ReMashed – Recommendations for Mash-Up Personal Learning Environments

Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H. G. K., & Koper, R. (2009). ReMashed - Recommendations for Mash-Up Personal Learning Environments. In U. Cress, V. Dimitrova & M. Specht (Eds.), Learning in the Synergy of Multiple Disciplines. Proceedings of the Fourth European Conference on Technology-Enhanced Learning (EC-TEL 2009) (pp. 788-793). September, 29 - October, 2, 2009, Nice, France. Lecture Notes in Computer Science Vol. 5794. Berlin: Springer-Verlag.The following article presents a Mash-Up Personal Learning Environment called ReMashed that recommends learning resources from emerging information of a Learning Network. In ReMashed learners can specify certain Web2.0 services and combine them in a Mash-Up Personal Learning Environment. Learners can rate information from an emerging amount of Web2.0 information of a Learning Network and train a recommender system for their particular needs. ReMashed therefore has three main objectives: 1. to provide a recommender system for Mash-up Personal Learning Environments to learners, 2. to offer an environment for testing new recommendation approaches and methods for researchers, and 3. to create informal user-generated content data sets that are needed to evaluate new recommendation algorithms for learners in informal Learning Networks.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings

Drachsler, H., Peccau, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H. G. K., & Koper, R. (2009). ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings. In F. Wild, M. Kalz, M. Palmér & D. Müller (Eds.), Proceedings of 2nd Workshop Mash-Up Personal Learning Envrionments (MUPPLE'09). Workshop in conjunction with 4th European Conference on Technology Enhanced Learning (EC-TEL 2009): Synergy of Disciplines (pp. 23-30). September, 29, 2009, Nice, France: CEUR workshop proceedings, http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-506 .This article presents the ReMashed system that recommends learning content from emerging information of a Mash-Up Personal Learning Environment. ReMashed offers advice to find most suitable learning content for individual competence development of lifelong learners. The ReMashed system was initially designed to offer navigational support to lifelong learners in informal learning settings. In this article we want to discuss its ability to be used also in formal learning settings. For this purpose, we discuss the use of two different recommendation approaches for formal and informal learning within ReMashed.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings

This article presents the ReMashed system that recommends learning content from emerging information of a Mash-Up Personal Learning Environment. ReMashed offers advice to find most suitable learning content for individual competence development of lifelong learners. The ReMashed system was initially designed to offer navigational support to lifelong learners in informal learning settings. In this article we want to discuss its ability to be used also in formal learning settings. For this purpose, we discuss the use of two different recommendation approaches for formal and informal learning within ReMashed

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

Evaluation of Recommender Systems for Technology-Enhanced Learning: Challenges and Possible Solutions

Heleou, S., Drachsler, H., & Gillet, D. (2009). Evaluation of Recommender Systems for Technology-Enhanced Learning: Challenges and Possible Solutions. 1st workshop on Context-aware Recommender Systems for Learning at the Alpine Rendez-Vous. November, 30-December, 3, 2009, Garmisch-Patenkirchen, Germany.This paper discusses challenges and possible solutions of recommender systems for Technology-Enhanced Learning (TEL). It also briefly presents the the 3A contextual recommender system and explores its applicability and evaluation in the context of learners using multiple Web 2.0 applications

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software

Benefiting from the advent of social software, information sharing becomes pervasive. Personalized rating systems have emerged to evaluate the quality of user-generated content in open environment and provide recommendation based on users’ past experience. In this paper, a trust-based rating prediction approach for recommendation in Web 2.0 collaborative learning social software is proposed. Trust network is exploited in the rating prediction scheme and a multi-relational trust metric is developed in an implicit way. Finally the evaluation of the approach is performed using the dataset of collaborative learning social software, namely Remashed

Are Mash-Ups the Future for Online Learning Platforms? Psychology A-Level Students' Judgements about VLE and MUPPLE Interfaces

Virtual Learning Environments (VLEs) have become ubiquitous in colleges and universities but have failed to consistently improve learning (Machin, 2007). An alternative interface can be provided in the form of a mashed-up personal learning environment (MUPPLE). The aim of this study was to investigate student perceptions of its desirability and utility in comparison to their existing VLE. A psychology-oriented MUPPLE was constructed using a free online mash-up platform. A focus group of psychology A-level students was asked to identify likely advantages and disadvantages of the MUPPLE as compared to their existing VLE interface. They identified five potential advantages of the MUPPLE interface; aesthetics, congruence with online apps used outside formal education, user control, utility as an aid to A-level study, and likely utility as an aid to undergraduate study. With regard to utility as an aid to A-level study, the focus group expressed concern that, whilst the MUPPLE interface would be likely to enhance independent study, that this might not in turn advantage A-level students. However, no advantages were attributed to the VLE interface. Sixty-five psychology A-level students assessed a MUPPLE and a VLE interface against the five criteria identified by the focus group. A within-subjects MANOVA revealed significant preferences for the MUPPLE interface on all five criteria. Implications for psychology education are discussed, and further research is called fo

Dataset-driven research for improving recommender systems for learning

Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.dataTEL, STELLAR, AlterEgo, VOA3

First steps towards an integration of a Personal Learning Environment at university level

Ebner, M., Schön, S., Taraghi, B., Drachsler, H., & Tsang, P. (2011). First steps towards an integration of a Personal Learning Environment at university level. In R. Kwan et al. (Eds.), ICT 2011, CCIS 177 (pp. 22–36), Springer-Verlag Berlin: Heidelberg 2011.Personalization is seen as the key approach to handle the plethora of information in today’s knowledge-based society. It is expected that personalized teaching and learning will address the needs of the learners more efficiently. The education of the future will change by the influence of Web 2.0 contents and the steadily increasing amount of data. This means that the students of tomorrow will regularly have to deal with sharing and merging contents from different sources. Therefore, mashup technology will become a very important means to focus on individual learning needs and to personalize the access to particular information. The following article describes the challenges of Personal Learning Environments at higher education institutions. In the first section, the concept of Personal Learning Environments is presented, while the second section discusses the new challenges that arise for learning with the help of Personal Learning Environments. The third section of the article describes the technical background of Personal Learning Environments and the widget standard in general. In section four, a first prototype of a personal learning environment will be presented, which is integrated into the Technical University of Graz. A detailed description of the available widgets for the prototype, along with a first expert evaluation, will be provided. Finally, the conclusion of the article will sum up the main points of this paper and present the plans for future research together with the prospective developments.NeLLL AlterEg