Exploring knowledge graphs for the identification of concept prerequisites-Reference-Cited by-同舟云学术

Exploring knowledge graphs for the identification of concept prerequisites

Published:2019-12 Issue:1 Volume:6 Page:
ISSN:2196-7091
Container-title:Smart Learning Environments
language:en
Short-container-title:Smart Learn. Environ.

Author:

Manrique Rubén^ORCID,Pereira Bernardo,Mariño Olga

Abstract

AbstractLearning basic concepts before complex ones is a natural form of learning. Automated systems and instructional designers evaluate and order concepts’ complexity to successfully generate and recommend or adapt learning paths. This paper addresses the specific challenge of accurately and adequately identifying concept prerequisites using semantic web technologies for a basic understanding of a particular concept within the context of learning: given a target concept c, the goals are to (a) find candidate concepts that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our four step approach consists of (i) an exploration of Knowledge Graphs in order to identify possible candidate concepts; (ii) the creation of a set of potential concepts; (iii) deployment of supervised learning model to evaluate a proposed list of prerequisite relationships regarding the target set; and, (iv) validation of our approaching using a ground truth of 80 concepts from different domains (with a precision varying between 76% and 96%).

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Education

Link

http://link.springer.com/content/pdf/10.1186/s40561-019-0104-3.pdf

Reference25 articles.

1. Adorni, G., Alzetta, C., Koceva, F., Passalacqua, S., Torre, I. (2019). Artificial Intelligence in Education. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (Eds.)Springer, Cham, (pp. 1–13).

2. Changuel, S., Labroche, N., Bouchon-Meunier, B. (2015). Resources sequencing using automatic prerequisite–outcome annotation. ACM Trans. Intell. Syst. Technol., 6(1), 6–1630. https://doi.org/10.1145/2505349.

3. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P. (2002). Smote: Synthetic minority over-sampling technique. J. Artif. Int. Res., 16(1), 321–357.

4. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. https://doi.org/10.1145/2939672.2939785. http://doi.acm.org/10.1145/2939672.2939785. ACM, New York, (pp. 785–794).

5. Cimiano, P., & Völker, J. (2005) In Montoyo, A., Muńoz, R., Métais, E. (Eds.), Text2Onto, (pp. 227–238). Berlin, Heidelberg: Springer.

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