We study the problem of providing recommendations to students that help them in their studies. To address this problem, we present an approach of providing recommendations of remedial learning materials to students that fill the gaps in their knowledge of the subject in the courses they take. According to this method, we first identify gaps in the student's mastery of various course topics. We then identify those items from the library of assembled learning materials that help us to fill those gaps, and recommend these identified materials to the student. We show empirically through A/B testing that this approach leads to better performance results, as measured by student's total score on the final exam across the personalized, non-personalized, and control groups and by improvement of the student's average score on that exam in comparison to previously taken courses. The proposed method is scalable since it can be applied to a large number of students across many courses.

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This article presents a novel approach to detecting emergency events, such as power outages, that utilizes social media users as “social sensors” for virtual detection of such events. The proposed new method is based on the analysis of the Twitter data that leads to the detection of Twitter discussions about these emergency events. The method described in the article was implemented and deployed by one of the vendors in the context of detecting power outages as a part of their comprehensive social engagement platform. It was also field tested on Twitter users in an industrial setting and performed well during these tests.

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