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A Review, Timeline, and Categorization of Learning Design Tools Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System

Authors : Boban Vesin, Aleksandra Klašnja-Milićević, Mirjana Ivanović

Published in: Advances in Web-Based Learning – ICWL 2016

Publisher: Springer International Publishing

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Abstract

The success of intelligent tutoring system depends on the retrieval of relevant learning material according to the learner’s requirements. Therefore, the ultimate goal is development of the intelligent tutoring system that provides learning materials considering the requirements and understanding capability of the specific learner. In our previous research, we implemented a tutoring system named Protus 2.1 (PROgramming TUtoring System) that is used for learning basic concepts of Java programming language. It directs the learner’s activities and recommends relevant actions during the learning process based on the personal profile of each learner. This paper presents the implementation of collaborative tagging technique for content recommendation in Protus 2.1. This technique can be applied in intelligent tutoring systems for providing tag-enabled recommendations of concepts and resources. We investigated and identified tagging practices of students and their evolution over time.

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previous chapter A Multiple Constraints Framework for Collaborative Learning Flow Orchestration
next chapter Domain-Specific Recommendation by Matching Real Authors to Social Media Users
Literature
1.
Kim, H.H.: A personalized recommendation method using a tagging ontology for a social e-learning system. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part I. LNCS, vol. 6591, pp. 357–366. Springer, Heidelberg (2011)CrossRef
2.
Ocepek, U., et al.: Exploring the relation between learning style models and preferred multimedia types. Comput. Educ. 69, 343–355 (2013)CrossRef
3.
Zervas, P., Sampson, D.G.: The effect of users’ tagging motivation on the enlargement of digital educational resources metadata. Comput. Hum. Behav. 32, 292–300 (2014)CrossRef
4.
Salehi, M.: Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation. Data Knowl. Eng. 87, 130–145 (2013)CrossRef
5.
Lavoué, É.: Social tagging to enhance collaborative learning. In: Leung, H., Popescu, E., Cao, Y., Lau, R.W.H., Nejdl, W. (eds.) ICWL 2011. LNCS, vol. 7048, pp. 92–101. Springer, Heidelberg (2011)CrossRef
6.
Bateman, S., et al.: Applying collaborative tagging to e-learning. In: Proceedings of the 16th International World Wide Web Conference (WWW 2007) (2007)
7.
Verpoorten, D., et al.: Self-reported learning effects of a tagging activity carried out in a personal learning environment (PLE) by secondary-school pupils (2011)
8.
Anjorin, M., Rensing, C., Steinmetz, R.: Towards ranking in folksonomies for personalized recommender systems in e-learning. In: SPIM (2011)
9.
Cernea, D.A., Del Moral, E., Gayo, J.E.L.: SOAF: Semantic indexing system based on collaborative tagging. Interdisc. J. E-Learn. Learn. Objects 4, 137–150 (2008)
10.
Chen, J.-M., Chen, M.-C., Sun, Y.S.: A tag based learning approach to knowledge acquisition for constructing prior knowledge and enhancing student reading comprehension. Comput. Educ. 70, 256–268 (2014)CrossRef
11.
Zervas, P., Sampson, D.G., Aristeidou, M.: Investigating the effect of users’ tagging motivation on the digital educational resources metadata descriptions. International Association for Development of the Information Society (2012)
12.
Pirolli, P., Kairam, S.: A knowledge-tracing model of learning from a social tagging system. User Model. User-Adap. Inter. 23(2–3), 139–168 (2013)CrossRef
13.
Kohlhase, A., Reichel, M.: Embodied, constructionist learning: Social tagging and folksonomies in e-learning environments. In: Conference Proceedings, mICTE 2006 (2006)
14.
Klašnja-Milićević, A., et al.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56(3), 885–899 (2011)CrossRef
15.
Vesin, B., et al.: Protus 2.0: ontology-based semantic recommendation in programming tutoring system. Expert Syst. Appl. 39(15), 12229–12246 (2012)CrossRef
16.
Niemann, K., Wolpers, M.: Usage context-boosted filtering for recommender systems in TEL. In: Hernández-Leo, D., Ley, T., Klamma, R., Harrer, A. (eds.) EC-TEL 2013. LNCS, vol. 8095, pp. 246–259. Springer, Heidelberg (2013)CrossRef
17.
Ding, Y., et al.: Integrating social tagging data: upper tag ontology (UTO). In: 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008. IEEE (2008)
18.
Bonifazi, F., et al.: A web-based annotation tool supporting e-learning. In: Proceedings of the Working Conference on Advanced Visual Interfaces. ACM (2002)
19.
Milicevic, A.K., Nanopoulos, A., Ivanovic, M.: Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 33(3), 187–209 (2010)CrossRef
20.
Kim, H.-L., et al.: Evolution of social networks based on tagging practices. IEEE Trans. Serv. Comput. 6(2), 252–261 (2013)CrossRef
21.
Bagheri, E., Ensan, F.: Semantic tagging and linking of software engineering social content. Autom. Softw. Eng. 23(2), 1–44 (2014)
22.
Treude, C., Storey, M.-A.: Work item tagging: communicating concerns in collaborative software development. IEEE Trans. Softw. Eng. 38(1), 19–34 (2012)CrossRef
Metadata
Title
Protus 2.1: Applying Collaborative Tagging for Providing Recommendation in Programming Tutoring System
Authors
Boban Vesin
Aleksandra Klašnja-Milićević
Mirjana Ivanović
Copyright Year
2016
Book
Advances in Web-Based Learning – ICWL 2016

Print ISBN: 978-3-319-47439-7

Electronic ISBN: 978-3-319-47440-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47440-3_26

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