Prediction of student academic performance using Moodle data from a Further Education setting.

Abstract

Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p<0.008) though more than half of failing students were still misclassified. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of on an early-warning system at ten weeks.

Learning Management System (LMS) Use with Online Instruction

“…A proprietary system uses an exclusive code where schools purchase a license or subscription to access and use the LMS features (Kimmons et al, 2019). Open-source systems use a free license with no cost where users have the freedom to access and use the system (Kimmons et al, 2019;Quinn & Gray, 2020;Turnbull et al, 2019).…”

“…Open-source systems use a free license at no cost where users have the freedom to access and use the system (Kimmons et al, 2019;Turnbull et al, 2019). Examples of open-source systems include Moodle, Wordpress, and Drupal (Kehrwald & Parker, 2019;Kimmons et al, 2019;Quinn & Gray, 2020).…”

“…Educational entrepreneurs are promoting open-source systems to drive online learning environments that use a variety of media and communication tools and support learner choice in the selection and use of tools for online learning (Kehrwald & Parker, 2019;Quinn & Gray, 2020). These open-source systems continue to evolve to accommodate the possibility of their system existing only in web-based settings (Turnbull et al, 2019).…”

The Most Efficient Classifiers for the Students’ Academic Dataset

“…They concluded that the k-Nearest Neighbor got better accuracy than the other. Quinn and Gray [20] used data from the Moodle to predict students 'grades whether they will succeed or fail in the course. They applied the classification algorithms Random Forest, Gradient Boosting, k-Nearest Neighbours and Linear Discriminant Analysis using R. They summarized that the use of data from Moodle gives the ability to early detection of students at risk.…”

Deep Neural Network Model for Identification of Predictive Variables and Evaluation of Student’s Academic Performance

“…Most institutions are having a hard time attracting new students due to more competitive educational practises [6]. Students' academic performance must be evaluated in order to encourage growth [7] and improve higher education quality. Numerous factors, including the student's socioeconomic background [8] and previous academic accomplishment, may have an impact on their academic performance, complicating the picture.…”