Campus Virtuales

La Universidad en la Nube

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University & the Cloud
La Universidad y la Nube

Daniel Burgos. Logroño (La Rioja, España).

Rubén González Crespo. Logroño (La Rioja, España).

Fabio Nascimbeni. Sao Paulo (Brazil).

Resumen / Abstract



Palabras Clave / Keywords

Referencias / References

K-12, P. (2014). Pearson K-12.

Mariana Carroll, P. K. (2012). Securing Virtual and Cloud Environments. Cloud Computing and Services Science, 73-90.

Nagel, D. (2013). Cloud Computing To Make Up 35% of K-12 IT Budgets in 4 Years. The Journal.


Cómo citar / How to cite

Burgos, D., González, R. & Nascimbeni, F. (2014). La Universidad y la Nube. Campus virtuales, 3(1), 7-10.

Burgos, D., González, R. & Nascimbeni, F. (2014). University & the Cloud. Campus virtuales, 3(1), 7-10.

La Universidad en la Nube

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LIME: un modelo de recomendación para entornos de aprendizaje online formal/informal
LIME: a model of recommendation for online learning environments formal/informal

Alberto Corbí. Logroño (La Rioja, España)

Daniel Burgos. Logroño (La Rioja, España)

Resumen / Abstract

En los modelos e implementaciones sobre eLearning (conocidos habitualmente como sistemas Gestores de Aprendizaje o LMS) se da una aparente ausencia de conexión entre las actividades de índole formal e informal. Además, la metodología online se focalice en el establecimiento de un set de unidades y objetos de aprendizaje, así como tests y recursos como foros de discusión, blogs personales y mensajería. Ignoran, por tanto, todo el potencial del aprendizaje que surge de la interrelación entre el LMS, redes sociales y otras fuentes externas. Gracias a este comportamiento, a la interacción del usuario y a la labor de seguimiento y consejo personalizado por parte de un tutor, puede mejorar esta experiencia de aprendizaje. Se ha diseñado y desarrollado un modelo de aprendizaje online adaptativo para redes sociales de ámbito restringido, que da relevancia a este enfoque. Además, se ha programado un módulo de software que implementa este modelo conceptual de manera práctica y empleando para ello estándares promulgados por el IMS Global y tecnologías web. Finalmente se presenta el despliegue técnico de este producto entorno a un sistema gestor de contenidos académicos real.

In current eLearning models and implementations (e.g. Learning Management Systems-LMS) there is a lack of engagement between formal and informal activities. Furthermore, the online methodology focuses on a standard set of units of learning and learning objects, along with pre- defined tests, and collateral resources like, i.e. discussion for a and message wall. They miss the huge potential of learning via the interlacement of social networks, LMS and external sources. Thanks to user behavior, user interaction, and personalized counseling by a tutor, learning performance can be improved. We design and develop an adaptation eLearning model for restricted social networks, which supports this approach. In addition, we build a practical eLearning software module, based on standards from IMS Global and web technologies, that implements this conceptual model in a real application case. We present a preliminary deployment status on a modern learning management system.

Palabras Clave / Keywords

Aprendizaje potenciado mediante la tecnología, Personalización en aprendizaje a distancia, Redes sociales, Modelo Educativo Conceptual, eLearning, LMS.

Technology-enhanced Learning, eLearning, Personalization, Social Networks, Conceptual Educational Model, LMS.

Referencias / References

[1] R. A. Bjork, (1999). "Assessing our own competence: Heuristics and illusions," in Attention and performance XVII. Cognitive regulation of performance: Interaction of theory and application, D. Gopher and A. Koriat, Eds. Cambridge, MA: MIT Press, pp. 435-459.

[2] V. Romero and D. Burgos, (2010). "Meta-Mender: A meta-rule based recommendation system for educational applications," presented at Proceedings of the Workshop on Recommender Systems for Technology Enhanced Learning, RecsysTEL, Barcelona, Spain.

