Abstract
Although learning indicators are now properly studied and published, it is still very difficult to manage them freely within most distance learning platforms. As all activity indicators need to collect and analyze properly traces of the learning activity, we propose to use these traces as a starting point for a platform independent Trace Based-Indicator Management System (TB-IMS). This approach allows learning indicators to be created and reused in such a way that there is no need to modify the computer code of the learning platform. This paper presents the underlying theory and how this theory is implemented in a first TB-IMS. This TB-IMS is illustrated through an actual learning situation based on a Moodle platform. This approach is compared with similar attempts to manage learning indicators properly and is available for use with any other learning platform, provided the TB-IMS can access the learning platform traces.
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Even if it can be considered that a "non interaction at a particular place and time" can be observed. In this case, the system constructs an event as an observation of a non interaction. This is a tricky point that is discussed in (Champalle et al. 2013).
Throughout the paper, we consider that the word "indicator" means: "a human learning indicator activity".
Moodle stands for: Modular Object-Oriented Dynamic Learning Environment.
This is not the case for the intermediate variables which can be used in the formula.
Empty means that the m-trace model is present but that there are no obsels in the instance part.
References
Barros, B., & Verdejo, F. M. (2000). Analyzing student interaction processes in order to improve collaboration. The DEGREE approach. International Journal of Artificial Intelligence in Education, 11(3), 221–241. http://ijaied.org/pub/1004/file/1004_paper.pdf.
Bézivin, J., & Gerbé, O. (2001). Towards a precise definition of the OMG/MDA framework. In Proceedings of the 16th International Conference on Automated Software Engineering, IEEE (pp. 273–280). San Diego, USA. https://pdfs.semanticscholar.org/6586/b19c6edf8850ac97fffb4ea6f65144200b1b.pdf.
Bratitsis, T., & Dimitracopoulou, A. (2009). Studying the effect of interaction analysis indicators on students’ selfregulation during asynchronous discussion learning activities. In Proceedings of the 9th International Conference on Computer Supported Collaborative Learning (pp. 601–605). Greece. https://www.researchgate.net/publication/221033728_Studying_the_effect_of_Interaction_Analysis_indicators_on_student’s_Selfregulation_during_asynchronous_discussion_learning_activities.
Butoianu, V., Vidal, P., & Broisin, J. (2012). A model-driven approach to actively manage TEL indicators. In T. Amiel, & B. Wilson (Eds.), Proceedings of the EdMedia: World Conference on Educational Media and Technology (pp. 1757–1765). Denver, Colorado, USA: Association for the Advancement of Computing in Education (AACE). https://www.researchgate.net/publication/230675845_A_Model-driven_Approach_to_Actively_Manage_TEL_Indicators.
Champalle, O., Sehaba, K., & Mille, A. (2013). Capitalize and share observation and analysis knowledge to assist trainers in professional training with simulation case of training and skills maintain of nuclear power plant control room staff. In Proceedings of the 5th International Conference on Computer Supported Education (pp. 627–632). Aachen, Germany. http://liris.cnrs.fr/Documents/Liris-6056.pdf.
Champin, P. A., Mille, A., & Prié, Y. (2013). Vers des traces numériques comme objets informatiques de premier niveau : Une approche par les traces modélisées. Journal of Intellectica, 59, 171–204. http://liris.cnrs.fr/Documents/Liris-5998.pdf.
Cordier, A., Lefevre, M., Champin, P. A., Georgeon, O., & Mille, A. (2013). Trace-based reasoning-modeling interaction traces for reasoning on experiences. In Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference (pp. 363–368). Pete Beach, Florida, USA. http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS13/paper/download/5903/6100.
Cordier, A., Lefevre, M., Champin, P. A., Mille, A., Georgeon, O., & Mathern, B. (2014). Connaissances et raisonnement sur les traces d’interaction. Journal of Intelligence Artificielle, 28(2–3), 375–396. https://pdfs.semanticscholar.org/92c3/fb8d4cb4d2a4f45b1320afb4ed99af271ad3.pdf.
