Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

Abstract

Virtual learning environments facilitate online learning, generating and storing large amounts of data during the learning/teaching process. This stored data enables extraction of valuable information using data mining. In this article, we present a systematic mapping, containing 42 papers, where data mining techniques are applied to predict students performance using Moodle data. Results show that decision trees are the most used classification approach. Furthermore, students interactions in forums are the main Moodle attribute analyzed by researchers.This systematic mapping aims to identify what type of analysis is being used to predict students' performance on Moodle, using data mining. It is organized as follows:

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

“…Of the 15 tools reported in 11 articles, the most cited was the Moodle (Modular Object-Oriented Dynamic Learning Environment), a Virtual Learning Environment created in 1999 and multiplatform developed collaboratively by a virtual community spread around the world. According to Felix et al (2018), Moodle has been the most widely used opensource virtual learning environment for distance education worldwide. Other important considerations of the tools: 5 different MOOC's are used, which represents a great acceptance of this platform in the distance learning and teaching process; and Oeda and Hashimoto (2017) are not specific in the identification; Imran et al (2019) use two different MOOC for this purpose.…”

How are my Students going? A Tool to Analyse Students' Interactions on Capstone Courses

“…Authors argue that the framework reveals valuable information to help professors to monitor the online learning processes [Özdagoglu et al 2018]. Other researchers proposed software tools to promote the educational progress accompaniment in different scenarios, like [Felix et al 2018, Porfirio et al 2018].…”