# Using Virtual Learning Environment Data for the Development of Institutional Educational Policies
## 2 Related Work  
This section presents an overview of the research problem topic. Also in this section, Table 1 presents a summary of the aboarded studies. [PERSON] et al. [21] presented a practical tool that can be used to identify the risks and challenges that arise when implementing LA and explained how to approach the same. The authors propose a framework with seven main categories for LA initiatives: Purpose and Gain, Representation and Actions, Data, IT Infrastructure, Development and Operation, Privacy, and Ethics. They remarked that the order of implementation depends on each institution. The Data dimension encompasses the application of the advantages of modern technology and the various data sources available, looking for the right analysis to improve the quality of learning and teaching, as well as to enhance the chances of student success. In a global context, the prediction of performance and dropout is concentrated at the university level, with about 70% of the research focused on this purpose [22]. This trend is the same in Latin America [23]; however, according to [1], Latin American universities still have considerably lower adoption rates compared to institutions in other regions. Thus, Latin American educational institutions can use LA to combat disparities in teaching quality, performance problems, and high dropout rates. The potential of using predictive methods in education has already been demonstrated by numerous works in the literature [4; 14; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Our work focuses on the data dimension, as it is essential to analyze practical case studies and understand which are the key metrics and the processes they are applying. As there is already another work summarizing the important findings up to 2017 (i.e., [9]), we concentrated our exploratory search on papers after 2017. The systematic review from [9] covered the most relevant studies related to four main dimensions: computer-supported learning analytics, computer-supported predictive analytics, computer-supported behavioral analytics, and computer-supported visualization analytics from 2000 to 2017. The authors identified twelve relevant EDM/LA techniques that researchers normally combine: classification (26.25%), clustering (21.25%), visual data mining (15%), statistics (14.25%), association rule mining (14%), regression (10.25%), sequential pattern mining (6.50%), text mining (4.75%), correlation mining (3%), outlier detection (2.25%), causal mining (1%), and density estimation (1%). Searching EDM/LA works after 2017, we found research applying different techniques and using different sources of data that we mention here. A practical application of early prediction is proposed by [29]. The authors implemented an alert system to predict performance in some classes at the university. The research demonstrated that the use of predictive methods in education allowed an increase of up to 15% on te students' performance compared to those in classes that did not use the models. [PERSON] et al. [27] proposed the use of the Learning Analytics Dashboard for Advisers (LADA) as a tool to support the learning process and the students' final success. This tool seeks to assist educational counselors in the decision-making process through comparative and predictive analyses of the student data. The use of the predictive methods of this tool showed significant results, especially in complex cases, in student success. [PERSON] and [PERSON] [26] investigated the effectiveness of LA in identifying at-risk students in higher education. To this end, the authors compared the low-engagement alerts of an LA tool with the results of students at the end of the first year of graduation. In addition, different methodologies for generating alerts have been compared, such as the use of demographic data and only VLE participation data. The tests demonstrated that the VLE-data approach was more efficient at generating alerts than using socio-demographic data.