Diagne, F. (2009). Instrumentation de la supervision par la réutilisation d’indicateurs: Modèles et Architecture. Doctoral dissertation, Joseph Fourier University, Grenoble, France. https://hal.inria.fr/tel-00366368/document.
Dillenbourg, P. (1999). What do you mean by collaborative learning?. Collaborative learning: Cognitive and computational approaches, 1, 1–19. http://tecfa.unige.ch/tecfa/publicat/dil-papers-2/Dil.7.1.14.pdf.
Dimitracopoulou, A. (2004). State of the art of interaction analysis: Interaction analysis indicators interaction and collaboration analaysis supporting teachers and students selfregulation (ICALTS). In Researsh report. JEIRP Deliverable D.26.1.1. Kaleidoscope NoE (pp. 153). https://hal.archives-ouvertes.fr/hal-00190145/document.
Dimitracopoulou, A., Petrou, A., Martinez, A., Marcos, J. A., Kollias, V., Jermann, P., et al. (2005). State of the art on interaction analysis for metacognitive support and diagnosis. In Researsh report JEIRP. D.31.1.1, EU 6th Framework programme priority 2, Information society technology, Kaleidoscope Network of Excellence (pp. 2–62). https://hal.archives-ouvertes.fr/hal-00190146/document.
Gendron, E. (2010). Cadre conceptuel pour l’élaboration d’indicateurs de collaboration à partir des traces d’activité. Doctoral dissertation, Claude Bernard Lyon1 University, France. https://tel.archives-ouvertes.fr/tel-00708083/document.
Iksal, S., Choquet, C., & Pham Thi Ngoc, D. (2010). Generic modeling of indicator with UTL-the collaborative action function example. In Proceedings of the 2nd International Conference on Computer Supported Education (pp. 114–119). Valencia, Spain. https://www.researchgate.net/publication/221130379_A_Generic_Modeling_of_Indicator_with_UTL_-_The_Collaborative_Action_Function_Example.
Jermann, P. R. (2004). Computer support for interaction regulation in collaborative problem-solving. Doctoral dissertation, Geneva University, Switzerland. http://tecfa.unige.ch/tecfa/research/theses/jermann2004.pdf.
Ji, M., Michel, C., George, S., & Lavoué, E. (2014). DDART: A dynamic dashboard for collection, analysis and visualization of activity and reporting traces. In Proceedings of the 9th European Conference on Technology Enhanced Learning (pp. 440–445). Graaz, Austria. https://hal-univ-lyon3.archives-ouvertes.fr/hal-01130922/document.
Laforcade, P., Nodenot, T., Choquet, C., & Caron, P. A. (2007). Model-driven engineering (MDE) and model-driven architecture (MDA) applied to the modeling and deployment of technology enhanced learning (TEL) systems: Promises, challenges and issues. In Architecture Solutions for ELearning Systems (pp. 116–136). https://www.researchgate.net/publication/281327763_Model-Driven_Engineering_MDE_and_Model-Driven_Architecture_MDA_applied_to_the_Modeling_and_Deployment_of_Technology_Enhanced_Learning_TEL_Systems_promises_challenges_and_issues.
Laperrousaz, C. (2007). Question de la réutilisation d’outils de suivi d’activités d’apprenants dans des plates-formes de formation en ligne. In Proceedings of the 3rd Conference on EIAH: Environnements Informatiques pour l’Apprentissage Humain (pp. 485–496). Lausanne, Switzerland. https://hal.archives-ouvertes.fr/hal-00161482/document.
Martinez, A., Dimitriadis, Y., Rubia, B., & Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers and Education, 41(4), 353–368. https://telearn.archives-ouvertes.fr/hal-00190427/document.
May, M., George, S., & Prévôt, P. (2011). TrAVis to enhance online tutoring and learning activities: Real time visualization of students tracking data. Journal of Interactive Technology and Smart Education, 8(1), 52–69. https://www.researchgate.net/publication/220373199_TrAVis_to_Enhance_Online_Tutoring_and_Learning_Activities_Real_Time_Visualization_of_Students_Tracking_Data.
