Use of activity logs to improve online collaboration
DOI:
https://doi.org/10.5944/ried.21.2.20641Keywords:
activity analytics, online learning, CSCL, higher education, activity logs.Abstract
This article presents a review of works that center their interest in eLearning platforms and the data mining of participants’ activity. The studies in this research area generate information, through the analysis of such logs and data, that is provided to the students in real time to help them to collaborate and learn through collaboration on the platform. There are studies from different areas of study such as Learning Analytics, Educational Data Mining, Group Awareness Tools or Interaction Analysis Tools. The review takes a double perspective: i) to analyze the data extracted from activity logs, their processing, the information generated and the ways to communicate it; and ii) to explorer the model and the instruments used to assess how the information provided impact on online collaborative processes and/or the learning. The conclusions emphasize that the models of collaborative learning that justifies the selection of the data extracted from the activity logs, the processing, the information generated and provided to the students and the way of communicating it, are not explicitly stated. In addition, important biases are detected because of not considering the multidimensional nature of the collaborative learning processes. Also, few studies analyze the relations between students' uses of the information provided and the quality of their collaborative processes and learning results. The very few studies that do analyze such relation do not go into depth on the changes in group dynamics caused by information.Downloads
References
Bodemer, D. (2011). Tacit guidance for collaborative multimedia learning. Computers in Human Behavior, 27(3), 1079–1086. https://doi.org/10.1016/j.chb.2010.05.016
Bodemer, D., y Dehler, J. (2011). Group awareness in CSCL environments. Computers in Human Behavior, 27(3), 1043-1045. https://doi.org/10.1016/j.chb.2010.07.014
Bravo, C., Redondo, M. A., Verdejo, M. F., y Ortega, M. (2008). A framework for process-solution analysis in collaborative learning environments. International Journal of Human Computer Studies, 66(11), 812-832. https://doi.org/10.1016/j.ijhcs.2008.08.003
Brown, M. (2011). Learning Analytics: The Coming Third Wave. EDUCAUSE Learning Initiative Brief, 1(4), 1‒4. Recuperado de http://www.educause.edu/blog/pkurkowski/ELIReleasesNewBriefonLearningA/229163
Buder, J. (2011). Group awareness tools for learning: Current and future directions. Computers in Human Behavior, 27(3), 1114–1117. https://doi.org/10.1016/j.chb.2010.07.012
Coll, C., Bustos, A., y Engel, A. (2011). Perfiles de participación y presencia docente distribuida en redes asíncronas de aprendizaje: la articulación del análisis estructural y de contenido. Revista de Educación, 354, 657–688. Recuperado de http://www.mecd.gob.es/dctm/revista-de-educacion/articulos-re354/re35426.pdf?documentId=0901e72b811e1d42
Coll, C., Bustos, A., y Engel, A. (2015). La información sobre el ejercicio de la influencia educativa como medio para favorecer la participación y el aprendizaje en un foro en línea. Infancia y Aprendizaje, 38, 368-401. https://doi.org/10.1080/02103702.2015.1016745
Coll, C., Bustos, A., Engel, A., de Gispert, I., y Rochera, M. J. (2013). Distributed Educational Influence and Computer-Supported Collaborative Learning. Digital Education Review, 24, 23-42. Recuperado de http://revistes.ub.edu/index.php/der/article/view/11274/pdf
Coll, C., Engel, A., y Bustos, A. (2009). Distributed Teaching Presence and Participants’ Activity Profiles: a theoretical approach to the structural analysis of Asynchronous Learning Networks. European Journal of Education, 44(4), 521-538. https://doi.org/10.1111/j.1465-3435.2009.01406.x
Coll, C., Engel, A., y Niño, S. (2017). La actividad de los participantes como fuente de información para promover la colaboración. Una analítica del aprendizaje basada en el modelo de Influencia Educativa Distribuida. RED, Revista de Educación a Distancia, 53. https://doi.org/10.6018/red/53/2
Cooper, H. M. (1988). Organizing knowledge syntheses: A taxonomy of literature reviews. Knowledge, Technology y Policy, 1(1), 104-126. https://doi.org/10.1007/BF03177550
