Published In : IJCSN Journal Volume 5, Issue 5

Date of Publication : October 2016

Pages : 746-754

Figures :03

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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