Information Technology Reference
In-Depth Information
The two phases are the knowledge background and the testing phase
(Phase I) and the knowledge and the mobile support phase (Phase II). Within
the two phases, at first, learners have attained background knowledge as
step 1 shows. They might have the knowledge via three ways: traditional
classroom learning in the past; experiential learning in their daily life; and
informal learning via Internet, newspapers, television, and books. Secondly,
learners participate the testing as step 2 shows. The testing could be paper-
based, web-based, or even mobile-based.
Thirdly, the learning path planner compares the concept maps and con-
structs the learning path for each individual as step 3 shows. Finally, we
use positioning technologies and location-based services to get the learner's
position as step 4 shows, and use the guidance message generator to give the
learner either a moving guidance message such as “Please go to Area D in
room 203, and find out the artifact No. 23!” or a learning guidance message
such as “Please observe what special mark the vase has.”
In general, the learning flow in the real world is: learners first have
knowledge backgrounds and look at something they are interested in;
then they will move to the next learning spot where they complete the
observations; and repeat above steps as needed. Figure 16.7 shows how the
location-based guidance message generator joins the ubiquitous learning
framework.
First, domain experts construct the situated map according to the learn-
ing environment (e.g., a zoo; the step 1 in Figure 16.7). Step 2, the generator
transforms the situated map into a tree-form situated map for generating
guidance messages in the future. Step 3, the generator picks those learning
objects from the situated map. Step 4, learners choose the objects they want
to learn or they need to learn. Finally, the generator constructs the guidance
messages to keep learners moving and studying in the real world (step 5 in
Figure 16.7).
16.6 Conclusions
This paper reveals a MAA and location-based ubiquitous learning frame-
work. The framework uses multi-agent architecture as the platform and
designs different agents with different abilities. The agents can communicate
and collaborate with each other via agent communication language.
The framework applies the positioning technologies to create several agents
for ubiquitous learning, including dynamic grouping, learning path planner,
and guidance message generator. The framework is still going on, which
means there are more agents that might come out in the future and the cur-
rent designs of the agents might also be improved in the future.
