October 2012 Volume 15 Number 4 - Educational Technology ...

practice” for practicability, and “to be affective” for affectivability. We will explain the details in the following
sections.
Model of Teachability Reasoning
In the pursuit of novelty, an agent is motivated to seek solutions to achieve its goal. In the context of TA, this pursuit
aligns with the teachability requirement. A MTA achieves teachability through learning knowledge from students.
The learning process is triggered when the agent detects that a student is approaching and the agent has not learnt
valid knowledge from him. The GoalNet for modeling teachability is shown in Figure 5. The goal is “to learn from
user”. To achieve this, the agent initiates the conversation by requiring teaching from the student. He may reject the
agent’s request, and this simply ends the GoalNet execution and will trigger an event which is used in affectivability
reasoning. Once the user agrees, a concept map is shown to the student. The concept map is utilized as an interface
tool for the agent to get structured input from the student. The agent tracks the changes of the concepts and relations
on the concept map updated by the student. An error checking mechanism is used to alert the student, if any syntactic
error is detected. Otherwise, the agent analyzes the received input and saves to its knowledge base. The knowledge
representation in concept maps is application-dependent and requires to be defined by software designers. A use case
will be given in the experiment for more details.
Model of Practicability Reasoning
Figure 5. Model of Teachability
Figure 6. Model of Practicability
In the pursuit of performance, a motivated agent tries its best to behave well and achieve good feedback during its
life time. From the point of view of a tutoring system, TA’s performance goal should be consistent with its learning
objectives. For example, TAs try to answer questions correctly, have high score in the assessment, or deduce
appropriate actions upon a situation. The performance goal is content-dependent, and is specified by software
designers. Reasoning is triggered when a TA receives the knowledge, and it intends to be evaluated. The GoalNet for
modeling practicability is shown in Figure 6. The goal is “to practice” by reasoning over the learnt knowledge. To
achieve this, the agent starts by perceiving inquiries from the system. Questions are one type of the inquiry, if the
agent is evaluated in an ask-and-answer manner. A more interactive inquiry may be asking TAs to perform a certain
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