Le sepentelet de mer – Le retour des ITS Bienkowski et. al. 2012
Le modèle du CSCL Soller, Amy, Alejandra Martinez, Patrick Jermann, and Martin Muehlenbrock (2005). From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning
Techniques et données Types de données: social network analytics interpersonal relationships define social platforms discourse analytics language is a primary tool for knowledge negotiation and construction content analytics user-generated content is one of the defining characteristics of Web 2.0 disposition analytics intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovation context analytics mobile computing is transforming access to both people and content. Ferguson and Buckingham Shum (2012)Ferguson and Buckingham Shum (2012)'s Social Learning Analytics La procédure (roughly) Creating data for mining (optional) either automatically or manually by the users Collecting data from many sources, e.g. log files or web contents Aggregating, clustering, etc. All sorts of relational analysis Visualization of results (and raw data aggregations)
Les outils Peu ! LMS, workflow systems (LAMS) Web analytics (Google Analytics etc.) Outils data mining (difficiles !) Widgets de type experience sampling et dashboards Systèmes CSCL Bricolages wiki etc.
Conclusion dun papier EdMedia 12 Systems that structure learning activities and contents in one way or another usually include some kind of analytics. In addition, structured environments provide per definition more structured information to the participants. Asking the user is an easy strategy that can provide good information with respect to learners own perceptions of their learning, their contributions and their interactions. Student productions are key indicators for learning. Modern web technology allows inserting widgets into various online environments. Widgets can talk to other services and therefore can be used to create aggregating dashboards, e.g. for the teacher. Analytics are meant to be used by both learners and teachers. Analytics can provide various levels of assistance and insight: From simple mirroring tools, to metacognitive tools to guiding systems (Soller et al., 2005).
Conclusion dun papier EdMedia 12 1) Productions tomorrow: (1) light-weight productions/portfolio system that also includes in a simple task management system and a rubrics-based grading tool. (2) A tool like StatMediaWiki that provides visualizations for content evolution. in the future: (1) A e-Framework-like service-oriented architecture based on PLEs (2) Web API-based content and collaboration analytics for writing and discussion environments such as wikis, CMS and Forums. 2) Interactions tomorrow: Collaboration diagrams for wikis that work across individual pages, categories of pages and groups of participants. Collaboration diagrams for forums, e.g. tools that behave like SNAPP but work across topics. in the future: Collaboration diagrams that work across systems. This may require the use of some standardized digital identity like OpenId and will raise privacy issues. 3) Reflections tomorrow: Portable widgets like EnquiryBuilder with an (optional) server-side component that could be run by the teacher or his organization. in the future: Reflection tools and analytics should be integrated in e-portfolio systems and personal learning environments. Many learning institutions define institutional competence catalogues that could be linked to students reflective activities. In addition, the learner should be able to add his own goals. 4) Management and regulation tomorrow: A monitoring dashboard for the system described in point 1. in the future: (1) A LAMS-like monitoring tool that works across environments. (2) Q/A help-desk like forums that provide a state of problems addressed and solved.