Learning concept graphs from online educational data

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作者：
机构：
[1] Liu, Hanxiao
[2] Ma, Wanli
[3] Yang, Yiming
[4] Carbonell, Jaime
来源：
| 2016年 / AI Access Foundation卷 / 55期
关键词：
This paper addresses an open challenge in educational data mining; i.e; the problem of automatically mapping online courses from different providers (universities; MOOCs; etc.) onto a universal space of concepts; and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase; our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase; the induced concept-level links are used to infer the unknown courselevel prerequisite links. Whereas courses may be specific to one institution; concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning; e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT; Caltech; Princeton and CMU show promising results. © 2016 AI Access Foundation. All rights reserved;
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