William Villegas-Ch

Analysis of data mining techniques applied to LMS for personalized education

This article describes the models and the use of data mining techniques applied to Learning Management Systems (LMS) which allow institutions to offer the student a personalized education. It considers the ways in which the concepts of educational data mining (EDM) are applied to the information extracted from the LMS. The data from these systems can be evaluated to convert the information collected into useful information to provide an education tailored to the needs of each student. This approach seeks to improve the effectiveness and efficiency of education by recognizing patterns in student performance. This article presents an analysis of the data mining techniques that fit LMS, specifically in terms of a case study applied to the e-learning platform Moodle. The objective is to provide stakeholders with guidance on the use of EDM tools.

Big Data, the Next Step in the Evolution of Educational Data Analysis

This paper presents an analysis of new concepts such as big data, smart data and a data lake. It is to sought integrate learning management systems with these platforms and contribute to education by making it personalised and of quality. For this study, the data and needs of a university in Ecuador have been considered. This university has set its goals to the discovery of patterns, using data mining techniques applied to cubes generated in a data warehouse. However, the institution wants to integrate all the systems and sensors that contribute to the educational development of the student. Integrating more systems into the data warehouse has compromised the veracity of the data and the processing capabilities have been surpassed by the volume of data. The paper proposes the use of one of the platforms analysed and its tools to generate knowledge and to help the students to learn.

Improvement of massive open online courses by text mining of students' emails: a case study

In recent years, the constant increase in the number of online courses has led to radical changes in the education sector. These new online learning environments present a series of challenges that are difficult to manage using traditional methods. The challenges relate to the level of commitment and motivation shown by students on this type of course. Several articles have been identified from the analysed literature related to the application of text or opinion mining techniques for the analysis of comments made in social networks. In the educational field, articles related to the topic that focus on the analysis of opinion have been identified based on entries included in discussion forums for online courses. Many publications are geared towards solutions in the English language, and the nature of linguistic analysis of this type of study makes it necessary to adapt them for languages other than English. In this paper, we explore the opinion mining through text mining in emails from Massive Open Online Courses (MOOC). The opinion mining expressed in emails is a complex task due to the thematic disparity of emails, their size and the depth of linguistic analysis required. The purpose of this study is to analyse students opinions about their courses, their instructors, and the main tools used on the course. The research focus on the calculation and analysis of the frequency of terms, the analysis of concordances, groupings and n-grams. The case study used in this paper is a MOOC on the topic of web development with more than 40,000 enrolled students.

Data mining toolkit for extraction of knowledge from LMS

Today, information technology (IT) is an active part of education. Its main impact is in the administration of learning management systems (LMS). The support provided by IT in LMS has generated greater dexterity in the evaluation of the quality of education. The evaluation process usually includes the use of tools applied to online analytical processing (OLAP). The application of OLAP allows the consultation of large amounts of data. Data mining algorithms can be applied to the data collected to perform a pattern analysis. The potential use of these tools has resulted in their specialization, both in the presentation and in the algorithmic techniques, allowing the possibility of educational data mining (EDM). EDM seeks to enhance or customize education within LMS by classifying groups of students in terms of similar characteristics that require specific resources. The ease of use and extensive information about some of the EDM tools has caused many educational institutions to consider them for their own use. However, these institutions often make errors in data management. Errors in the use of data mean that the improvements in LMS are inadequate. The work described in this paper provides a guide on the use of applied methodology in the process of knowledge extraction (KDD). It also enumerates some of the tools that can be used for each step of the process.