Hendrik Drachsler

Bio: Hendrik Drachsler is an academic researcher from Leibniz Association. The author has contributed to research in topics: Learning analytics & Recommender system. The author has an hindex of 32, co-authored 194 publications receiving 5660 citations. Previous affiliations of Hendrik Drachsler include Katholieke Universiteit Leuven & Goethe University Frankfurt.

Translating Learning into Numbers: A Generic Framework for Learning Analytics

Abstract: Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15(3), 42–57.

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Abstract: Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Recommender Systems in Technology Enhanced Learning

Abstract: Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415). Berlin: Springer.

Knowledge Worker Roles and Actions— Results of Two Empirical Studies

Abstract: Reinhardt, W., Schmidt, B., Sloep, P. B., & Drachsler, H. (2011). Knowledge Worker Roles and Actions - Results of Two Empirical Studies. Knowledge and Process Management, 18(3), 150–174. doi: 10.1002/kpm.378 Online: http://onlinelibrary.wiley.com/doi/10.1002/kpm.378/abstract

Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model

Abstract: This article argues that there is a need for Personal Recommender Systems (PRSs) in Learning Networks (LNs) in order to provide learners with advice on the suitable learning activities to follow. LNs target lifelong learners in any learning situation, at all educational levels and in all national contexts. They are community-driven because every member is able to contribute to the learning material. Existing Recommender Systems (RS) and recommendation techniques used for consumer products and other contexts are assessed on their suitability for providing navigational support in an LN. The similarities and differences are translated into specific requirements for learning and specific requirements for recommendation techniques. The article focuses on the use of memory-based recommendation techniques, which calculate recommendations based on the current data set. We propose a combination of memory-based recommendation techniques that appear suitable to realise personalised recommendation on learning activities in the context of e-learning. An initial model for the design of such systems in LNs and a roadmap for their further development are presented.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.