For the last Switch-AAA’s Info Day, I was asked to present the “New Learning Environments” (NLEs) developed these last 3-4 years within the AAA program. I was not given any definition about what characterizes these environments, but I received 6 project’s titles (out of about 50), which were categorized as « NLE ». Let us consider in turn these projects.
IT-Service Integration (ITSI) is about designing a future learning (academic) environment combining the virtual and « physical » (on-campus) components, which include studying, teaching, research and administrative aspects. The pre-study phase indicated that learners and teachers are not necessarily interested in « virtualizing » learning and teaching, but rather they wish to learn and teach using modern and integrated IT-enhanced easy-to-use environments. Moreover, learning environments should not be constricted to formal learning and teaching, or to a specific didactical scenario. Instead, an integrated IT-enhanced environment should be supportive of a variety of teaching scenarii and of Life-Long Learning (LLL). Identification of such a new environment must focus on software, hardware, infrastructure, rooms, etc., and must consider:
- Big vs. small classes (lecture and seminar rooms);
- Formal vs. non-formal teamwork;
- Public space as learning place (mobile learning, learning at home, library and learning centers);
- Examinations and assessments.
ScienceWISE is a Web-based Interactive Semantic Environment for e-Science (in particular in physics, mathematics, biophysics, environmental and computer science). Its main goal is to provide a platform for the creation of virtual organizations of scientists, working together on a dynamical generation of professional field-specific ontologies. The prototype version of the ScienceWISE system is already functioning. The first invited experts already started to fill the content of encyclopedia definitions and ontological content. The ontological information simplifies students’ orientation in unfamiliar fields, permitting an efficient adaptation into the scientific research, and bridges the well-known gap between textbooks topics and subjects of scientific papers…
It also provides a platform for scientists, working together on a dynamical generation of professional field-specific ontologies and cross-links important concepts within papers uploaded to arXiv.org. This innovative approach improves drastically the information communication for interdisciplinary research, making scientific knowledge more accessible for colleagues and students from different fields. This projects also addresses computer science techniques, related to « information extraction » (including equation search and match), « reputation and trust management », « ranking » (calculation of mutual relevance of scientific articles) and « modeling of user experience », and could become a standard part of a preprint submission procedure at arXiv.org.
Système E-Learning INductif (SELIN) is a project aiming at developing early on at university active learning, teaching of science of observation, and providing teachers and students with multiple opportunities for interaction and empirical investigation. This project is based on pedagogical and epistemological concepts that emphasize the techniques of observation and inductive methods of analysis to work in fields whose main method of scientific inquiry is observation. Use of multimedia tools, targeted exercises, and a high degree of interactivity to enhance students’ learning should help students to develop the following abilities:
- Pay attention to the obvious, which is core to a strict scientific method;
- Interpret a given situation through audio-visual content;
- Compare and contrast different scales of analysis, frameworks of analysis (individual, group, institutional) and methods of data acquisition (simple observation, interaction, interviews, data manipulation).
Lifelong Learning Transfer (LLL-Transfer) focuses on the development of guidelines for anchoring LLL in university strategy, and to work out a feasibility study for a competence profiling application. Higher education should no longer focus only on formal learning outcomes but also includes informal and non-formal learning aspects. The project reviewed existing LLL strategies and organizations and related services (Alumni, Career services, Continuing Education, etc.) in Swiss Higher Education Institutions (HEIs) and proposed guidelines and checklists for the implementation of LLL. A feasibility study for a skill-profiler was also performed. A skill-profiler should support self-reflection and awareness of knowledge management, and puts the accent on soft-skill integration. Three other objectives of the project are:
- To support competence management at individual level, permitting to capture, compare, and visualize soft skills, and to link them with job market;
- To offer an open source tool interfaced to e-Portfolios and other Personal Learning Environment (PLE) tools;
- To provide HEIs with access to soft-skill profiles so as to get a global view on competencies of students and staff.
Personal Learning Environment (PLE) and e-Porfolio are 2 projects with the objective of putting together institutional and non-institutional tools, to support formal and informal learning, and to introduce a learner-centric environment accompanied with a recommendation engine to help organize it. The PLE brings a new pedagogy, more learner-centric within a perspective of improvement of knowledge and competencies. A survey targeting students provided a prior measurement of technological complexity underlying the development of such a tool. This survey also provided the necessary data to analyze the Swiss e-Portfolio landscape and for developing an implementation plan. So far, a blog (ciel.unige.ch) focusing on educative-related technologies and soft-skills seminars for PhD students to promote cross-curricular competencies have been set up by the project team. In a second phase, the following objectives will be addressed:
- Designing an institutional PLE enabler (iPLEe) to bridge personal, institutional and worldwide resources, as well as to enable collaborations between co-learners and sharing of resources ;
- Providing students with a set of learning tools, both formal and informal, linking together institutional tools (eg. Moodle, Chamilo, Mediaserver, etc.) and non-institutional tools (eg. Youtube, twitter, Googledocs, etc.) ;
- Teaching how to use these technologies by informing users about their values and educational usages, assessing their usages so as to improve their acceptance and tailor them to users’ needs;
- Federating and recommending resources (tools and content) among institutions via a recommendation engine (RE) fed through techno-pedagogical watch activity.
What makes these learning environments “new”?
To answer this question, I structured these projects in a tree representation, shown in the accompanying figure. “Newness” in this tree is measured by how much the environment promotes deep learning, critical thinking, collaborative learning, and knowledge creation. Technologically, “newness” can also be measured on the web evolution scale, in which Web 2.0 allows teachers with new ways to engage students in a meaningful way and is thus doing more than just retrieve information as with Web 1.0, while Web 3.0 (or Semantic Web) promotes knowledge construction.
Virtual learning environments, such as learning management systems, have low ranking given they generally do not promote deep learning. Task-market, which is another AAA project (but not initially labeled “NLE”), is however a clear improvement into deep learning as it allows students to propose and share exercises to enhance learning on specific tasks. As we go up into this tree, informal and non-formal learning becomes more prominent. Higher up, the tree branches into knowledge creation (on the left) and self-reflexion (on the right). This separation might seem artificial given critical thinking is somehow related to knowledge creation. However, through this dichotomy, I wanted to clearly separate the learning environments that promote the acquisition of skill competencies, from those environments that are more research-oriented, and thus more akin to the master and doctoral levels. At a coarser level, this tree measures the degree of customized learning offered by the learning environment.
What is new today becomes common tomorrow. A better definition will thus have to be found for the NLEs. When mature enough, that is, once fully customizable and designed for life-long deep learning and knowledge creation, such environments will probably simply be called “Learning Environments”.