[3] V. Romero, D. Burgos, and A. Pardo, (2011). "Meta-rule based Recommender Systems for Educational Applications," in Educational Recommender Systems and Technologies: Practices and Challenges, O. Santos and J. Boticario, Eds.: Information Science-Idea Group.

[4] B. White and J. Frederiksen, (2005). "A theoretical framework and approach for fostering metacognitive development," Educational Psychologist, vol. 40, pp. 211–223.

[5] G. Linden, B. Smith, and Y. J., (2003). " recommendations: Item-to-item collaborative filtering," Internet Computing IEEE, vol. 7, pp. 76-80.

[6] B. Marlin, (2003). "Modeling user rating profiles for collaborative filtering," in Advances in neural information processing systems, S. Thrun, L. K. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press.

[7] S. Y. Chen and G. D. Magoulas, (2005). Adaptable and Adaptive Hypermedia Systems. Hershey, PA: IRM Press.

[8] K. I. Ghauth and N. A. Abdullah, "Learning materials recommendation using good learners’ ratings and content-based filtering," Education Technology Research and Development.

[9] T. Kerkiri, A. Manitsaris, and A. Mavridou, (2007). Reputation metadata for recommending personalized e-learning resources. Uxbridge: IEEE Computer Society.

[10] C. Romero, S. Ventura, P. D. De Bra, and C. D. Castro, (2003). "Discovering prediction rules in AHA! courses.," presented at 9th International User Modeling Conferenc.

[11] D. Burgos, C. Tattersall, and R. Koper, (2007). "How to represent adaptation in eLearning with IMS Learning Design" Interactive Learning Environments, vol. 15, pp. 161-170.

[12] J. J. Rocchio, (1971). Relevance feedback in information retrieval, in the SMART Retrieval System. Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice Hall, Inc.

[13] D. Burgos. (2013). "L.I.M.E. A recommendation model for informal and formal learning, engaged". International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI, 2, 79-86. DOI: 10.9781/ijimai.2013.2211.

[14] Forment M., Casan-Guerrero M.J., Conde M., García F.J., and Severance C. (2011). “Interoperability for LMS: the missing piece to become the common place for e-learning innovation”. International Journal of Knowledge and Learning. Vol. 6, pp. 130-141.

[15] Paulo J. and Queirós R., (2011). “Using the Learning Tools Interoperability Framework for LMS Integration in Service Oriented Architectures”. Conference Proceedings in Technology Enhanced Learning, TECH-EDUCATION '11. Springer Verlag.

[16] S. Saigaonkar and M. Rao. (2010). “XML filtering system based on ontology”. A2CWiC ’10: Proc. of the 1st Amrita ACMW Celebration on Women in Computing in India, pages 1–6.

[17] J. Cheney, S. Lindley, and P. Wadler. (2013). “A practical theory of language-integrated query”. In Proceedings of the 18th ACM SIGPLAN international conference on Functional programming (ICFP '13). ACM, New York, NY, USA, 403-416.

[18] Bosch, H, Heinrich, J., Muller, C., Hoferlin, B., Reina, G., Hoferlin, M., Worner, M. and Koch S., (2009). “Innovative filtering techniques and customized analytics tools”. Visual Analytics Science and Technology. VAST 2009. IEEE Symposium.

[19] Gonzalez, M.A.C., Penalvo, F.J.G., Guerrero, M.J.C.; Forment, M.A., (2009). “Adapting LMS Architecture to the SOA: An Architectural Approach”. Internet and Web Applications and Services, 2009. ICIW '09. pp. 322-327.

C[20] Kelly D. and Thorn K. (2013). “Should Instructional Designers care about the Tin Can API?”. eLearn Magazine, Issue 3.

Cómo citar / How to cite

Corbí, A. & Burgos, D. (2014). LIME: un modelo de recomendación para entornos de aprendizaje online formal/informal. Campus virtuales, 3(1), 12-20.

Corbí, A. & Burgos, D. (2014). LIME: a model of recommendation for online learning environments formal/informal. Campus virtuales, 3(1), 12-20.

