Legislature Building anytime in once a week, and they have
to follow the lead from the MOOAR Client application
installed on their iPhone devices to complete all the activities
and tasks of each learning object. The outdoor activities are
organized in four sections according to the courses’ progress.
When the three learners are in the Alberta Legislature
Building and launch the MOOAR application installed on
their iPhone devices, which will automatically login to the
MOOAR system, they will see a personalized main page
adaptive to their learning profile, and display the amounts of
activities and tasks in this section. Also the AR launch button
and Logout button are shown at the end of the main page.
Once they clicked on the AR launch button, they are able to
start learning in the interactive AR learning mode.
In this research, all of the learning contents, activities and
tasks are already pre-defined in the Learning Objects Data
Model, so in the designed scenario, there are 10 different
objects located in and around the Alberta Legislature
Building. Each of them could relate to one or more different
program based learning contents. For example, object
number one could only relate to historical and political
learning contents, activities and tasks. The learning contents
data related to each object are shown in Table II.
Furthermore, each object has specific location information,
the MOOAR will instruct learner to complete each activity
and task according to their personalized learning sequence.
TABLE II. LEARNING OBJECTS WITH ADAPTIVE LEARNING
CONTENTS.
In the MOOAR system, after complete the 6DOF and
Adaptive processes, the server will create a personalized
learning sequence table as shown in Table III. According to
this table, the MOOAR system will provide personalized
adaptive AR learning contents to each learner. A camera
displaying object may represent a learning object to the three
students, for example, object 2 provides H2 (Historical
contents related to object 2) to Will, A2 (Architectural
contents for object 2) to Danny and P2 (Political contents for
object 2) to Jimmy, which means object 2 is a learning object
for all of them. On the other hand, an object could represent
a simple Scenery View to each one of them, for example,
SVA 1 to Danny and SVH 10 to Will.
TABLE III. PERSONALIZED LEARNER SEQUENCE.
Once the learner changes the camera view between Front and
Ground, the system will provide different AR contents
between learning information and navigation information.
Furthermore, this research also designed a learning
performance evaluation process in this scenario. If learners
cannot meet the goal of each learning object, the system will
replay the same learning activity or task again, else, the
system will instruct learner to the next learning object
according to the personalized sequence table.