Rank Aggregation for Course Sequence Discovery

In this work, we adapt the rank aggregation framework for the discovery of optimal course sequences at the university level. Each student provides a partial ranking of the courses taken throughout his or her undergraduate career. We compute pairwise rank comparisons between courses based on the order students typically take them, aggregate the results over the entire student population, and then obtain a proxy for the rank offset between pairs of courses. We extract a global ranking of the courses via several state-of-the art algorithms for ranking with pairwise noisy information, including SerialRank, Rank Centrality, and the recent SyncRank based on the group synchronization problem. We test this application of rank aggregation on 15 years of student data from the Department of Mathematics at the University of California, Los Angeles (UCLA). Furthermore, we experiment with the above approach on different subsets of the student population conditioned on final GPA, and highlight several differences in the obtained rankings that uncover hidden pre-requisites in the Mathematics curriculum

Rank Aggregation for Course Sequence Discovery

This work extends the rank aggregation framework for the setting of discovering optimal course sequences at the university level, and contributes to the literature on educational applications of network analysis. Each student provides a partial ranking of the courses taken throughout her or his undergraduate career. We build a network of courses by computing pairwise rank comparisons between courses based on the order students typically take them, and aggregate the results over the entire student population, to obtain a proxy for the rank offset between pairs of courses. We extract a global ranking of the courses via several state-of-the art algorithms for ranking with pairwise noisy information, including SerialRank, Rank Centrality, and the recent SyncRank based on the group synchronization problem. We test this application of rank aggregation on 15 years of student data from the Department of Mathematics at the University of California, Los Angeles (UCLA). Furthermore, we experiment with the above approach on different subsets of the student population conditioned on final GPA, and highlight several differences in the obtained rankings that uncover potential hidden pre-requisites in the Mathematics curriculum

How to Integrate Formal and Informal Settings in Massive Open Online Courses Through a Transgenic Learning Approach

Capítulo del libro "Lecture Notes in Educational Technology"Formal and informal learning settings overlap quite often. Since the arrival and consolidation of 24/7 Internet services, contents, authoring tools and free storage, informal settings have become a powerful resource for learning. The combination of both scenarios is a reality that must be tackled: one with regular academic programmes, and the other one with mostly unstructured learning and information unit. However, successful engagement between them is not that common. Regular academic programmes deployed with learning management systems (LMS) keep a track of user activity and user performance. Sometimes these inputs are stored and processed to provide some useful feedback in the form of recommendation or advice to the very same user. However, there is usually no tracking of user activity on social media, external fora or other online services. This lack of awareness about user activity outside the LMS prevents the educational methodology or the pedagogical model from considering all the available information. It also restricts the analysis to just one area: official education. To this extent, MOOCs have become a crucial part of combined educational models that move between formal and informal settings, where they play a key role in the learning path of every user. However, this does not seem enough. The current educational landscape requires disruption to boost the learning-teaching process. We call this disruption transgenic learning. It is based on the user’s behaviour and interactions, along with efficient monitoring and personalised counselling by a tutor. It is focused on improving the user’s awareness of their real academic status and their related performance. In this book chapter, we discuss the specific implementation of a personalised eLearning model for restricted social networks and learning management systems, called LIME, which supports this approach. LIME is focused on massive enrolment and large datasets. We also present a framework and software prototype that implements the model (iLIME). Lastly, we show a successful practical case study that makes use of iLIME integrated with the Sakai CLE LMS. This case study depicts the technical issues, challenges and solutions involved in a successful deployment of the LIME model into a real university scenari