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The value of OERs mainly depends on how easy they can be searched or located through a web search engine. Currently, the MERLOT II metadata repository requests resource providers to choose the relevant discipline category manually while adding material to its repository. This practice appears very time-consuming and also bound to involve human errors. If a member picks an incorrect discipline category, then the learning resource may not be correctly categorized in the repository. This situation may result in a learning resource to be not shortlisted for a given keyword search of the “MERLOT Smart Search” or in the “Advanced search.” Above investigations motivated us to recognize the importance of developing an automated learning content classification solution for OER repositories. In this study, we proposed a novel automated learning content classification model (LCCM) to classify learning resources into relevant discipline categories while adding them to the MERLOT repository. The research goal incorporated in this paper include dataset preparation, data preprocessing, feature extraction using LDA topic model, and calculating the semantic similarity using a pre-trained word embedding matrix. These methods serve as a base for classifying learning resources more effectively within a short time.
- Automatic learning object classification
- Multi-label classification
- Open Educational Resources
- Topic models
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Real-Time Motion Interpretation for Detailed Musical Expression via Deep Learning on Instrument Fingering(2/2)
1/08/19 → 31/07/20