Handbook of Learning Analytics

Chapter 29

Handbook of Learning Analytics
First Edition

Linked Data for Learning Analytics:
The Case of the LAK Dataset

Davide Taibi & Stefan Dietze


The opportunities of learning analytics (LA) are strongly constrained by the availability and quality of appropriate data. While the interpretation of data is one of the key requirements for analyzing it, sharing and reusing data are also crucial factors for validating LA techniques and methods at scale and in a variety of contexts. Linked data (LD) principles and techniques, based on established W3C standards (e.g., RDF, SPARQL), offer an approach for facilitating both interpretability and reusability of data on the Web and as such, are a fundamental ingredient in the widespread adoption of LA in industry and academia. In this chapter, we provide an overview of the opportunities of LD in LA and educational data mining (EDM) and introduce the specific example of LD applied to the Learning Analytics and Knowledge (LAK) Dataset. The LAK dataset provides access to a near-complete corpus of scholarly works in the LA field, exposed through rigorously applying LD principles. As such, it provides a focal point for investigating central results, methods, tools, and theories of the LA community and their evolution over time.

Export Citation: Plain Text (APA)     BIBTeX     RIS

Supplementary Material

No Supplementary Material Available

References (12)

d’Aquin, M., Adamou, A., & Dietze, S., (2013). Assessing the educational linked data landscape. Proceedings of the 5th Annual ACM Web Science Conference (WebSci ’13), 2–4 May 2013, Paris, France (pp. 43–46). New York: ACM.

d’Aquin, M., & Jay, N. (2013). Interpreting data mining results with linked data for learning analytics: Motivation, case study and direction. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 155–164). New York: ACM.

d’Aquin, M., Dietze, S., Herder, E., Drachsler, H., & Taibi, D. (2014). Using linked data in learning analytics. eLearning Papers, 36. http://hdl.handle.net/1820/5814

Ben Ellefi, M., Bellahsene, Z., Dietze, S., & Todorov, K. (2016). Intension-based dataset recommendation for data linking. In H. Sack, E. Blomqvist, M. d’Aquin, C. Ghidini, S. P. Ponzetto, & C. Lange (Eds.), The Semantic Web: Latest Advances and New Domains (pp. 36–51; Lecture Notes in Computer Science, Vol. 9678). Springer.

Bizer, C., Heath, T., & Bernes-Lee, T. (2009). Linked data: The story so far. https://wtlab.um.ac.ir/images/e-library/linked_data/Linked%20Data%20-%20The%20Story%20So%20Far.pdf

Dawson, S., Gašević, D., Siemens, G., & Joksimović, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. Proceedings of the 4th International Conference on Learning Analytics & Knowledge(LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 231–240). New York: ACM.

Dietze, S., Sanchez-Alonso, S., Ebner, H., Yu, H., Giordano, D., Marenzi, I., & Pereira Nunes, B. (2013). Interlinking educational resources and the web of data: A survey of challenges and approaches. Program: Electronic Library and Information Systems, 47(1), 60–91.

Dietze, S., Taibi, D., Yu, H. Q., & Dovrolis, N. (2015). A linked dataset of medical educational resources. British Journal of Educational Technology, 46(5), 1123–1129.

Dietze, S., Taibi, D., & d’Aquin, M. (2017). Facilitating scientometrics in learning analytics and educational data mining: The LAK dataset. Semantic Web Journal, 8(3), 395–403.

Dietze, S., Drachsler, H., & Giordano, D. (2014). A survey on linked data and the social web as facilitators for TEL recommender systems. In N. Manouselis, K. Verbert, H. Drachsler, & O. C. Santos (Eds.), Recommender systems for technology enhanced learning: Research trends and applications (pp. 47-77). Springer.

Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. San Rafael, CA: Morgan & Claypool Publishers.

Taibi, D., Fetahu, B., & Dietze, S. (2013). Towards integration of web data into a coherent educational data graph. Proceedings of the 22nd International Conference on World Wide Web (WWW ’13), 13–17 May 2013, Rio de Janeiro, Brazil (pp. 419–424). New York: ACM. doi:10.1145/2487788.2487956

About this Chapter

Linked Data for Learning Analytics: The Case of the LAK Dataset

Book Title
Handbook of Learning Analytics

pp. 337-345




Society for Learning Analytics Research

Davide Taibi1
Stefan Dietze2

Author Affiliations
1. Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Italy
2. L3S Research Center, Germany

Charles Lang3
George Siemens4
Alyssa Wise5
Dragan Gašević6

Editor Affiliations
3. Teachers College, Columbia University, USA
4. LINK Research Lab, University of Texas at Arlington, USA
5. Learning Analytics Research Network, New York University, USA
6. Schools of Education and Informatics, University of Edinburgh, UK


Register | Lost Password