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.

Purpose: Predicting learner outcomes in blended learning (BL) is a new problem with many challenges, as learner data must be collected in both face-to-face and online environments. The purpose of this article is to identify the best method for building a model to predict student performance in BL and to determine the appropriate time for early prediction. Design/methodology/approach: In this study, the authors propose a process for building a model to predict students’ learning outcomes early in BL. We will identify important features, select appropriate machine learning algorithms to build prediction models and determine suitable early prediction times. We collected learner behavior data at various times in two courses: general information (GI) and data structures and algorithms (DSA), with 746 and 102 learners, respectively. Findings: Experimental results show that the two most suitable algorithms for building prediction models are linear regression and random forest classification algorithms. Additionally, the research reveals that offline classroom behaviors such as attending class diligently and taking quizzes contribute to improving the efficiency of prediction models. Moreover, early prediction can be done from the eighth week of the semester with an efficiency that is not much different from predicting using the whole course data. Originality/value: Early prediction of learning outcomes allows educators to adjust teaching plans and warn students, thereby improving the quality of teaching and learning. Although the research was only conducted in small-scale experimental courses, the results are positive and can be applied to other blended courses with the same teaching scenario.

The goal of extracting linguistic data summaries is to produce summary sentences expressed in natural language which represent knowledge hidden in numerical dataset. At the most general level, human users can get a very large number of linguistic summaries. In this paper, we propose a model of genetic algorithm combined with greedy strategy to extract an optimal set of linguistic summaries based on the evaluation measures of goodness and diversity of the set of linguistic summaries. The experimental results on creep dataset have demonstrated the outperformance of the proposed model of genetic algorithm combined with greedy strategy in comparison with the existing genetic algorithm models in extracting linguistic summaries from data.