How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Abstract

Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

“…Therefore, it is not wrong to pay attention to the needs of patients and design from their point of view in the logo design of art therapy centers. Among them, data cleaning can be used to remove the noise present in the data and correct the inconsistency problem; data set can merge data from multiple data sources into a complete and consistent data store; data transformation can compress the data to a smaller interval, thus contributing to the efficiency of mining algorithms using distance metrics and the accuracy of the executed results; remove redundant data by clustering and reduce data size by clustering [ 12 ]. The various data preprocessing methods described earlier can be used simultaneously or selectively, to obtain high-quality data after professional and scientific processing, thus preparing for high-quality mining results and thus producing good decisions.…”

Acquiring, Analyzing and Interpreting Knowledge Data for Sustainable Engineering Education: An Experimental Study Using YouTube

“…Although the authors of [25] conducted a systematic review of the literature describing the sources and use of educational data, data analyses from the YouTube channel were unfortunately not considered. Additionally, in [26], the researchers examined the influence of instructor-generated video content on student engagement and participation in a course using the number of posts per week and the number of characters per post as parameters.…”

Forecasting Students Dropout: A UTAD University Study

“…On the other hand, their main focus is providing early prediction instead, including ranking and forecasting mechanisms on addressing the dropout student's problem. De Oliveira et al [16] searched scientific indexed publications in higher education to analyze the retention and dropout of higher education students. They identified the data and techniques used and proposed a classifier using several categories considering several student and external features.…”