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

Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning

Drachsler, 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

Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

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

ReMashed - An Usability Study of a Recommender System for Mash-Ups for Learning

The following article presents an usability study of a Mash-up Personal Learning Environment called ReMashed that recommends items from the emerging information of a Learning Network. In ReMashed users can specify certain Web 2.0 services and combine them in a Mash-Up Personal Learning Environment. The users can rate information from an emerging amount of Web 2.0 information of a Learning Network and train a recommender system for their particular needs. In total 49 participants from 8 different countries registered to evaluate the ReMashed system. The participants contributed Web 2.0 contents and used the recommender system for one month. The evaluation was concluded with an online questionnaire where most of the participants were positive about the ReMashed system and offered helpful ideas for future developments

Les systèmes de recommandations pour soutenir l'agentivité des enseignantes et des enseignants au collégial dans leur développement professionnel

L’objectif général de cette thèse est de contribuer au soutien du développement professionnel des enseignantes et des enseignants au collégial en investiguant leur agentivité avec le numérique. L’agentivité est définie ici comme la capacité à définir et à poursuivre des objectifs de développement professionnel. Cette recherche, qui met en œuvre une méthodologie d’expérimentation de devis, s’est déroulée en trois phases. Dans une première phase, nous avons investigué des besoins d’enseignantes du collégial en nous intéressant aux buts que devait soutenir une plateforme numérique soutenant l’exercice de l’agentivité. Pour y parvenir, trois ateliers de codesign ont été menés et analysés sous l’angle du modèle de l’expérience utilisateur de Hassenzahl (2003). Ces ateliers, inspirés des Future Workshop (Muller et Druin, 2012), ont permis d’identifier des buts motivationnels des participantes, c’est-à-dire ce qu’elles souhaitaient qu’une plateforme misant sur l’exercice d’agentivité puisse combler : faire du développement professionnel une priorité, poser un regard réflexif sur l’innovation, faciliter l’accès aux ressources, et faciliter les échanges et le partage. Les ateliers ont aussi permis d’identifier des buts fonctionnels et opérationnels, c’est-à-dire les fonctionnalités qui permettent de combler les besoins motivationnels. Parmi eux, plusieurs se sont révélés être liés aux systèmes de recommandations, qui sont des outils et techniques qui suggèrent les items les plus susceptibles d’intéresser un utilisateur (Ricci et al., 2015). C’est pourquoi une revue systématique de la littérature a été réalisée afin d’identifier notamment les techniques utilisées et les façons d’évaluer les systèmes de recommandations utilisés dans un contexte d’apprentissage. Dans cette deuxième phase de cette recherche doctorale, ce sont 56 articles scientifiques revus par les pairs, parus entre 2008 et 2018, qui ont été analysés sous trois grandes questions et une cinquantaine d’aspects. Ils ont permis d’orienter le développement de la plateforme, dont l’implantation et les améliorations ont constitué la troisième et dernière phase. Cette phase s’est déroulée en trois itérations de conception, intervention, analyse et amélioration. Durant chacune des itérations, les six participantes ont expérimenté la plateforme, répondu à un questionnaire et reçu une rétroaction personnalisée. Le questionnaire visait à analyser le potentiel de la plateforme pour répondre aux buts motivationnels identifiés à la première phase, à analyser la satisfaction à l’égard des ressources recommandées (Erdt et al., 2015), de même qu’à analyser l’expérience utilisateur (Hassenzahl, 2003). Pour des fins d’analyse, les réponses aux questionnaires ont été croisées avec les données entrées par le personnel enseignant dans la plateforme ainsi que les actions faites dans la plateforme et entrées au journal d’évènements. Cette analyse nous a permis d’observer une augmentation de la perception du soutien à l’agentivité des enseignantes, en particulier la capacité de la plateforme à faciliter l’accès aux ressources pour mieux connaitre les occasions de développement professionnel et pour accéder aux activités de développement professionnel les plus appropriées. Au fil des itérations, nous avons également observé une augmentation de la satisfaction à l’égard des ressources recommandées grâce à une approche basée sur le contenu. La variation de la moyenne globale des aspects hédoniques et pragmatiques est elle aussi positive à chacune des itérations. C’est dire que le codesign d’un environnement numérique dans le cadre d’une recherche avec des enseignantes a été une forme de développement professionnel, et le codesign en contexte de développement professionnel a favorisé l’exercice de l’agentivité des participantes. Le design itératif de cette recherche a contribué à faire du développement professionnel une priorité, un besoin identifié durant la phase de codesign. Cette étude a permis d’identifier et de mettre en œuvre des pistes permettant de faciliter l’exercice de l’agentivité des enseignantes et des enseignants.The general objective of this thesis is to contribute to supporting the professional development of college teachers by investigating their agency with the support of digital technology. Agency is defined here as the ability to define and pursue professional development goals. This research, which implements a design-based research methodology, was carried out in three phases. In the first phase, we investigated the needs of college teachers by looking at the goals that a digital platform supporting the exercise of agency should support. To achieve this, three codesign workshops were conducted and analyzed from the perspective of Hassenzahl's (2003) user experience model. These workshops, inspired by the Future Workshop (Muller & Druin, 2012), made it possible to identify the participants' “be-goals”, i.e., what they wanted a platform based on the exercise of agency to achieve: to make professional development a priority, to take a reflective look at innovation, to facilitate access to resources, and to facilitate exchanges and sharing. The workshops also made it possible to identify “do-goals” and “motor-goals”, i.e., the functionalities that make it possible to meet be-goals. Among them, several were found to be related to recommendation systems, which are tools and techniques that suggest the items most likely to interest a user (Ricci et al., 2015). For this reason, a systematic review of the literature was conducted to identify the techniques used and the ways to evaluate the recommendations systems used in a learning context. In this second phase of this doctoral research, 56 peer-reviewed scientific articles, published between 2008 and 2018, were analyzed under three main questions and about 50 aspects. They were used to help guide the development of the platform, whose implementation and improvements constituted the third and final phase. This phase took place in three iterations of design/implementation, intervention, analysis and improvement. During each iteration, the six participants experimented with the platform, answered a questionnaire and received personalized feedback. The questionnaire aimed to analyze the platform's potential to meet the motivational goals (“be-goals”) identified in the first phase, to analyze satisfaction with the recommended resources (Erdt et al., 2015), and to analyze the user experience (Hassenzahl, 2003). For analysis purposes, the responses to the questionnaires were cross-referenced with the data entered by the teaching staff in the platform as well as the actions taken in the platform and entries in the event log. This analysis allowed us to observe an increase in the perception of support for teachers' agency, in particular the platform's ability to facilitate access to resources to learn more about professional development opportunities and to access the most appropriate professional development activities. Over the iterations we also observed an increase in satisfaction with the recommended resources through a content-based approach. The variation in the overall average of the hedonic and pragmatic aspects is also positive in each iteration. This means that codesigning a digital environment in the context of research with female teachers was a form of professional development, and codesigning in a professional development context promoted the participants' exercise of agency. The iterative design of this research contributed to making professional development a priority, a need identified during the codesign phase. This study made it possible to identify and implement avenues to facilitate teachers' exercise of agency

