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Emma Howard

Publications and source records attributed to Emma Howard.

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ouladFormat R package: Preparing the Open University Learning Analytics Dataset for analysis

Analysing educational data sets is fundamental to many fields of research focusing on improving student learning. However, large educational data sets are complex and can involve intensive preprocessing. These obstacles can be overcome through the development of educational tools which simplifies the preprocessing stages of analysis. The Open University Learning Analytics Dataset (OULAD), available online, contains data from 32,593 students across 22 module presentations at the Open University. This paper introduces the R software package ouladFormat; which loads and formats the OULAD for data analysis. The paper summarizes the ouladFormat R package and explains the different functions within the package. In addition, two case studies are provided which discuss how the OULAD and ouladFormat R package could be used when preparing for an educational study, and in the early identification of at-risk students. The package increases the accessibility of the OULAD for researchers, practitioners, and educators, and supports reproducibility and comparability of educational studies.

stat.CO

Contrasting Prediction Methods for Early Warning Systems at Undergraduate Level

In this study, we investigate prediction methods for an early warning system for a large STEM undergraduate course. Recent studies have provided evidence in favour of adopting early warning systems as a means of identifying at-risk students. Many of these early warning systems rely on data from students' engagement with Learning Management Systems (LMSs). Our study examines eight prediction methods, and investigates the optimal time in a course to apply an early warning system. We present findings from a statistics university course which has a large proportion of resources on the LMS Blackboard and weekly continuous assessment. We identify weeks 5-6 of our course (half way through the semester) as an optimal time to implement an early warning system, as it allows time for the students to make changes to their study patterns whilst retaining reasonable prediction accuracy. Using detailed (fine-grained) variables, clustering and our final prediction method of BART (Bayesian Additive Regressive Trees) we are able to predict students' final grade by week 6 based on mean absolute error (MAE) to 6.5 percentage points. We provide our R code for implementation of the prediction methods used in a GitHub repository.

math.HO