Abstract
Currently, topic modelling has regained interest in the world of e-learning, where it is necessary to search through an extensive database of online learning objects, mainly in the form of educational videos. The main problem is the retrieval of those learning objects that are best suited to students’ keyword searches. Today this problem is still an open topic. According to this, this paper aims to provide a more sophisticated method to improve the search of educational videos and thus show those that best fit the learning objectives of students. To do this, a semi-supervised method to cluster and classify a large data-set of educational videos from the Universitat Politècnica de València has been developed. The proposed method employs open content resources from Wikipedia as labelled data to train the model.
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edX MOOC platform: https://www.edx.org/.
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We decided to use this embeddings method since its suitability is well demonstrated by state of the art in this field. Furthermore, it was already trained on a considerable amount of data.
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This design decision was supported by the fact that it behaved better than other algorithms and it required a smaller data-set than the commonly used neural networks.
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Acknowledgements
This work was partially supported by the project TIN2017-89156-R of the Spanish government, and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID- 10-14) of the Universitat Politècnica de València.
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Stoica, A.S., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C. (2019). A Semi-supervised Method to Classify Educational Videos. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_19
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