Content Assistance and Recommendations in Learning Material - A Folksonomy-based Approach
Benedikt Engelbert, Karsten Morisse, Oliver Vornberger
2016
Abstract
With the variety of Learning Materials (LM) available in Learning Management Systems and the Internet, the time a student requires to select the most appropriate content increases. Especially the use of the Internet to find new LM is time consuming and not necessarily successful. A study accomplished at our university shows, that students mainly look for alternative explanations, content related exercises and examples, which can be used in addition to the existing LM. In this paper we describe the System Learning Assistance Osnabrueck (LAOs), which is based on a collaborative tagging approach with the main goals to give content related assistance for available LM, but also recommend content in further LM e.g. from the Internet.
References
- Agrawal, A., Leonard, S. & Paepcke, A., 2015. YouEDU?: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips. In Proceedings of the 8th International Conference on Educational Data Mining. pp. 297-304.
- Anjorin, M., Rensing, C. & Steinmetz, R., 2011. Towards ranking in folksonomies for personalized recommender systems in e-learning. CEUR Workshop Proceedings, 781(October), pp.22-25.
- Broisin, J. et al., 2010. A personalized recommendation framework based on cam and document annotations. Procedia Computer Science 1, 1(2), pp.2839-2848.
- Dahl, D. & Vossen, G., 2008. Evolution of learning folksonomies: social tagging in e-learning repositories. International Journal of Technology Enhanced Learning, 1(1/2), p.35.
- Engelbert, B., Morisse, K. & Vornberger, O., 2013. Zwischen Nutzung und Nutzen - Die Suche nach geeigneten Lernmaterialien und deren Mehrwerte im Kontext einer Informatikveranstaltung. In GMW 2014 - Lernräume gestalten - Bildungskontexte vielfältig denken. Zürich, pp. 508-519.
- Fu, X. et al., 2007. Video Annotation in a Learning Environment. Proceedings of the American Society for Information Science and Technology, 43(1), pp.1-22.
- Heim, P. et al., 2009. Semantisch unterstütze Informationsextraktion aus Dokumentenmengen. Hartmut Wandke; Saskia Kain & Doreen Struve, ed., “Mensch und Computer 2009”, pp.415-418.
- Hotho, A., Jäschke, R., et al., 2006. Folkrank: A ranking algorithm for folksonomies. Proc. FGIR, pp.2-5.
- Luo, G. & Pang, Y., 2010. Video annotation for enhancing blended learning of physical education. Artificial Intelligence and Education (ICAIE), …, pp.761-764.
- Machardy, Z. & Pardos, Z.A., 2015. Evaluating The Relevance of Educational Videos using BKT and Big Data. In Proceedings of the 8th International Conference on Educational Data Mining. pp. 424- 427.
- Manouselis, N. et al., 2013. Recommender Systems for Learning, New York, NY: Springer New York.
- Mohsin, S.F., 2010. Web based Multimedia Recommendation System for e-Learning Website. Journal of Advanced Networking and Applications, 223, pp.217-223.
- Niemann, K., 2015. Increasing the accessibility of learning objects by automatic tagging. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK 7815, pp.414-415.
- Pan, W. & Hawryszkiewycz, I., 2004. A method of defining learning processes. Beyond the 21st ASCILITE Conference, pp.734-742.
- Purwitasari, D. et al., 2011. Ontology-based annotation recommender for learning material using contextual analysis. In Proceedings of the IETEC'11 Conference. Kuala Lumpur, Malaysia.
- Ricci, F. et al., 2011. Recommender Systems Handbook. In F. Ricci et al., eds. Recommender Systems Handbook. Boston, MA: Springer US.
- Singh, T. & Khanna, S., 2014. Reinforcement learning approach towards effective content recommendation in MOOC environments 1. In IEEE International Conference on MOOC, Innovation and Technology in Education (MITE), 2014. pp. 285-289.
- Yu, L. & Li, Q., 2009. Personal Media Data Organization and Retrieval in e-Learning?: A Collaborative Tagging Based Approach. MTDL, pp.1-7.
Paper Citation
in Harvard Style
Engelbert B., Morisse K. and Vornberger O. (2016). Content Assistance and Recommendations in Learning Material - A Folksonomy-based Approach . In Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-179-3, pages 456-463. DOI: 10.5220/0005895304560463
in Bibtex Style
@conference{csedu16,
author={Benedikt Engelbert and Karsten Morisse and Oliver Vornberger},
title={Content Assistance and Recommendations in Learning Material - A Folksonomy-based Approach},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2016},
pages={456-463},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005895304560463},
isbn={978-989-758-179-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Content Assistance and Recommendations in Learning Material - A Folksonomy-based Approach
SN - 978-989-758-179-3
AU - Engelbert B.
AU - Morisse K.
AU - Vornberger O.
PY - 2016
SP - 456
EP - 463
DO - 10.5220/0005895304560463