Sistema de recomendación de recursos basado en filtrado colaborativo para la plataforma edX

En los últimos años ha surgido el concepto de MOOC (Massive Open Online Course) así como plataformas asociadas para dar soporte a estos cursos. Las plataformas diseñadas para este fin ofertan cursos en línea accesibles a nivel mundial de manera abierta, por lo que tienen que afrontar el reto de dar servicio a un gran número de usuarios, del orden de miles o decenas de miles, de forma que su experiencia de aprendizaje sea óptima y lo más productiva posible. Una de las plataformas de MOOCs más populares es edX. En este proyecto se ha desarrollado un recomendador de recursos educativos para la plataforma edX, incluyendo el diseño de un algoritmo de recomendación de los problemas que el usuario debería realizar a continuación. El algoritmo se basa en recomendar problemas con los que han interaccionado, realizado y aprobado usuarios similares al usuario al que se va a realizar la recomendación. La similitud de los usuarios se calcula en función de las notas obtenidas en los distintos problemas del curso. En cualquier momento el usuario puede seleccionar la pestaña de recomendación (Recommend Me!), situada en la parte superior del sistema de gestión del aprendizaje (LMS) junto al resto de las pestañas del curso, y obtener un número determinado de problemas propuestos. Para el desarrollo de esta aplicación se ha trabajado con una instancia de la plataforma edX y se ha utilizado el framework Django. Posteriormente, para la evaluación del algoritmo se han creado dos cursos ficticios en el sistema de gestión de contenidos (CMS) de la plataforma e, igualmente, registrar de forma ficticia varios alumnos en estos cursos para hacer una serie de pruebas y poder garantizar la precisión de las recomendaciones generadas por el algoritmoIngeniería Técnica en Telemátic

Living analytics methods for the social web

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