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

D2.3.3 Evaluation results of the LinkedUp VICI competition

This document D2.3.3 is the final report of Task 2.4 – Evaluation of challenge submissions. Task 2.4 is about the actual assessment of the participating projects within the LinkedUp Veni, Vidi and Vici competition on the basis of the LinkedUp Evaluation Framework (D2.2.1). The main objective of Task 2.4 is to summarise and report the outcomes of the various competitions and analyse the practical experiences of the experts with the LinkedUp Evaluation Framework to further improve the evaluation framework. In the current document D2.3.3 we report about the Linked Data tools and ideas that have been submitted to the third and final data competition - Vici. In total, we received 13 submissions, 10 of them have been shortlisted and invited to a poster presentation at the 13th International Semantic Web Conference (ISWC 2014), four of them have been awarded by the LinkedUp evaluation procedure. In addition, an audience price has been awarded at the ISWC. This deliverable shortly lists the Vici submissions, explains the evaluation procedure that resulted in a short list of the best submissions, justifies the decision for the winners, and also reports the experiences collected within Vici.LinkedU

Towards highly informative learning analytics

Among various trending topics that can be investigated in the field of educational technology, there is a clear and high demand for using artificial intelligence (AI) and educational data to improve the whole learning and teaching cycle. This spans from collecting and estimating the prior knowledge of learners for a certain subject to the actual learning process and its assessment. AI in education cuts across almost all educational technology disciplines and is key to many other technological innovations for educational institutions. The use of data to inform decision-making in education and training is not new, but the scope and scale of its potential impact on teaching and learning have silently increased by orders of magnitude over the last few years. The release of ChatGPT was another driver to finally make everyone aware of the potential effects of AI technology in the digital education system of today. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data with AI has the potential to provide evidence-based insights into learners’ abilities and patterns of behaviour that, in turn, can provide crucial action points to guide curriculum and course design, personalised assistance, generate assessments, and the development of new educational offerings. AI in education has many connected research communities like Artificial Intelligence in Education (AIED), Educational Data Mining (EDM), or Learning Analytics (LA). LA is the term that is used for research, studies, and applications that try to understand and support the behaviour of learners based on large sets of collected data

dataTEL - Final Report

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

D9.1 Internal Communications Channels

D9.1 Internal Communications Channel. LACE ProjectLace projec

Guest Editorial: Ethics and Privacy in Learning Analytics

The European Learning Analytics Community Exchange (LACE) project is responsible for an ongoing series of workshops on ethics and privacy in learning analytics (EP4LA), which have been responsible for driving and transforming activity in these areas. Some of this activity has been brought together with other work in the papers that make up this special issue. These papers cover the creation and development of ethical frameworks, as well as tools and approaches that can be used to address issues of ethics and privacy. This editorial suggests that it is worth taking time to consider the often intertangled issues of ethics, data protection and privacy separately. The challenges mentioned within the special issue are summarised in a table of 22 challenges that are used to identify the values that underpin work in this area. Nine ethical goals are suggested as the editors’ interpretation of the unstated values that lie behind the challenges raised in this paper

JCAL special issue on multimodal learning analytics [editorial]

Editoria