Abstract
With the development of computer technology, more and more people begin to learn programming. And there are a lot of platforms for programmers to practice. It’s often difficult for these platforms to customize the needs of users at different levels. In this paper, we address the above limitations and propose an intelligent tutoring model, to help programming platforms achieve better tutoring for different levels of users. We first devise a novel framework for programming education tutoring which is combined with programming education knowledge graph, crowdsourcing system and online knowledge tracing. Then, by ontology definition, information extraction and data fusion, we construct a knowledge graph to store the data in a more structured way. During the knowledge tracing stage, we extract behavior features and question knowledge features from a relational database and knowledge graph separately. Meanwhile, we improve the process for student ability evaluation and adapt the Knowledge Tracing algorithm to predict students’ behavior on knowledge and questions. Experiment results on real-world user behavior data sets show that through the help of Knowledge Tracing algorithm, we can achieve considerably satisfied results on students’ behavior prediction.
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Acknowledgment
This work was partially supported by NSFC 61401155 and NSFC 61502169. The first author thanks University Côte d’Azur, France and Inria Sophia Antipolis Méditerranée, France where she conducted her master’s final year project and internship.
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Sun, Y., Wang, L., Xie, Q., Dong, Y., Lin, X. (2020). Online Programming Education Modeling and Knowledge Tracing. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_23
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DOI: https://doi.org/10.1007/978-3-030-55130-8_23
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