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In this study we present step by step the design and implementation of affective tutoring systems inside a learning social network using soft computing technologies. We have designed a new architecture for an entire system that includes a new social network with an educational approach, and a set of intelligent tutoring systems for mathematics learning which analyze and evaluate cognitive and affective aspects of the learners. Moreover, our intelligent tutoring systems were developed based on different theories, concepts and technologies such as Knowledge Space Theory for the domain module, an overlay model for the student module, ACT-R Theory of Cognition and fuzzy logic for the tutoring module, Kohonen neural networks for emotion recognition and decision theory to help students achieve positive affective states. We present preliminary results with one group of students using the software system.



1.1       Background of the study

Emotions are human feelings associated with mood, temperament, personality, disposition, and motivation and also with hormones such as dopamine, noradrenaline, and serotonin. Motivations direct and energize behavior, while emotions provide the affective component to motivation, positive or negative. Emotions are prominent elements always present in the mind of human beings (Picard, 2015). Expressing emotions during oral communication is a way to complement information about the speaker. This information complements the information contained in the explicit exchange of linguistic messages. Paul Ekman leaded studies on the effect of emotions in the speech and their relation to facial expressions. More recently computer scientists have got involved in the problem of automatic emotion recognition and have classified emotions using pattern recognition techniques. Knowing the emotional state of individuals offers relevant feedback information about the psychological state of a speaker in order to take important decisions on how a system’s user Automatic should emotions be recognition attended can improve the performance, usability and in general, the quality of human-computer interaction systems, students learning productivity, client attention systems and other kinds of applications (Ekman, 2009).

In recent years, intelligent tutoring systems (ITSs) have incorporated the ability to recognize the student's affective state, in addition to traditional cognitive state recognition. These ITSs have special devices or sensors to measure or monitor actions facial, skin conductance, speech features, etc. and as a result recognize the emotional or affective student (Arroyo, Woolf, Cooper, Burleson, Muldner and Christopherson, 2009). However, the area of affective computing applied to the STI is still developing this being due to the fact that ITSs are intended to measure cognitive rather than affective states (Carbonell, 2010). Research on affective computing includes detecting and responding to affect. Affect detection systems identify frustration, interest, boredom, and other emotions (Conati and McLaren, 2014). On the other hand, affect response systems transform negative emotional states (frustration, boredom, fear, etc.) to positive ones (D’Mello, Picard and Graesser, 2017). In this study we present the architecture and the implementation of an affective and intelligent tutoring system embedded in a learning social network. Both elements, the ITS and the social network, is going to be used to improve poor Math results in the ENLACE test (National Assessment of Academic Achievement in Schools). ENLACE is the standardized evaluation of the National Educational System, applied to students in Grades 1-9 in public and private schools. The results of ENLACE applied in early 2011 to 14 million children from third to ninth elementary level, reveals that more than nine million students have an "insufficient" and "elemental" level in learning mathematics (

Intelligent tutoring systems (ITSs) are computer programs that are designed to incorporate techniques from the AI community in order to provide tutors which know what they teach, who they teach and how to teach it. AI attempts to produce in a computer behaviour which, if performed by a human, would be described as 'intelligent': ITSs may similarly be thought of as attempts to produce in a computer behaviour which, if performed by a human, would be described as 'goad teaching' (Elsom-Cook, 1987). The design and development of such tutors lie at the intersection of computer science, cognitive psychology and educational research; this intersecting area is normally referred to as cognitive science. For historical reasons, much of the research in the domain of educational software involving AI has been conducted in the name of 'ICAI', an acronym for 'Intelligent Computer- Aided Instruction'. This phrase, in turn, evolved out of the name 'Computer-Aided Instruction' (CAI) often referring to the use of computers in education. Nevertheless, to all intents and purposes, ITSs and ICAI are synonymous. However, though some researchers still prefer 'ICAI' (e.g. Self, 1988a, uses it in the title of his recent book), it is now often replaced by the acronym 'ITS' (Sleeman& Brown, 1982b).

The latter, which is also the author's personal preference, is certainly gaining support, as confirmed by the international conference on Intelligent Tutoring Systems held in Montreal, Canada, as recently as June 2013. This preference is motivated by the claim that, in many ways, the significance of the shift in research methodology goes beyond the adding of an T to CAI (Wenger, 1987). However, some researchers are understandably hesitant to use the term 'intelligent', instead opting for labels such as 'Knowledge-Based Tutoring System' (KBTS) or 'Adaptive Tutoring System' (ATS). Wenger (2008) prefers the label Knowledge Communication Systems. Nevertheless, most researchers appear to be reasonably content with the acronym ITS. This is fine as long as everyone involved with the area understands that the usage of the word 'intelligent' is, strictly speaking, a misnomer. This does not appear to be the case, resulting in some very ambitious goals/claims, particularly in the more theoretical parts of the literature: this also appears to be a valid criticism of the entire AI literature. The fact that ITS research spans three different disciplines has important implications. It means that there are major differences in research goals, terminology, theoretical frameworks, and emphases amongst ITS researchers. This will become apparent later in this paper. ITS research also requires a mutual understanding of the three disciplines involved, a very stressful demand given the problems of keeping abreast with even a single discipline today. However, some researchers have stood up to the challenge. As a result, a great deal has been learnt about how to design and implement ITSs. A number of impressive ITSs described in chapter two this research paper bear testimony to this fact.


1.2              Statement of the Problem

This research work is carried out to design and develop an Intelligent Tutoring System (ITS); the proposed system will be aimed at reducing the level of student’s dependency on their teachers.

1.3       Objectives of the study

The objectives of the study are to design and implement an Intelligent Tutoring System (ITS) that will be able to perform the following functions:

  1. The tendency to explain instructional material to oneself in terms of the underlying domain knowledge (self-explanation)
  2. To allow students interact by asking questions.
  3. To provide possible answers to the questions from students
  4. Test the students.
  5. Analyse their performance.

1.4       Significance of the study

The significant of these study includes the following:

  1. It makes learning easier and more interesting to student in the sense that one can learn from the comfort of his or her home.
  2. Its improve the knowledge of  lecturer  strategies ( i.e. how to teach, in what order, typical mistakes and remediation,)
  3. It improve the standard of learning for student.

1.5       Scope of the Study

This study takes overview on intelligent tutoring system. Due to time factor, we will be focusing on networking since we have so many areas of study. Implementing all the fields of study will take several months before it is successfully implemented, that is the major reason why we are limiting our research  to only one field of study which is computer networking as student learns painlessly, successfully and without instruction from their teachers/lecturers.

1.6       Limitation of the study

Intelligent tutoring systems are expensive both to develop and implement. The research phase paves the way for the development of systems that are commercially viable. However, the research phase is often expensive; it requires the cooperation and input of subject matter experts, the cooperation and support of individuals across both organizations and organizational levels. Another limitation in the development phase is the conceptualization and the development of software within both budget and time constraints. There are also factors that limit the incorporation of intelligent tutors into the real world, including the long timeframe required for development and the high cost of the creation of the system components. A high portion of that cost is a result of content component building. For instance, surveys revealed that encoding an hour of online instruction time took 300 hours of development time for touring content. Similarly, building the Cognitive Tutor took a ratio of development time to instruction time of at least 200:1 hours. The high cost of development often eclipses replicating the efforts for real world application. Intelligent tutoring systems are not, in general, commercially feasible for real-world applications.


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