Am I doing well? A4Learning as a self-awareness tool to integrate in Learning Management Systems

  1. Luis De La Fuente Valentín
  2. Daniel Burgos
Revista:
Campus Virtuales

ISSN: 2255-1514

Año de publicación: 2014

Volumen: 3

Número: 1

Páginas: 32-40

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Campus Virtuales

Resumen

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.

Referencias bibliográficas

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • D. Keim, G. Andrienko, J.D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon. (2008). Visual analytics: Definition, process, and challenges. Springer Berlin Heidelberg.
  • 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.
  • 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.
  • 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.
  • C. Carmean, and P. Mizzi. (2010). "The Case for Nudge Analytics." Educause Quarterly 33, no. 4.
  • 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.
Fuente de los datos: Dialnet