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nodes and edges that don’t match the student’s individual situation or request (such as learning needs, learning
preferences, career goals, the previous semester’s GPA, and so on).
In the case of automatic curriculum suggestions based on this storyboard representation, the top predicate of the
inference looks as follows:
compose curriculum (Student) :- /* suggests an individual sub-storyboard for a student */
collect information (Student, database, StudentsInfoList), /* collects information via a web interface */
collect information (Student, EDP, EDPInfoList ), /* collects information from an EDP system */
process (StudentsInfoList, EDPInfoList ), /* derives an individual sub-storyboard */
present result (Student ). /* presents the result of the composition */
“Student” refers to a student’s unique identification, “database” refers to information that a student has manually
input via a related web interface.
The predicate “collect_information” gathers the information needed to compose the sub-storyboard. Depending on
the type of information this predicate is called twice in the above Prolog-rule. Once to receive the manually input
data from a database and once to receive the data automatically input by the EDP system.
The predicate “process” produces facts similar to those that represent the complete storyboard, but contain the
student’s identification:
includes (Student , graph , [] )
edge (Student , [] , [] , [ , ] )
These facts represent the student’s individual sub-storyboard.
The predicate “present result” calls a program, which graphically presents the derived sub-storyboard.
For example, in the case of the dynamic storyboard of the studies offered by SIE of TDU, our experiments indicate
that these sub-storyboards are basically unique sequences with only a few remaining options. Namely, there are only
a few different options left after all nodes and edges that were inappropriate, not possible, or not useful for an actual
student were automatically omitted. Having few options left is key to maintaining an overview of the maze of
opportunities. By narrowing down the options, students can manually make their individual choices in the maze of
dynamically changing opportunities and constraints. Dynamic storyboarding has such benefits.
The results so far are quite promising. Several (at least five) students of SIE in TDU joined the experiment to prove
the benefits mentioned above. The students confirmed that this approach is helpful in creating individually tailored
and optimized curricula. Indeed, due to the multilayered overview nature of dynamic storyboarding, students could
easily create their individual storyboard or curriculum. Meanwhile, teachers created, displayed, checked, and
modified general or master dynamic storyboards using Microsoft Visio (Walker & Eaton, 2004) for the storyboard
(usually for representing the syllabus of subjects) mentioned previously in the subsection “The new storyboard
concept” of the “Storyboarding” section.
Using Microsoft Visio, teachers created, checked, and modified master storyboards. Such master storyboards are
initially prepared so that students can use them for easy selection and combination (called individualization) of their
needed, permitted, and preferable subjects in their specified program of study. Here, the model is represented at the
master level of dynamic storyboards. The master dynamic storyboards are pre-generated by teachers using Microsoft
Visio as the master knowledge base, displayed by the system, and checked/modified by students. The modification
input by students make the master dynamic storyboards into individual dynamic storyboards with regulations such as
prerequisite conditions and recommendations satisfied by the support of the system’s checks, warnings, and
suggestions.
The system checks the regulations using the data, including regulations and students’ achievements such as GPA and
subjects passed. The data are sent from the EDP system using the Prolog-based program as mentioned above.
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