Handbook of Learning Analytics

Belleau, F., Nolin, M.-A., Tourigny, N., Rigault, P., & Morissette, J. (2008). Bio2RDF: Towards a mashup to build bioinformatics knowledge systems. Journal of Biomedical Informatics, 41(5), 706–716.

Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data — the story so far. International Journal on Semantic Web and Information Systems, 5(3), 1–22. Preprint retrieved from http://tomheath.com/papers/bizer-heath-berners-lee-ijswis-linked-data.pdf

Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5–6), 318–331.

Cooper, A. R. (2013). Learning analytics interoperability: A survey of current literature and candidate standards. http://blogs.cetis.ac.uk/adam/2013/05/03/learning-analytics-interoperability

d’Aquin, M., & Jay, N. (2013). Interpreting data mining results with linked data for learning analytics: Motivation, case study and directions. 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., Adamou, A., & Dietze, S. (2013, May). 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.

De Nies, T., Salliau, F., Verborgh, R., Mannens, E., & Van de Walle, R. (2015, May). TinCan2PROV: Exposing interoperable provenance of learning processes through experience API logs. Proceedings of the 24th International Conference on World Wide Web (WWW ’15), 18–22 May 2015, Florence, Italy (pp. 689–694). New York: ACM.

Desmarais, M. C., & Baker, R. S. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22(1–2), 9–38.

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 (pp. 47–75). New York: Springer.

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

Dietze, S., Yu, H. Q., Giordano, D., Kaldoudi, E., Dovrolis, N., & Taibi, D. (2012). Linked education: Interlinking educational resources and the web of data. Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC 2012), 26–30 March 2012, Riva (Trento), Italy (pp. 366–371). New York: ACM.

Dowell, N. M., Graesser, A. C., Hennis, T. A., Skrypnyk, O., Dawson, S., de Vries, P., & Kovanović, V. (2015). Modeling learners’ social centrality and performance through language and discourse. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.), Proceedings of the 8th International Conference on Education Data Mining (EDM2015), 26–29 June 2015, Madrid, Spain (pp. 250–257). International Educational Data Mining Society. http://files.eric.ed.gov/fulltext/ED560532.pdf

Duval, E. (2011). Attention please! Learning analytics for visualization and recommendation. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK ʼ11), 27 February–1 March 2011, Banff, AB, Canada (pp. 9–17). New York: ACM.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.

Groth, P., Loizou, A., Gray, A. J., Goble, C., Harland, L., & Pettifer, S. (2014). API-centric linked data integration: The open PHACTS discovery platform case study. Web Semantics: Science, Services and Agents on the World Wide Web, 29, 12–18.

Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis lectures on the semantic web: Theory and technology, 1(1), 1–136. Morgan & Claypool.

Hu, Y., McKenzie, G., Yang, J. A., Gao, S., Abdalla, A., & Janowicz, K. (2014). A linked-data-driven web portal for learning analytics: Data enrichment, interactive visualization, and knowledge discovery. In LAK Workshops. http://geog.ucsb.edu/~jano/LAK2014.pdf

Joksimović, S., Kovanović, V., Jovanović, J., Zouaq, A., Gašević, D., & Hatala, M. (2015). What do cMOOC participants talk about in social media? A topic analysis of discourse in a cMOOC. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March, Poughkeepsie, NY, USA (pp. 156–165). New York: ACM.

Jovanović, J., Bagheri, E., Cuzzola, J., Gašević, D., Jeremic, Z., & Bashash, R. (2014). Automated semantic annotation of textual content. IEEE IT Professional, 16(6), 38–46.

Kagemann, S., & Bansal, S. (2015). MOOCLink: Building and utilizing linked data from massive open online courses. Proceedings of the 9th IEEE International Conference on Semantic Computing (IEEE ICSC 2015), 7–9 February 2015, Anaheim, California, USA (pp. 373–380). IEEE.

Kessler, C., d’Aquin, M., & Dietze, S. (2013). Linked data for science and education. Journal of Semantic Web, 4(1), 1–2.

Kontokostas, D., Westphal, P., Auer, S., Hellmann, S., Lehmann, J., Cornelissen, R., & Zaveri, A. (2014). Test-driven evaluation of linked data quality. Proceedings of the 23rd International Conference on World Wide Web (WWW ’14), 7–11 April 2014, Seoul, Republic of Korea (pp. 747–758). New York: ACM.

Lausch, A., Schmidt, A., & Tischendorf, L. (2015). Data mining and linked open data: New perspectives for data analysis in environmental research. Ecological Modelling, 295, 5–17.

