LASyM: A Learning Analytics System for MOOCs

Abstract

Abstract-Nowadays, the Web has revolutionized our vision as to how deliver courses in a radically transformed and enhanced way. Boosted by Cloud computing, the use of the Web in education has revealed new challenges and looks forward to new aspirations such as MOOCs (Massive Open Online Courses) as a technology-led revolution ushering in a new generation of learning environments. Expected to deliver effective education strategies, pedagogies and practices, which lead to student success, the massive open online courses, considered as the "linux of education", are increasingly developed by elite US institutions such MIT, Harvard and Stanford by supplying open/distance learning for large online community without paying any fees, MOOCs have the potential to enable free university-level education on an enormous scale. Nevertheless, a concern often is raised about MOOCs is that a very small proportion of learners complete the course while thousands enrol for courses. In this paper, we present LASyM, a learning analytics system for massive open online courses. The system is a Hadoop based one whose main objective is to assure Learning Analytics for MOOCs' communities as a mean to help them investigate massive raw data, generated by MOOC platforms around learning outcomes and assessments, and reveal any useful information to be used in designing learning-optimized MOOCs. To evaluate the effectiveness of the proposed system we developed a method to identify, with low latency, online learners more likely to drop out.

Demystifying Learning Analytics in Personalised Learning

“…According to Suthers [53], learning analytics is an emerging field that combines the areas of computational sciences and education, using computational techniques to capture and analyse data from within the learning environment. Learning analytics have been used for a range of applications, for example Purdue signals for student retention [54, LOCO to provide educators with feedback on students' learning activities and performance [55], and LASyM to analyse students' behaviours with the intent of increasing the impact of analytics on teaching and learning in such environments [56]. However, predicting students' learning success and providing proactive feedbacks have been two of the most frequently adopted tasks associated with learning analytics [57].…”

Real-Time Analysis of Students’ Activities on an E-Learning Platform based on Apache Spark

“…Yassine Tabaa et al [10] described a learning analytics system for MOOCs based on Hadoop cluster deployed on a private cloud. The main core component of this system is the analytics engine which relies on Map and Reduce model programming, for performing many different analytics jobs, on data that comes from relational database, by using a data integrator based on Apache Sqoop for bulk transfer data from sql sources to HDFS.…”

iMOOC on Climate Change: Evaluation of a Massive Open Online Learning Pilot Experience

“…Other learning analytics can also be used to track better online learners more likely to drop out. Tabaa and Medouri (2013) proposes a specific Learning Analytics System for MOOCs that analyses, with low latency, students' behaviour with the intent of increasing the impact of analytics on teaching and learning in such environments. In addition, longitudinal studies tracking post-MOOC outcomes such as sequences of courses taken, professional certifications obtained, or job opportunities received would help assess the longer term economic value of participating in these courses and allow for cost-benefit analyses to estimate the overall returns to society of investing in MOOC creation (Hollands & Tirthali, 2014).…”