La Universidad en la Nube

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Am I doing well? A4Learning as a self-awareness tool to integrate in Learning Management Systems
¿Lo estoy hacien bien? A4Learning como una herramienta de auto-conciencia de integración en los Sistemas de Gestión de Aprendizaje

Luis De La Fuente Valentín. Logroño (La Rioja, España)

Daniel Burgos. Logroño (La Rioja, España)

Resumen / Abstract

Most current online education scenarios use a Learning Management System (LMS) as the basecamp for the course activities. The LMS offers some centralized services and also integrates functionality from third party services (cloud services). This integration enriches the platform and increases the educational opportunities of the scenario. In such a distance scenario, with the students working in different physical spatial locations, they find difficult to determine if their activity level matches the expectation of the course. A4Learning performs a daily-updated analysis of learners’ activities by establishing the similarity between two given students. That is, finds students that are doing similar things in the Learning Management System. Then, the system finds and represents how similar students have similar achievements in the course. A4Learning can be integrated within the LMS to provide the students with a visual representation their similarity with others as an awareness mechanism, so that the students can determine the achievements of similar students in previous courses and estimate their own performance.

Palabras Clave / Keywords

Self-awareness, Learning analytics, Learning management system, Similarity measurement, eLearning, LMS.

Referencias / References

[1] M. Cocea, and W. Stephan. (2009). "Log file analysis for disengagement detection in e-Learning environments." User Modeling and User-Adapted Interaction 19, no. 4. pp. 341-385.

[2] J.P. Campbell, P.B. DeBlois, and D.G. Oblinger. (2007). "Academic analytics: A new tool for a new era." Educause Review 42, no. 4.

[3] M. Munoz-Organero, P. J. Munoz-Merino, and C. Delgado Kloos. (2010). "Student behavior and interaction patterns with an LMS as motivation predictors in E-learning settings." IEEE Transactions on Education, 53, no. 3. pp. 463-470.

[4] V.A. Romero-Zaldivar, A. Pardo, D. Burgos, and C. Delgado Kloos. "Monitoring student progress using virtual appliances: A case study." Computers & Education 58, no. 4. 2012. pp.1058-1067.

[5] M. Wang, J. Peng, B. Cheng, H. Zhou, and J. Liu. (2011). "Knowledge Visualization for Self-Regulated Learning." Educational Technology & Society 14, no. 3. pp. 28-42.

[6] S. Govaerts, K. Verbert, E. Duval, and A. Pardo. "The student activity meter for awareness and self-reflection." In CHI'12 Extended Abstracts on Human Factors in Computing Systems, ACM. pp. 869-884.

[7] J.L. Santos, K. Verbert, S. Govaerts, and E. Duval. (2012). "Addressing learner issues with StepUp!: an Evaluation." In Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013. pp. 14-22.

[8] O. McGrath. (2010). "Data Mining User Activity in Free and Open Source Software (FOSS)/Open Learning Management Systems." International Journal of Open Source Software and Processes (IJOSSP) 2, no. 1. pp. 65-75.

[9] C. Romero, S. Ventura, and E. García. (2008). "Data mining in course management systems: Moodle case study and tutorial." Computers & Education 51, no. 1. pp. 368-384.

[10] G. Siemens, and R. Baker. (2012). "Learning analytics and educational data mining: Towards communication and collaboration." In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, ACM. pp. 252-254.

[11] D. Keim, G. Andrienko, J.D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon. (2008). Visual analytics: Definition, process, and challenges. Springer Berlin Heidelberg.

[12] J.A. Larusson, and R. Alterman. (2009). "Visualizing student activity in a wiki-mediated co-blogging exercise." In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, ACM. pp. 4093-4098.

[13] R. Mazza, and V. Dimitrova. (2004). "Visualising student tracking data to support instructors in web-based distance education." In Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, ACM. pp. 154-161.

[14] J. O'Donovan, B. Smyth, B. Gretarsson, S. Bostandjiev, and T. Höllerer. (2008). "PeerChooser: visual interactive recommendation." InProceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM. pp. 1085-1088.

[15] C. Carmean, and P. Mizzi. (2010). "The Case for Nudge Analytics." Educause Quarterly 33, no. 4.