Maturana, R. A., Alvarado, M. E., López-Sola, S., Ibáñez, M. J., & Elósegui, L. R. (2013). Linked data based applications for learning analytics research: Faceted searches, enriched contexts, graph browsing and dynamic graphic visualisation of data. LAK Data Challenge. http://ceur-ws.org/Vol-974/lakdatachallenge2013_03.pdf

Milikić, N., Krcadinac, U., Jovanović, J., Brankov, B., & Keca, S. (2013). Paperista: Visual exploration of semantically annotated research papers. LAK Data Challenge. http://ceur-ws.org/Vol-974/lakdatachallenge2013_04.pdf

Mirriahi, N., Gašević, D., Dawson, S., & Long, P. D. (2014). Scientometrics as an important tool for the growth of the field of learning analytics. Journal of Learning Analytics, 1(2), 1–4.

Muñoz-Merino, P. J., Pardo, A., Kloos, C. D., Muñoz-Organero, M., Wolpers, M., Katja, K., & Friedrich, M. (2010). CAM in the semantic web world. In A. Paschke, N. Henze, & T. Pellegrini (Eds.), Proceedings of the 6th International Conference on Semantic Systems (I-Semantics ’10), 1–3 September 2010, Graz, Austria. New York: ACM. doi:10.1145/1839707.1839737

Newman, M. E. (2006). Modularity and community structure in networks, Proceedings of the National Academy of Sciences, 103(23), 8577–8582.

Niemann, K., Wolpers, M., Stoitsis, G., Chinis, G., & Manouselis, N. (2013). Aggregating social and usage datasets for learning analytics: Data-oriented challenges. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 245–249). New York: ACM.

Nunes, B. P., Fetahu, B., & Casanova, M. A. (2013). Cite4Me: Semantic retrieval and analysis of scientific publications. LAK Data Challenge, 974. http://ceur-ws.org/Vol-974/lakdatachallenge2013_06.pdf

Ochoa, X., Suthers, D., Verbert, K., & Duval, E. (2014). Analysis and reflections on the third learning analytics and knowledge conference (LAK 2013). Journal of Learning Analytics, 1(2), 5–22.

Piedra, N., Chicaiza, J. A., López, J., & Tovar, E. (2014). An architecture based on linked data technologies for the integration and reuse of OER in MOOCs context. Open Praxis, 6(2), 171–187.

Santos, J. L., Verbert, K., Klerkx, J., Duval, E., Charleer, S., & Ternier, S. (2015). Tracking data in open learning environments. Journal of Universal Computer Science, 21(7), 976–996.

Scheffel, M., Niemann, K., Leon Rojas, S., Drachsler, H., & Specht, M. (2014). Spiral me to the core: Getting a visual grasp on text corpora through clusters and keywords. LAK Data Challenge. http://ceur-ws.org/Vol-1137/lakdatachallenge2014_submission_3.pdf

Schmitz, H. C., Wolpers, M., Kirschenmann, U., & Niemann, K. (2011). Contextualized attention metadata. In C. Roda (Ed.), Human attention in digital environments (pp. 186–209). New York: Cambridge University Press.

Sharkey, M., & Ansari, M. (2014). Deconstruct and reconstruct: Using topic modeling on an analytics corpus. LAK Data Challenge. http://ceur-ws.org/Vol-1137/lakdatachallenge2014_submission_1.pdf

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10. http://itdl.org/Journal/Jan_05/article01.htm

Softic, S., De Vocht, L., Taraghi, B., Ebner, M., Mannens, E., & De Walle, R. V. (2014). Leveraging learning analytics in a personal learning environment using linked data. Bulletin of the IEEE Technical Committee on Learning Technology, 16(4), 10–13.

Taibi, D., & Dietze, S. (2013), Fostering analytics on learning analytics research: The LAK dataset. LAK Data Challenge, 974. http://ceur-ws.org/Vol-974/lakdatachallenge2013_preface.pdf

Zablith, F. (2015). Interconnecting and enriching higher education programs using linked data. Proceedings of the 24th International Conference on World Wide Web (WWW ’15), 18–22 May 2015, Florence, Italy (pp. 711–716). New York: ACM.

Zotou, M., Papantoniou, A., Kremer, K., Peristeras, V., & Tambouris, E. (2014). Implementing “rethinking education”: Matching skills profiles with open courses through linked open data technologies. Bulletin of the IEEE Technical Committee on Learning Technology, 16(4), 18–21.

Zouaq, A., Joksimović, S., & Gašević, D. (2013). Ontology learning to analyze research trends in learning analytics publications. LAK Data Challenge. http://ceur-ws.org/Vol-974/lakdatachallenge2013_08.pdf