Abstract
With the proliferation in number and scale of online courses, several challenges have emerged in supporting stakeholders during their delivery and fruition. Machine Learning and Semantic Analysis can add value to the underlying online environments in order to overcome a subset of such challenges (e.g. classification, retrieval, and recommendation). However, conducting reproducible experiments in these applications is still an open problem due to the lack of available datasets in Technology-Enhanced Learning (TEL), mostly small and local. In this paper, we propose COCO, a novel semantic-enriched collection including over 43 K online courses at scale, 16 K instructors and 2,5 M learners who provided 4,5 M ratings and 1,2 M comments in total. This outruns existing TEL datasets in terms of scale, completeness, and comprehensiveness. Besides describing the collection procedure and the dataset structure, we depict and analyze two potential use cases as meaningful examples of the large variety of multi-disciplinary studies made possible by having COCO.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Please contact the authors by e-mail to obtain a copy of the dataset.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
References
eLearning Market Trends and Forecast 2017–2021 (2017). https://www.docebo.com/resource/elearning-market-trends-and-forecast-2017-2021/. Accessed 20 Nov 2017
G2crowd grid for online course providers (2017). https://www.g2crowd.com/categories/online-course-providers. Accessed 20 Nov 2017
Basu, S., Yu, Y., Zimmermann, R.: Fuzzy clustering of lecture videos based on topic modeling. In: 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2016)
Dessì, D., Fenu, G., Marras, M., Recupero, D.R.: Leveraging cognitive computing for multi-class classification of e-learning videos. In: European Semantic Web Conference, pp. 21–25. Springer (2017)
Yang, H., Meinel, C.: Content lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 7(2), 142–154 (2014)
Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Recommender Systems Handbook, pp. 421–451. Springer (2015)
Fazeli, S., Rajabi, E., Lezcano, L., Drachsler, H., Sloep, P.: Supporting users of open online courses with recommendations: an algorithmic study. In: Advanced Learning Technologies (ICALT), pp. 423–427. IEEE (2016)
Class central. https://www.class-central.com/. Accessed 20 Nov 2017
Course talk. https://www.coursetalk.com. Accessed 20 Nov 2017
Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Mining Text Data, pp. 163–222 (2012)
Estivill-Castro, V., Limongelli, C., Lombardi, M., Marani, A.: DAJEE: a dataset of joint educational entities for information retrieval in technology enhanced learning. In: Proceedings of the 39th International ACM SIGIR, pp. 681–684. ACM (2016)
Pappas, N., Popescu-Belis, A.: Combining content with user preferences for ted lecture recommendation. In: 2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 47–52. IEEE (2013)
Merlot (2017). https://www.merlot.org/merlot/index.htm. Accessed 20 Nov 2017
Ho, A.D., Reich, J., Nesterko, S.O., Seaton, D.T., Mullaney, T., Waldo, J., Chuang, I.: Harvardx and mitx: the first year of open online courses (2014)
Mace. http://ea-tel.eu/tel-research/mace/. Accessed 20 Nov 2017
Acknowledgments
Danilo Dessì and Mirko Marras acknowledge Sardinia Regional Government for the financial support of their PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020, Axis III “Education and Training”, Specific Goal 10.5).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Dessì, D., Fenu, G., Marras, M., Reforgiato Recupero, D. (2018). COCO: Semantic-Enriched Collection of Online Courses at Scale with Experimental Use Cases. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_133
Download citation
DOI: https://doi.org/10.1007/978-3-319-77712-2_133
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77711-5
Online ISBN: 978-3-319-77712-2
eBook Packages: EngineeringEngineering (R0)