Dawson, S., Gaševi, D., Siemens, G., y Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. En Proceedings of the fourth international conference on learning analytics and knowledge (pp. 231-240). ACM. https://doi.org/10.1145/2567574.2567585
Dillenbourg, P., Järvelä, S., y Fischer, F. (2009). The evolution of research on computer-supported collaborative learning: from design to orchestration. En N. Balacheff, S. Ludvigsen, T. de Jong, A. Lazonder y S. Barnes (Eds.), Technology-Enhanced Learning (pp. 3–19). Springer. https://doi.org/10.1007/978-1-4020-9827-7_1
Dimitracopoulou, A. (2008). Computer based Interaction Analysis Supporting Self-regulation: Achievements and Prospects of an Emerging Research Direction. Technology, Instruction, Cognition and Learning, 6(4), 291-314. Recuperado de http://www.oldcitypublishing.com/FullText/TICLfulltext/TICL6.4fulltext/TICLv6n4p291-314Dimitracopoulou.pdf
Engel, A. (2008). Construcción del conocimiento en entornos virtuales de enseñanza y aprendizaje. La interrelación entre los procesos de colaboración entre alumnos y los procesos de ayuda y guía del profesor. (Tesis doctoral, Universidad de Barcelona). Recuperado de http://www.tesisenxarxa.net/TDX-0123109-115623
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. https://doi.org/10.1504/IJTEL.2012.051816
Fessakis, G., Dimitracopoulou, A., y Palaiodimos, A. (2013). Graphical interaction analysis impact on groups collaborating through blogs. Educational Technology and Society, 16(1), 243–253. Recuperado de http://www.ifets.info/journals/16_1/21.pdf
García, E., Romero, C., Ventura, S., y de Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88. http://doi.org/10.1016/j.iheduc.2010.07.006
Janssen, J., y Bodemer, D. (2013). Coordinated Computer-Supported Collaborative Learning: Awareness and Awareness Tools. Educational Psychologist, 48(1), 40–55. https://doi.org/10.1080/00461520.2012.749153
Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J. y Järvenoja, H. (2015). Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools. Educational Technology Research and Development, 63, 125–142. https://doi.org/10.1007/s11423-014-9358-1
Jeong, H., y Hmelo-Silver, C. E. (2016). Seven affordances of computer-supported collaborative learning: How to support collaborative learning? How can technologies help? Educational Psychologist, 51(2), 247–265. https://doi.org/10.1080/00461520.2016.1158654
Jeong, H., Hmelo-Silver, C. E., y Yu, Y. (2014). An examination of CSCL methodological practices and the influence of theoretical frameworks 2005–2009. International Journal of Computer-Supported Collaborative Learning, 9(3), 305–334. https://doi.org/10.1007/s11412-014-9198-3
Kimmerle, J., y Cress, U. (2008). Group awareness and self-presentation in computer-supported information exchange. International Journal of Computer-Supported Collaborative Learning, 3(1), 85-97. https://doi.org/10.1007/s11412-007-9027-z
Kirschner, P. A., y Erkens, G. (2013). Toward a Framework for CSCL Research. Educational Psychologist, 48(1), 1–8. https://doi.org/10.1080/00461520.2012.750227
Kuosa, K., Distante, D., Tervakari, A., Cerulo, L., Fernández, A., Koro, J., y Kailanto, M. (2016). Interactive visualization tools to improve learning and teaching in online learning environments. International Journal of Distance Education Technologies, 14(1), 1-21. https://doi.org/10.4018/IJDET.2016010101
Kwon, K., Hong, R.-Y., y Laffey, J. M. (2013). The educational impact of metacognitive group coordination in computer-supported collaborative learning. Computers in Human Behavior, 29(4), 1271-1281. https://doi.org/10.1016/j.chb.2013.01.003
Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., … y Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6). https://doi.org/10.14742/ajet.3058
McCormick, J. (2013). Visualizing interaction: Pilot investigation of a discourse analytics tool for online discussion. Bulletin of the Technical Committee on Learning Technology, 15(2), 10–13. Recuperado de http://lttf.ieee.org/issues/april2013/McCormick.pdf
Mora, N., Caballé, S., y Daradoumis, T. (2016). Providing a multi-fold assessment framework to virtualized collaborative learning in support for engineering education. International Journal of Emerging Technologies in Learning, 11(7). https://doi.org/10.3991/ijet.v11i07.5882
Niño, S. (2017). El uso de la información sobre el ejercicio de la influencia educativa para la mejora de los procesos y los resultados del aprendizaje colaborativo en entornos digitales. (Tesis doctoral, Universidad de Barcelona).
