208 research outputs found
Learning analytics as a "middle space"
No full textLearning Analytics, an emerging field concerned with an- alyzing the vast data “given off” by learners in technology supported settings to inform educational theory and prac- tice, has from its inception taken a multidisciplinary ap- proach that integrates studies of learning with technological capabilities. In this introduction to the Proceedings of the Third International Learning Analytics & Knowledge Con- ference, we discuss how Learning Analytics must function in the “middle space” where learning and analytic concerns meet. Dialogue in this middle space involves diverse stake- holders from multiple disciplines with various conceptions of the agency and nature of learning. We hold that a sin- gularly unified field is not possible nor even desirable if we are to leverage the potential of this diversity, but progress is possible if we support “productive multivocality” between the diverse voices involved, facilitated by appropriate use of boundary objects. We summarize the submitted papers and contents of these Proceedings to characterize the voices and topics involved in the multivocal discourse of Learning Analytics.sponsorship: Research Foundation Flanders (FWO)status: Publishe
Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning
No full textDrachsler, 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
EATEL - Special Interest Group
No full textDrachsler, H., & Verbert, K. (2011, September). dataTEL - Data-driven Research and Learning Analytics. EATEL Special Interest Group. Presentation given at EC-TEL 2011, Palermo, Italy.Presentation at ECTEL11 for the launch of the SIG dataTEL under the umbrella of the European Association of TEL (EATEL).dataTEL, NeLLL, AlterEg
Introduction slides for RecSysTEL workshop
No full textDrachsler, H., Manouselis, N., Santos, O., & Verbert, K. (2012, 19 September). Introduction slides for RecSysTEL workshop. Presentation at the 2nd Workshop on Recommender Systems for Technolgy Enhancend Learning (RecSysTEL 2012) at EC-TEL 2012, Saarbrücken, Germany.Introduction slides for RecSysTEL workshop at ECTEL12 conference, Saarbruecken, Germany.AlterEgo, dataTEL, Open Discovery Spac
Creating effective learning analytics dashboards: lessons learnt
No full textLearning Analytics (LA) dashboards help raise student and teacher awareness regarding learner activities. In blog-supported and inquiry-based learning courses, LA data is not limited to student activities, but also contains an abundance of digital learner artefacts, such as blog posts, hypotheses, and mind-maps. Exploring peer activities and artefacts can help students gain new insights and perspectives on learning efforts and outcomes, but requires effort. To help facilitate facilitate and promote this exploration, we present the lessons learnt during and guidelines derived from the design, deployment and evaluation of five dashboards.sponsorship: The European Community's 7th Framework Programme (FP7/2007-2013) under grant agreement No 318499 - weSPOT project
The Erasmus+ programme, Key Action 2 Strategic Partnerships, of the European Union under grant agreement 2015-1-UK01-KA203-013767 – ABLE project.status: Publishe
Towards a Social Trust-aware Recommender for Teachers
No full textFazeli, 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
Recommender systems for technology enhanced learning: research trends and applications
No full text© Springer Science+Business Media New York 2014. As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.status: Publishe
A trust-based social recommender for teachers
No full textFazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2012). A trust-based social recommender for teachers. In N. Manouselis, H. Drachsler, K. Verbert, & O. C. Santos (Eds.), 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012) in conjunction with the 7th European Conference on Technology Enhanced Learning (EC-TEL 2012) (pp. 49-60). September, 18-19, 2012, Saarbrücken, Germany.Online communities and networked learning provide teachers with social learning opportunities to interact and collaborate with others in order to develop their personal and professional skills. In this paper, Learning Networks are presented as an open infrastructure to provide teachers with such learning opportunities. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable resources for them. In this paper, recommender systems are introduced as a potential solution to address this issue. Unfortunately, most of the educational recommender systems cannot make accurate recommendations due to the sparsity of the educational datasets. To overcome this problem, we propose a research approach that describes how one may take advantage of the social data which are obtained from monitoring the activities of teachers while they are using our social recommender.NELLL, Open Discovery Space (ODS
Evaluating student-facing learning dashboards of affective states
No full textDetection and visualizations of affective states of students in computer based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of such visualizations with students in real life settings is an open issue. This research reports on our experiences from the use of four different types of dashboard visualizations in two user studies (n=115). Students who participated in the studies were bachelor and master level students from two different study programs at two universities. The results indicate that usability, measured by interpretability, perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotion awareness, still needs to be improved. The level of students awareness about their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge on visualization techniques. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques.sponsorship: This work is partially supported by the eMadrid project (funded by the Regional Government of Madrid) under grant no S2013/ICE-2715, the Commin project (funded by the Spanish Ministry of Economy and Competitiveness) under grant no IPT-2012-0883-430000 and the RESET project (Ministry of Economy and Competiveness) under grant RESET TIN2014-53199-C3-1-R. The research has been partially financed by the SURF Foundation of the Netherlands and the KU Leuven Research Council (grant agreement no. C24/16/017).status: Publishe
Fault diagnosis and maintenance optimization for interconnected systems: With applications to railway and climate control systems
No full textFor many systems, like medical devices, nuclear reactors, and transportation systems, an adequate maintenance optimization approach is essential to ensure high levels of reliability and safety while keeping operational costs low. A promising approach towards this goal is condition-based maintenance, which plans maintenance only when the system health indicates a need for it. To infer the system health, monitoring devices are installed to collect health-related data. The path from the monitoring data to a maintenance schedule then involves the following steps: 1. fault diagnosis, i.e. detecting abnormal system behavior and identifying its cause;2. failure prognosis, i.e. predicting future system health;3. maintenance optimization, i.e. determining the required type of maintenance as well as the optimal time to perform the maintenance task. Although various methods have been published for all three tasks, discrepancies still exist between the assumptions made in the literature and the conditions encountered in practice. These discrepancies include, e.g., unrealistic assumptions regarding the absence of component interdependencies and regarding the (number of) available monitoring signals. This thesis contributes to resolving these discrepancies by proposing methods for fault diagnosis, failure prognosis, and maintenance optimization, particularly focusing on narrowing the gap between theory and practice. When treating the individual tasks, the dependencies between fault diagnosis, failure prognosis, and maintenance optimization are explicitly taken into account.<br/
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