Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies

Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).Recommendation Strategy; Simulation Study; Way-Finding; Collaborative Filtering; Rating

An evaluation framework for data competitions in TEL

This paper presents a study describing the development of an Evaluation Framework (EF) for data competitions in TEL. The study applies the Group Concept Method (GCM) to empirically depict criteria and their indicators for evaluating software applications in TEL. A statistical analysis including multidimensional scaling and hierarchical clustering on the GCM data identified the following six evaluation criteria: 1.Educational Innovation, 2.Usability, 3.Data, 4.Performance, 5.Privacy, and 6.Audience. Each of them was operationalized through a set of indicators. The resulting Evaluation Framework (EF) incorporating these criteria was applied to the first data competition of the LinkedUp project. The EF was consequently improved using the results from reviewers' interviews, which were analysed qualitatively and quantitatively. The outcome of these efforts is a comprehensive EF that can be used for TEL data competitions and for the evaluation of TEL tools in general. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11200-8_6.EC/FP7/LinkedUpEC/FP7/DURAAR

CD95 maintains stem cell-like and non-classical EMT programs in primary human glioblastoma cells

Glioblastoma (GBM) is one of the most aggressive types of cancer with limited therapeutic options and unfavorable prognosis. Stemness and non-classical epithelial-to-mesenchymal transition (ncEMT) features underlie the switch from normal to neoplastic states as well as resistance of tumor clones to current therapies. Therefore, identification of ligand/receptor systems maintaining this privileged state is needed to devise efficient cancer therapies. In this study, we show that the expression of CD95 associates with stemness and EMT features in GBM tumors and cells and serves as a prognostic biomarker. CD95 expression increases in tumors and with tumor relapse as compared with non- tumor tissue. Recruitment of the activating PI3K subunit, p85, to CD95 death domain is required for maintenance of EMT-related transcripts. A combination of the current GBM therapy, temozolomide, with a CD95 inhibitor dramatically abrogates tumor sphere formation. This study molecularly dissects the role of CD95 in GBM cells and contributes the rational for CD95 inhibition as a GBM therapy

Learning analytics in European higher education–trends and barriers

Learning analytics (LA) as a research field has grown rapidly over the last decade. However, adoption of LA is mostly found to be small in scale and isolated at the instructor level. This paper presents an exploratory study on institutional approaches to LA in European higher education and discusses prominent challenges that impede LA from reaching its potential. Based on a series of consultations with senior managers from 83 different higher education institutions in 24 European countries, we observe that LA is primarily perceived as a tool to enhance teaching and institutional management. As a result, teaching and support staff are found to be the main users of LA and the target audience of training support. In contrast, there is little evidence of active engagement with students or using LA to develop self-regulated learning skills. We highlight the importance of grounding LA in learning sciences and including students as a key stakeholder in the design and implementation of LA. This paper contributes to our understanding of the development of LA in European higher education and highlights areas to address in both practice and research. © 2020 Elsevier LtdThis work was supported by the Erasmus+ Programme of the European Union [562080-EPP- 1-2015-1-BE-EPPKA3-PI-FORWARD]. The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission will not be held responsible for any use which may be made of the information contained therein. We would like to thank the participant of this study for their generous contributions

Not Yet Ready for Everyone: An Experience Report about a Personal Learning Environment for Language Learning

Abstract. A Personal Learning Environment (PLE) is a mash-up of learning services. It enables students and teachers to assemble a work environment that is adapted to a domain and specific individual needs. In this article, we report on our experiences on using a PLE for Lan-guage Learning in five French lectures at the Shanghai Jiao Tong Uni-versity Continuing Education School. We found that while a PLE has the potential to simplify access to and usage of Web sites and services for language learning, students will use it only if properly motivated. Fur-thermore, at the time being, difficulties that result from the user interface and technical implementation make the interactions with PLEs difficult. The problems need to be overcome in order for PLEs to become adopted by the average, not technically highly literate students and teachers. Key words: PLE, mash-up, experience report

The Games Realising Effective & Affective Transformation (GREAT) Project – A pathway to sustainable impact on climate change policy

In this paper the authors introduce the Games Realising Effective and Affective Transformation (GREAT) research project. This EU funded intervention posits the application of digital games, game making and games technologies, as a realisable sustainable solution to actively engage citizens in meaningful dialogue with governments to address the global challenge of climate change. The primary objective of the intervention is to facilitate citizens by using emergent technologies, to provide input into developing national and international policy priorities to address the challenges presented by global climate change, technologies that are both available and accessible. The GREAT project commenced on 01 February 2023 and brings together leading scientists in academia and the games industry in a single programme of research and innovation. The Project aims to establish new forms of social engagement and encourage meaningful dialogue between citizens and senior policy stakeholders (policy makers, policy implementers, political parties, and affected citizens)

The Proof of the Pudding: Examining Validity and Reliability of the Evaluation Framework for Learning Analytics

While learning analytics (LA) is maturing from being a trend to being part of the institutional toolbox, the need for more empirical evidences about the effects for LA on the actual stakeholders, i.e. learners and teachers, is increasing. Within this paper we report about a further evaluation iteration of the Evaluation Framework for Learning Analytics (EFLA) that provides an efficient and effective measure to get insights into the application of LA in educational institutes. For this empirical study we have thus developed and implemented several LA widgets into a MOOC platform’s dashboard and evaluated these widgets using the EFLA as well as the framework itself using principal component and reliability analysis. The results show that the EFLA is able to measure differences between widget versions. Furthermore, they indicate that the framework is highly reliable after slightly adapting its dimensions

Towards a Social Trust-Aware Recommender for Teachers

Fazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2014). Towards a Social Trust-aware Recommender for Teachers. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 177-194): Springer New York.Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this . The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trust-based recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the re-quirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical ev-idence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ul-timately be deployed in the ODS platform.NELLL, EU 7th framework Open Discovery Spac