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his/her followed approach, as it doesn’t match the taught concepts that were the
subject of the current training being tested.
Accordingly, this research have studied and identified the characteristics of IGS as
opposed to other active support systems. Achieved are models for testing, questions,
and grading, which led to the design of a framework for IGS systems. Finally a
prototype (Smart Grader—SG) was then implemented. SG is supposed to integrate to
ITS systems to provide more effective learning through grading student tests,
correcting mistakes, and providing, on the spot, advices on better solution strategies.
A List programming problem was used for experimentation.
In general, the results achieved by the Smart Grader research project were
promising and encouraging. However, further investigations are still required along
several directions: first, assessing SG’s practical viability through measuring the cost
and time efficiency of the whole process of creating a question bank, preparing a test,
and holding a testing session, and hence, improving the framework design; second,
adding accumulative experience and machine learning component to the framework
so as to detect new correct answers and hence enlarging the knowledgebase; third,
adding dynamic adaptation of the Grader’s behavior; finally, Implementing a more
comprehensive environment with practical considerations, e.g., using the mobile and
cooperative agent technology to give more flexibility and power.
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