Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning

Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G., Lindstaedt, S., Stern, H., Friedrich, M., & Wolpers, M. (2010, 28 September). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. Presentation at the 1st Workshop Recommender Systems in Technology Enhanced Learning (RecSysTEL) in conjunction with 5th European Conference on Technology Enhanced Learning (EC-TEL 2010): Sustaining TEL: From Innovation to Learning and Practice, Barcelona, Spain.The presentation is based on the positioning paper of the dataTEL Theme Team of the STELLAR Network of Excellence (http://www.teleurope.eu/pg/groups/9405/datatel/) that addresses the lack of educational data sets in TEL and present ideas to overcome this situation. The accompanying paper: Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning, can be found at http://www.sciencedirect.com/science/journal/18770509 and a pre-print is available in our Dspace repository and at scribd. The presentation starts with a description of the current situation where almost none educational data sets are publicly available. This is a strange situation as plenty of data is saved on a daily base in LMS like Moodle, Blackboard. In other domains like e-commerce it is a common practice to use publicly available data sets from different application environments (e.g. Yahoo, MovieLens) in order to evaluate algorithms and create new data products. These data sets are for instance used as benchmarks to develop new recommendation algorithms and compare them to other algorithms in certain settings. Recommender systems are also increasingly applied in Technology Enhanced Learning field but it is still an application area that lacks such publicly available data sets. Although there is a lot of research conducted on recommender systems in TEL, they lack data sets that would allow the experimental evaluation of the performance of different recommendation algorithms using comparable, interoperable, and reusable data sets. This leads to awkward experimentation and testing such as using data sets from movies in order to evaluate educational recommendation algorithms.Stella

Turning Learning into Numbers – A Learning Analytics Framework

Drachsler, H., & Greller, W. (2011, 30-31 August). Turning Learning into Numbers - A Learning Analytics Framework. Invite talk at SURF Learning Analytics seminar, Utrecht, The Netherlands.Presentation of the Learning Analytics framework at the SURF Learning Analytics seminar 2011. http://www.surf-academy.nl/programma/event/?id=395NeLL

dataTEL - Final Report

Drachsler, H. (2011). dataTEL - Final Report. STELLAR Theme Team funding.Final report of the dataTEL Theme Team.dataTEL, NeLLL AlterEg

Learning Analytics and Future R&O Opportunities

Drachsler, H. (2012, 20 June). Learning Analytics and Future R&O Opportunities. Guest lecture given at Learning Analytics seminar, RWTH Aachen, Aachen, Germany.Guest lecture given at RWTH Aachen, Germany.dataTEL, AlterEg

1st International Workshop on Learning Analytics and Linked Data

Drachsler, H., Dietze, S., Greller, W., D'Aquin, M., Jovanovic, J., Pardo, A., Reinhardt, W., & Verbert, K. (2012). 1st International Workshop on Learning Analytics and Linked Data. In S. Dawson, C. Haythornthwaite, S. Buckingham Shum, D. Gasevic, & R. Fergusson (Eds.), LAK '12 Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 9-10). New York, NY, USA: ACMThe main objective of the 1st International Workshop on Learning Analytics and Linked Data (#LALD2012) is to connect the research efforts on Linked Data and Learning Analytics in order to create visionary ideas and foster synergies between both young research fields. Therefore, the workshop will collect, explore, and present datasets, technologies and applications for Technology-Enhanced Learning (TEL) to discuss Learning Analytics approaches that make use of educational data or Linked Data sources. During the workshop, an overview of available educational datasets and related initiatives will be given. The participants have the opportunity to present their own research with respect to educational datasets, technologies and applications and discuss major challenges to collect, reuse, and share these datasets

LinkedUp kickoff / Session 4: Evaluation Framework Criteria and Indicator

Drachsler, H., & Stoyanov, S. (2012, 8 November). LinkedUp kickoff / Session 4: Evaluation Framework Criteria and Indicator. Presentation for WP2 - Evaluation, L3S, Hannover, Germany.Presentation for WP2 - Evaluation, about the development of the Evaluation framework with the Group Concept Mapping approach.Linkedu

dataTEL - Datasets for Technology Enhanced Learning

The dataTEL white paper develop during the dataTEL workshop at the ARV2011. The workshop was motivated by the issue that very less educational datasets are publicly available in TEL, so that the outcomes of different TEL adaptive applications and recommender systems that support personalised learning are hardly comparable. In other domains like in e-commerce it is a common practise to use different datasets as benchmarks to evaluate recommender systems algorithms to make the results comparable (MovieLens, Book-Crossing, EachMovie dataset). So far, no universally valid knowledge exists in TEL on algorithm that can be successfully applied in a certain learning setting to personalise learning. Having a collection of datasets could be a first major step towards a theory of personalisation with in TEL that can be based on empirical experiments with verifiable and valid results. Therefore, the main objective of the dataTEL workshop was to explore suitable datasets for TEL with a specific focus on recommender and adaptive information systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required preprocessing procedures and rules to create sharable datasets, common evaluation criteria for recommender systems in TEL and how a dataset driven future in TEL could look like

STELLAR Alpine Rendez-Vous White Paper

Drachsler, H., Verbert, K., Sicilia, M. A., Wolpers, M., Manouselis, N., Vuorikari, R., Lindstaedt, S., & Fischer, F. (2011). dataTEL - Datasets for Technology Enhanced Learning. STELLAR Alpine Rendez-Vous White Paper. Alpine Rendez-Vous 2011 White paper collection, Nr. 13., France (2011) Accessible at: http://oa.stellarnet.eu/open-archive/browse?resource=6756_v1The dataTEL white paper develop during the dataTEL workshop at the ARV2011. The workshop was motivated by the issue that very less educational datasets are publicly available in TEL, so that the outcomes of different TEL adaptive applications and recommender systems that support personalised learning are hardly comparable. In other domains like in e-commerce it is a common practise to use different datasets as benchmarks to evaluate recommender systems algorithms to make the results comparable (MovieLens, Book-Crossing, EachMovie dataset). So far, no universally valid knowledge exists in TEL on algorithm that can be successfully applied in a certain learning setting to personalise learning. Having a collection of datasets could be a first major step towards a theory of personalisation within TEL that can be based on empirical experiments with verifiable and valid results. Therefore, the main objective of the dataTEL workshop was to explore suitable datasets for TEL with a specific focus on recommender and adaptive information systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required preprocessing procedures and rules to create sharable datasets, common evaluation criteria for recommender systems in TEL and how a dataset driven future in TEL could look like.dataTEL, NeLLL AlterEgo, STELLAR, MAVSE