We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Computer Science


Publication details for Mr Ahmed Alamri

Cristea, A. I., Alshehri, M., Alamri, A., Kayama, M., Stewart, C. & Shi, L (2018), How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers, in Andersson, B., Johansson, B., Carlsson, S., Barry, C., Lang, M., Linger, H. & Schneider, C. eds, 27th International Conference on Information Systems Development (ISD2018). Lund, Association for Information Systems, 6.

Author(s) from Durham


Data-intensive analysis of massive open online courses (MOOCs) is popular. Researchers have been proposing various parameters conducive to analysis and prediction of student behaviour and outcomes in MOOCs, as well as different methods to analyse and use these parameters, ranging from statistics, to NLP, to ML, and even graph analysis. In this paper, we focus on patterns to be extracted, and apply systematic data analysis methods in one of the few genuinely large-scale data collection of 5 MOOCs, spread over 21 runs, on FutureLearn, a UK-based MOOCs provider, that, whilst offering a broad range of courses from many universities, NGOs and other institutions, has been less evaluated, in comparison to, e.g., its American counterparts. We analyse temporal quiz solving patterns; specifically, the less explored issue on how the first number of weeks of data predicts activities in the last weeks; we also address the classical MOOC question on the completion chance. Finally, we discuss the type of feedback a teacher or designer could receive on their MOOCs, in terms of fine-grained analysis of their material, and what personalisation could be provided to a student.