collected from a learning management system to extract compact sets of interpretable linguistic summaries for understanding the common rules of blended learning courses by utilizing enlarged hedge algebras. Those extracted linguistic summaries in the form of sentences in natural language are easy to understand for humans. Furthermore, a method of detecting the exceptional cases or outliers of the learning courses based on linguistic summaries expressing common rules in different scenarios is also proposed. The experimental results on two real-world datasets of two learning courses of Discrete Mathematics and Introduction to Computer Science show that the proposed methods have promising practical applications. They can help students and lecturers find the best way to enhance their learning methods and teaching style.

Forecasting methods based on fuzzy time series have been examined intensively during last years. Three main factors which affect the accuracy of those forecasting methods are length of intervals, the way of establishing fuzzy logical relationship groups, and defuzzification techniques. Many researches focus on optimizing length of intervals in order to improve forecasting accuracies by utilizing various optimization techniques. In the line of that research trend, in this paper, a hybrid particle swarm optimization combined with simulated annealing (PSO-SA) algorithm is proposed to optimize length of intervals to improve forecasting accuracies. The experimental results in comparison with the existing forecasting models show that the proposed forecasting model is an effective forecasting model.