Abstract:

This article examines the work on the application of data mining tools in learning management systems (LMS). The purpose of this paper is to introduce the concept of rigorous reviews of current empirical evidence to the educational data mining (EDM) community. Proposing a method for extracting information from records, analyzing them statistically, and interpreting that information into useful knowledge; how they interact with regard to positive impacts, especially with regard to learning and improving skills in those involved. This work is aimed at researchers who focus their studies on e-learning and the use of data mining tools.

The search terms identified articles that provided empirical evidence on the impacts and results in improving e-learning when applying a data mining method. The results are aligned to learning with a multidimensional approach to categorize articles based on citation indexes that can be analyzed to determine the popularity and impact of specific articles, authors, and publications. The findings revealed that mining data to measure levels of student learning, can make adjustments to the parameters of evaluation and resources available to the student. The adjustments made in the parameters generate a range of impacts and perceptual, cognitive, behavioral, affective and motivational results. These parameters were considered as tools for the classification of articles according to the acquisition of knowledge, understanding of content and motivational results. Along with the methodological limitations and the recommendations to continue working in this line.

The structure of the work is based on the following description:
section 1 provides an introduction to systematic reviews as a meaningful research method,
section 2 specifies the steps of a systematic review,
section 3 examines the planning stages of a systematic review,
section 4 examines the stages of conducting a systematic review,
section 5 discusses the reporting of a systematic review.

Keywords:

Data mining, moodle, collaborative learning, edm.