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Educating the K-Gray engineering community in today’s digital world requires straightforward yet flexible access to highquality educational resources. Inspires by this, we propose an automatic evaluation system for learning resources ranking in a real world digital library, Engineering Pathway (EP). The Engineering Pathway is a portal to high-quality teaching and learning resources in engineering, applied science and math, computer science/information technology, and engineering technology, which is designed for use by K-12 and university educators and students. We model the best and most popular leaning resource objects from Premier Award Winners to recognize high-quality and non-commercial courseware designed to enhance the engineering education. We adopt the D-S evidence theory to model our problem. After giving effective definition of the mass function, the model can be transferred into multinomial regression model. We try three different models: linear regression, quadratic regression and sextic regression to get the most practicable model. With the help of this model, it will be much more simple and precise to help our domain experts to select the most valuable learning resources in our EP digital library. Experiments show that out proposed model performs well through training and optimization.

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