Mazza, R., & Botturi, L. (2007). Monitoring an online course with the GISMO tool: A case study. Journal of Interactive Learning Research, 18(2), 251–265. https://www.researchgate.net/publication/252554029_Monitoring_an_online_course_with_the_GISMO_tool_A_case_study.
Merceron, A., & Yacef, K. (2004). Mining student data captured from a web-based tutoring tool: Initial exploration and results. Journal of Interactive Learning Research, 15(4), 319–346. https://www.researchgate.net/publication/292023411_Mining_student_data_captured_from_a_web-based_tutoring_tool_Initial_exploration_and_results.
Mostow, J., Beck, J., Cuneo, A., Gouvea, E., & Heiner, C. (2005). A generic tool to browse tutor-student interactions: Time will tell!. In Proceedings of the 12th International Conference on Artificial Intelligence in Education (pp. 29–32). Amsterdam, The Netherlands. https://pdfs.semanticscholar.org/eeb8/7c154e2dca61141354ba6208210908509c8c.pdf.
Reffay, C., Teplovs, C., & Blondel, F. M. (2011). Productive re-use of CSCL data and analytic tools to provide a new perspective on group cohesion. In Proceedings of the 9th International Computer-Supported Collaborative Learning Conference: Connecting Computer-Supported Collaborative Learning to Policy and Practice (pp. 846–850). Hong Kong, China. https://halshs.archives-ouvertes.fr/edutice-00616547/document.
Santos, O. C., Gaudioso, E., & Boticario, J. G. (2003). Helping the tutor to manage a collaborative task in a web-based learning environment. In Proceedings of the AIED 2003 Workshop Towards Intelligent Learning Management Systems (pp. 72–81). Sydney, Australia. https://pdfs.semanticscholar.org/40e6/ab1079a0848a5ebb260082c09a226c7d98a6.pdf.
Seidwitz, E. (2003). What models mean. IEEE Software, 20(5), 26–32. http://www.ie.inf.uc3m.es/grupo/docencia/reglada/ASDM/Seidewitz03.pdf.
Settouti, L. S., Marty, J. C., Mille, A., & Prié, Y. (2009). A trace-based system for technology-enhanced learning systems personalisation. In Proceedings of the 9th International Conference on Advanced Learning Technologies ICALT (pp. 93–97). Riga, Latvia. https://www.researchgate.net/publication/221423776_A_Trace-Based_System_for_Technology-Enhanced_Learning_Systems_Personalisation.
Soller, A., Martinez, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15, 261–290. http://ijaied.org/pub/1016/file/1016_Soller05.pdf.
Tedesco, P. A. (2003). MArCo: building an artificial conflict mediator to support group planning interactions. International Journal of Artificial Intelligence in Education, 13, 117–155. http://ijaied.org/pub/979/file/979_paper.pdf.
Von Davier, A. A., & Halpin, P. F. (2013). Collaborative problem solving and the assessment of cognitive skills: Psychometric considerations. Research report no. 13–41. P. Educational testing service, NJ (Ed.) (p. 36). https://www.ets.org/Media/Research/pdf/RR-13-41.pdf.
Zarka, R., Champin, P. A., Cordier, A., Egyed-Zsigmond, E., Lamontagne, L., & Mille, A. (2013). TStore: A trace-base management system using finite-state transducer approach for trace transformation. In Proceeding of the 1st International Conference on Model-Driven Engineering and Software Development (pp. 117–122). Barcelona, Spain. http://liris.cnrs.fr/Documents/Liris-5880.pdf.
Zhang, H., & Almeroth, K. (2010). Moodog: Tracking student activity in online course management systems. Journal of Interactive Learning Research, 21(3), 407–429. https://www.learntechlib.org/p/32307.
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Djouad, T., Mille, A. Observing and Understanding an On-Line Learning Activity: A Model-Based Approach for Activity Indicator Engineering. Tech Know Learn 23, 41–64 (2018). https://doi.org/10.1007/s10758-017-9337-9
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DOI: https://doi.org/10.1007/s10758-017-9337-9