The trend of research in educational technology, during the last decade, has been to focus on learners and learning. The evolution of the ideas could be sketched in the following way : the initial paradigm was to design Intelligent Tutoring Systems (ITS) as autonomous machines with strong instructional functionalities and some sort of modelling of learners' needs and cognitive characteristics, a second paradigm has been the development of learning environments (eg microworlds) opening to the learner a real space for the exploration and the construction of knowledge. The former has not led to clear success, the later has evidenced serious difficulties (well documented by the Logo literature) and the need to complement the environment by teachers input and guidance. The lesson then, is that if teaching reduced to instruction is not the more successful avenue, the absence of teaching features in a learning environment does not guaranty either the quality of the learning output.
What are the lessons ? Clearly the need to search for a new paradigm which could ensure a better equilibrium between learning and teaching, between human and machines.
The common interest of Europe and the US, either in general education or professional training (lifelong learning), to overcome educational difficulties especially in science, mathematics and language learning, together with their common recognition of the potentialities of educational technology, should lead to a fruitful synergy in this area.
Teaching, an emergent property of learning environments - IST 2000
1. Teaching,
an emergent property of
eLearning environments
Nicolas.Balacheff@imag.fr
CNRS, France
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
1
2. Looking ahead to what we did
• learner centered design
– but… the needs of the to-be-learned objects, which are
under learnability and teachability constraints, is forgotten
• design richer environments
– but... the richer the environment the more complex the
learning experience, the more guidance is needed
• domain independant design
– but... any knowledge domain as its own epistemological
characteristics
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
2
3. Knowing as a capacity of facing situations, carrying
tasks, solving problems under certain constraints...
M
action
S
M
subject
feedback
milieu
constraints
Optimal learning situations to be
organised, under epistemological
and time constraints
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
Control of the distance
between the learning system
and the refered "reality"
3
4. Complexity 1
learning systems [S⇔M] are content specific
• reading and writing
• foreign languages
• chemistry
• statistics
• surgery
• music writing
• mathematics
algebra
geometry
M
social
material
symbolic
...
Being specific
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
4
5. Complexity 2
the acceleration of the knowledge life cycle
Learners
needs
Society
needs
Content to be
learned
abilities
flexible
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
5
6. Complexity 3
Individuals are more than learning systems
at any time, a relationship should be possible between the
learner and a knowledgeable other.
Teachers and trainers are users of systems
as well as learners, hence they may need
as much support in order to cooperate
efficiently with artificial agents
hybrid
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
6
7. Complexity 3
Individuals are more than learning systems
at any time, a relationship should be possible between the
learner and a knowledgeable other.
Teachers and trainers are users of systems
as well as learners, hence they may need
as much support in order to cooperate
efficiently with artificial agents
Quic kT ime™ et un déc ompresseur
Cinepak sont requis pour visualiser
c ette image.
hybrid
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
7
8. Look back to eLearning future
Human-system partnership
Two types of users : learners and knowledge holders
teachers, trainers
any expert...
Emergence
Educating is not the function of one well defined entity, but
the product of a web of interactions
Ownership of the learning space
Life long learners will be creators of their learning space, of its
content, of its organization, as well as of its presentation
Laboratoire Leibniz - IMAG / CNRS-UJF-INPG
8
Editor's Notes
The trend of research in educational technology, during the last decade, has been to focus on learners and learning. The evolution of the ideas could be sketched in the following way : the initial paradigm was to design Intelligent Tutoring Systems (ITS) as autonomous machines with strong instructional functionalities and some sort of modelling of learners' needs and cognitive characteristics, a second paradigm has been the development of learning environments (eg microworlds) opening to the learner a real space for the exploration and the construction of knowledge. The former has not led to clear success, the later has evidenced serious difficulties (well documented by the Logo literature) and the need to complement the environment by teachers input and guidance. The lesson then, is that if teaching reduced to instruction is not the more successful avenue, the absence of teaching features in a learning environment does not guaranty either the quality of the learning output.
What are the lessons ? Clearly the need to search for a new paradigm which could ensure a better equilibrium between learning and teaching, between human and machines.
The common interest of Europe and the US, either in general education or professional training (lifelong learning), to overcome educational difficulties especially in science, mathematics and language learning, together with their common recognition of the potentialities of educational technology, should lead to a fruitful synergy in this area.
The reasons why the learner, either a child or an adult, needs "teaching inputs" are very often hidden as a result of the strength of the emphasis on the constructivist principles of design of learning environments. These needs are especially important with modern environments which are largely distributed and provide a potential access to a huge range of knowledge and information. These reasons could be sketched by the following questions which acknowledge that the learner has in general a low level of control on the events which are on the edge of the learning process in which he or she is involved—unlike the expert problem-solver:
"How to look for something you don't know? "
"How to know that you have found what you looked for? "
"How to know that you have learned?"
A last question raises a crucial question related to the fact that in many cases learning is related to a willing to get the adequate qualification with respect to a given competence or activity. Indeed, the issue of certification must be considered together with the design of a learning environment, since…
"How will others know that you know?"
These issues, which call for the involvement of teaching (agents) in the learning environment, are even more essential in the case of complex knowledge (as opposed to basic skills).