Recuperado de http://psyed.edu.es/archivos/grintie/Tesis_SNino_junio17.pdf
Park, Y., y Jo, I.-H. (2015). Development of the learning analytics dashboard to support students’ learning performance. Journal of Universal Computer Science, 21(1), 110-133. https://doi.org/10.3217/jucs-021-01-0110
Pifarré, M., Cobos, R., y Argelagós, E. (2014). Incidence of group awareness information on students’ collaborative learning processes. Journal of Computer Assisted Learning, 30(4), 300–317. https://doi.org/10.1111/jcal.12043
Resta, P., y Laferrière, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19(1), 65-83. https://doi.org/10.1007/s10648-007-9042-7
Romero, C., y Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40, 601-618. https://doi.org/10.1109/TSMCC.2010.2053532
Roschelle, J. (2013). Special Issue on CSCL: Discussion. Educational Psychologist, 48, 67–70. https://doi.org/10.1080/00461520.2012.749445
Sangin, M., Molinari, G., Nüssli, M.-A. M.-A., y Dillenbourg, P. (2011). Facilitating peer knowledge modeling: Effects of a knowledge awareness tool on collaborative learning outcomes and processes. Computers in Human Behavior, 27(3), 1059–1067. https://doi.org/10.1016/j.chb.2010.05.032
Schoor, C., Kownatzki, S., Narciss, S., y Körndle, H. (2014). Effects of feeding back the motivation of acollaboratively learning group. Electronic Journal of Research in Educational Psychology, 12(1), 191-210. https://doi.org/10.14204/ejrep.32.13077
Siemens, G., y Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46, 30–32. https://doi.org/10.1145/2330601.2330605
Silius, K., Tervakari, A.-M. A.-M., y Kailanto, M. (2013). Visualizations of User Data in a Social Media Enhanced Web-based Environment in Higher Education. International Journal of Emerging Technologies in Learning, 8(S2), 13. https://doi.org/10.3991/ijet.v8iS2.2740
Stahl, G. (2015). A decade of CSCL. International Journal of Computer-Supported Collaborative Learning, 10(4), 337-344. https://doi.org/10.1007/s11412-015-9222-2
Stahl, G., Koschmann, T., y Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. En R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409-426). Cambridge, UK:Cambridge University Press. Recuperado de http://GerryStahl.net/cscl/CSCL_English.pdf
Wise, A. F., Vytasek, J., Hausknecht, S., y Zhao, Y. (2016). Developing learning analytics design knowledge in the “middle space”: The student tuning model and align design framework for learning analytics use. Journal of Asynchronous Learning Network, 20(2). Recuperado de https://olj.onlinelearningconsortium.org/index.php/olj/article/view/783/210
Xia, L. (2015). Reflections on on-line teaching and learning, based on learning process data. World Transactions on Engineering and Technology Education, 13(3). Recuperado de http://www.wiete.com.au/journals/WTE&TE/Pages/Vol.13,%20No.3%20(2015)/40-Xia-L.pdf
Zorrilla, M., Álvarez, E., y García-Saiz, D. (2015). A parametrisable method for measuring online attendance in e-learning tools. International Journal of Technology Enhanced Learning, 7(4), 289-308. https://doi.org/10.1504/IJTEL.2015.074185
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 RIED. Revista Iberoamericana de Educación a Distancia
This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.
