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Massive Open Online Course(MOOC) is undergoing explosive growth recently, both the number of MOOC platforms and courses are increasing dramatically during these years. One of the major concerns in MOOC is high dropout rate, we study dropout prediction in MOOCs, using student's learning activities data in a period of time to measure how likely students would drop out in next couple of days. We collect...
Educational Data Mining and Learning Analytics are two growing fields of study, trying to make sense of education data and to improve teaching and learning experience. We study dropout prediction in Massively Open Online Courses (MOOCS), where the goal is given student's learning behavior log data in one month, to predict whether students would drop out in next ten days. We collect 39 courses data...
Current smartphones are integrated with rich sensors, which provides a good opportunity for the smartphone sensor data mining. By mining these data, we are able to analyze the user's behaviors. This paper describes HARLib, a human activity recognition library on the Android operating system. We use accelerometer built-in smartphone to recognize the user's activities, including walking, running, sitting,...
In this paper, we propose a data-driven approach that extracts prior information for segmentation of the left ventricle in cardiac MR images of transplanted rat hearts. In our approach, probabilistic priors are generated from prominent features, i.e., corner points and scale-invariant edges, for both endo- and epi-cardium segmentation. We adopt a level set formulation that integrates probabilistic...
During the indexing process of traditional search engine, web pages become a list of terms, but single term cannot represent the rich content of web pages, which makes information retrieval methods mainly based on terms matching often result in depressed precision. This paper proposes a novel query expansion technique that has phrases as its expansion unit. Phrases typically have a higher information...
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