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A Recommendation System For Building School Teachers’ Multidisciplinary Skills
Educational systems are in serious need of personalized platforms, that could help to build students’ multidisciplinary skills. A recommendation system focused on multidisciplinary learning objects could be a solution to the issue. Moscow electronic school repository is analyzed and patterns of its users’ behaviors are described. Those patterns are observed based on the character and structure of actions available to the users, such as creating, copying, using, accessing, and viewing learning objects. The platform users constitute a network community, using similar objects and showing similar interests and thus building network relationships. These networks can be analyzed both at the macro and micro levels, thus visualizing a personal profile of a user in the system. Data analysis showed 7 clusters of users, most of who are not very active, while a moderate number of them exhibit so-called lurking behavior. They look through the learning resources, sometimes use them, but seldom create their own content. Our research found that a trend to create multidisciplinary objects is observed among active users, while lurkers are likely to create mostly monodisciplinary objects. The ratio of multidisciplinary objects can be increased by supporting delurking behaviors among users. Our findings can be useful both for educators and developers of platform learning solutions.