Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies’ application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students’ abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.
A high quality review of the distance learning literature from 1992-1999 concluded that most of the research on distance learning had serious methodological flaws. This paper presents the results of a small-scale replication of that review. A sample of 66 articles was drawn from three leading distance education journals. Those articles were categorized by study type, and the experimental or quasi-experimental articles were analyzed in terms of their research methodologies. The results indicated that the sample of post-1999 articles had the same methodological flaws as the sample of pre-1999 articles: most participants were not randomly selected, extraneous variables and reactive effects were not controlled for, and the validity and reliability of measures were not reported.