[16] de-La-Fuente-Valentín, L., Pérez-Sanagustín, M., Hernández-Leo, D., Pardo, A., Blat, J., & Delgado Kloos, C. (2014). Technological support for the enactment of collaborative scripted learning activities across multiple spatial locations. Future Generation Computer Systems, 31, 223-237.

Cómo citar / How to cite

De La Fuente, L. & Burgos, D. (2014).¿Lo estoy hacien bien? A4Learning como una herramienta de auto-conciencia de integración en los Sistemas de Gestión de Aprendizaje. Campus virtuales, 3(1), 32-40.

De La Fuente, L. & Burgos, D. (2014). Am I doing well? A4Learning as a self-awareness tool to integrate in Learning Management Systems. Campus virtuales, 3(1), 32-40.

La Universidad en la Nube

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Cloud Cuckoo Land: evidence from a study of student drop-out
Cloud Cuckoo Land: evidencia de un estudio de los estudiantes de deserción

Joe Cullen. London (United Kingdom)

Cristina Castellanos. London (United Kingdom)

Resumen / Abstract

This paper considers some of the issues around the migration of higher education services to ‘the cloud’, selecting MOOCs as an example of one element of service delivery that is being seen as an example of how new forms of distributed services can revolutionise higher education – in particular by opening up access to more people from diverse backgrounds. The paper presents some counter-arguments to this view, and explores whether these new technologies of teaching and learning are able to preserve the integrity of ‘reflexive dialogue’ that seems to reflect the core value of our higher education institutions. It presents evidence from an EU-funded project – STAY IN – which is researching student drop out and how it can be reduced through on-line services – as a contribution to these debates.

Palabras Clave / Keywords

Higher education, Social inclusion, Cloud, MOOC, Drop out, Learning, on-line services, Organisation.

Referencias / References

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Cómo citar / How to cite

Cullen, J. & Castellanos, C. (2014). Cloud Cuckoo Land: evidencia de un estudio de los estudiantes de deserción. Campus virtuales, 3(1), 22-30.

Cullen, J. & Castellanos, C. (2014). Cloud Cuckoo Land: evidence from a study of student drop-out. Campus virtuales, 3(1), 22-30.

La Universidad en la Nube

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Active Algorithms: Sociomaterial Spaces in the E-learning and Digital Cultures MOOC
Algoritmos Activos: Espacios Sociomateriales en los E-Learning y en las Culturas digitales MOOC

Jeremy Knox. Edinburgh (United Kingdom)

Resumen / Abstract

This paper will explore two examples from the design, structure and implementation of the ‘E-learning and Digital Cultures’ Massive Open Online Course (MOOC) from the University of Edinburgh in partnership with Coursera. This five week long course (known as the EDCMOOC) was delivered twice in 2013, and is considered an atypical MOOC in its utilisation of both the Coursera platform and a range of social media and open access materials. The combination of distributed and aggregated structure will be highlighted, examining the arrangement of course material on the Coursera platform and student responses in social media. This paper will suggest that a dominant instrumentalist view of technology limits considerations of these systems to merely enabling or inhibiting educational aims. The subsequent discussion will suggest that sociomaterial theory offers a valuable framework for considering how educational spaces are produced through relational practices between humans and non-humans. An analysis of You Tube and a bespoke blog aggregator will show how the algorithmic properties of these systems perform functions that cannot be reduced to the intentionality of either the teachers using these systems, or the authors who create the software, thus constituting a complex sociomaterial educational enactment.

Palabras Clave / Keywords

MOOC, Sociomaterial, Instrumentalism, Essentialism, Determinism, Blog aggregation, You Tube, Space.

Referencias / References

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Cómo citar / How to cite

Knox, J. (2014). Algoritmos Activos: Espacios Sociomateriales en los E-Learning y en las Culturas digitales MOOC. Campus virtuales, 3(1), 42-55.

Knox, J. (2014). Active Algorithms: Sociomaterial Spaces in the E-learning and Digital Cultures MOOC. Campus virtuales, 3(1), 42-55.