Intelligent Tutoring Systems (ITSs) are special
classes of E-learning systems designed using Artificial
Intelligence (AI) approaches to provide adaptive and
personalized tutoring based on the individuality of students.
The student model is an important component of an ITS that
provides the base for this personalization. During the course
of interaction between student and the ITS, the system
observe student’s actions and other behavioral properties,
create a quantitative representation of these student’s
attributes called a student model.
Published In:IJCSN Journal Volume 5, Issue 5
Date of Publication : October 2016
Pages : 746-754
Tables : 02
Salisu Muhammad Sani : Department of Computer Science, Faculty of Computer Science and Information Technology,
University Putra Malaysia, 43400, Serdang, Selangor Malaysia.
Abdullahi Baffa Bichi : Department of Computer Science, Faculty of Computer Science and Information Technology,
Bayero University Kano, Nigeria.
Shehu Ayuba : Department of Computer Science, Faculty of Mathematical and Computer Science,
Kano University Science and Technology, Wudil, Kano State, Nigeria.
Artificial Intelligent Techniques, Intelligent
Tutoring Systems, Student Modeling, E-learning Systems
The results of the findings for the student modeling
approaches and the various existing works are presented in
tables 2.1 and 2.2 respectively. To be more specific, table
2.1 presents the student modeling approaches that have
been used in a variety of adaptive and/or personalized
tutoring systems. Table 2.2 presents a number of existing
student models, the approaches that have been used in
their modeling as well as the numerous limitations that
characterized each model. From the result in table 2.1, it
can be observed that the most common used student
modeling techniques within the period of the review are
the stereotype, Fuzzy logic and Bayesian approaches. The
review of various student modeling approaches and the
existing student models focuses mainly within a five year
period (2010–2015) in order to arrive at getting the more
recent trends in these directions. The year 2010 recorded
the highest number of research works within the period
under consideration. In addition, it can also be seen that
many researchers have used a hybrid student model, which
brings together various features of different techniques of
student modeling, in order to combine various aspects of
student’s characteristics. For instance, there are hybrid
student models that combine overlay with stereotype
modeling techniques, or stereotypes with machine learning
techniques, or an overlay student model with Bayesian
networks techniques, or Bayesian networks with machine
learning algorithms. The above combinations of student
modeling techniques are just some examples.
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