Abstract
Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.
Highlights
Contemporary society is undergoing significant changes, especially the emergence of digital technologies that permeate and affect different social instances, such as the educational sphere
As indicated by Brown (2011), the advance of distance learning is characterized by the technological contribution available in Virtual Learning Environments (VLE), known as Learning Management Systems (LMS)
This article presents the results of a systematic mapping study that aims to understand how Learning Analytics (LA) and intelligent services have been applied in distance education environments
Summary
Contemporary society is undergoing significant changes, especially the emergence of digital technologies that permeate and affect different social instances, such as the educational sphere. In addition to enabling interaction and interactivity, virtual environments provide a large amount of information that can be considered valuable to analyze behavior and predict student performance, improving the educational process using appropriate data analysis techniques. Learning Analytics (LA) is defined by Chatti et al (2012) as collection, analysis, and use of large amounts of data and information from students to improve the understanding of their behaviors and contexts, and improve learning results, increasing the efficiency and effectiveness of the institution. Long and Siemens (2014) indicate that LA is defined as the measurement, collection, analysis, and reporting of data on students and their learning contexts in order to understand and optimize learning and the environment in which it occurs. This article presents the results of a systematic mapping study that aims to understand how LA and intelligent services have been applied in distance education environments.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
