COMPARATIVE ANALYSIS OF INTELLIGENT TUTORING SYSTEM APPROACHES

Journal for Research | Volume 03| Issue 10 | December 2017 ISSN: 2395-7549

Comparative Analysis of Intelligent Tutoring System Approaches Pankaj Yadav Department of Computer Science and Engineering Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, India

Harkiran Kaur Department of Computer Science and Engineering Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, India

Abstract With the huge advancement in technology there is a need of advancement in education. Lots of work have been done in education system to enhance the learning ability of students, Intelligent Tutoring System (ITS) is one of the outcome of those advancements. It provides a platform which reduce the dependency on human tutor as it focuses on providing smart (intelligent) system to students for learning. ITS is a system which is implemented on computers that uses Artificial Intelligence(AI) techniques for advanced learning experience where human tutor is replaced by a virtual tutor. It presents education and other study related information in a flexible and personalized way. An ITS mainly consists of three models Domain Model, Student Model, Tutor Model.

Keywords: Affective Computing, Comparative Study, e-Learning, Intelligent Tutoring System (ITS), Models of Tutoring System _______________________________________________________________________________________________________ I.

INTRODUCTION

An Intelligent Tutoring System (ITS) is an advanced computing system that produces customized and prompt instructions to learners without requiring intervention of human teacher. Intelligent Tutoring Systems have a common purpose of providing a learning experience in a meaningful and effective manner by using a number of computing technologies. The main focus of Intelligent Tutoring System is on one-to-one learning and on training based on the individual preferences. With the help of ITS a high quality of education can be provided.

Traditional ITS model consisted of i. The Domain Model ii. The Student Model iii. The Tutoring Model [1]. The Domain Model includes the information/knowledge of the system i.e. how the tutor is being set up and how the student sees the instructions. The domain consists of the properties of domain and the tasks to be trained. The Student Model is responsible for the assessment of student’s knowledge and learning. It gives the information about student’s ability and expertise based on the tests performed. The test result shows the improvement area and the area to work on. The Tutoring Model uses different algorithms and approaches based on the analysis for the learning of the student.

II. LITERATURE SURVEY Ramón Zatarain-Cabada, M. L. Barrón-Estrada et al. in [2] proposed ITS which used visual affect and learning style recognition software systems. The First system implemented basic emotions in student’s expressions using Paul Ekman’s method and the second recognizes the learning style based on the Felder-Silverman model. Both the systems are put together to form the ITS. Minami Otsuki, Masaki Samejima in [3] proposed a case based ITS for e-Learning on project management. For teaching the Project Manager (PM) typical situations encountered during a project for its successful completion. Cases are built from past experiences such that PM gets to know the real time problems and how to deal with it.

David Arnau, Miguel Arevalillo-Herraez et al. in [4] proposed the paper for Arithmetic Problem Solving that works under the supervision of a virtual tutor. This is based on the analysis of the thoughtful processes that takes place during problem solving and tasks performed by a human while solving arithmetic problem under the supervision of one-to-one tutoring situation. ITS analyses the student’s approach for solving problems and provides the solution based on previous response by user and the problem type.

Patil Deepti Reddy and Dr. Sasikumar M. in [5] proposed paper that emphasized more on the student model, as the author believed that knowledge state of the student help in generating lessons, problems, feedback and guidance in a personalised way.

This paper used ITS for the learning of natural language i.e. Telugu. Confidence level of a learner is calculated to find out how much comfortable learner is in learning and answering questions based on this confidence level ITS works to improve the weakness of the learner.

Prema Nedungadi and M.S.Remya in [6] proposed a ITS model PC-BKT (Personalized and Clustered Bayesian Knowledge Tracing) which is the enhancement of BKT-PPC model. BKT-PPC model had few drawbacks as it was based on prior learning.

Prior Learning was common for every student irrespective of their capabilities. PC-BKT is the improved method with improved

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Comparative Analysis of Intelligent Tutoring System Approaches (J4R/ Volume 03 / Issue 10 / 002)

accuracy. In the proposed model students with similar skills are clustered and then personalised learning is provided. The Authors proposed an Algo for Personalised BKT Model.

Ika Widiastuti and Nurul Zainal Fanani in [7] proposed ITS which is based on adjustment of difficulty level of question in order to provide an adaptive system. The adjustments are done automatically by using fuzzy logic and item response theory. The architecture (Figure 1) includes Expert Model, Pedagogical Model, Student Model, and Communication Model and the adjustment levels are set by varying Question Level and adjusting Learner Level.

Fig. 1: Structure of Adaptive Learning [7]

Tenzin Doleck, Ram B. Basnet et al. in [8] proposed a ITS model used for training novice physicians in diagnosing diseases on virtual patient using Hidden Markov Models. Previous researches in Clinical Reasoning was focused on whether disease is diagnosed correctly or not however in this paper author focused more on the actions and sequential steps that leads learner to deduce the disease.

Danial Hooshyar, Rodina Binti Ahmad et al. in [9] proposed a flowchart based computer programming tutoring for the new learners of programming and named the method as FITS (Flow-Chart Intelligent Tutoring System), for decision making Bayesian network technique is applied for taking decisions and to respond according to the knowledge level of learner.

Therefore, the proposed system uses the dynamic analysis for learning.

Mohamed Yassine Si Allal Zarouk, Mohamed Khaldi in [10] authors exploited the use of self-regulated learning(SRL) based on Zimmerman cyclic model to provide metacognitive based response by considering learner’s differences in skills, type of knowledge learner want and metacognitive needs.

Nguyen Thai-Nghe and Lars Schmidt-Thieme in [11] emphasized on the Student Model to track information of individual student in order to analyze student’s performance, such that system can provide early feedbacks to improve the learning results of the student. The analysis done by ITS takes into consideration the time spent by learner for a particular problem, hints requested, correct and incorrect answers. Authors proposed multi-relational factorization approach for student modelling.

Danial Hooshyar, Rodina Binti Ahmad et al. in [12] proposed a game based learning by using concepts of ITS to teach introductory programming. A Game-based Intelligent Tutoring System (GITS) is developed in the form of competitive board games. The board game uses the Snake and Ladder game to improve web based programming solving techniques, Bayesian network is also used for the purpose of decision making.

Maria Sette, Lixin Tao et al. in [13] proposed that cyber learning have a drawback of personal and assessment driven learning which leads to confused student. With huge and diverse learning materials and lack of focusing on specific concept rather than general concept. The industry standard is to use the Wen Ontology Language(OWL) for representing the knowledge but It had drawback as it used “first-class” relation, “is-a”, between the concepts and different knowledge areas need customized relations among the concepts such as “part-of” and time dependency. Therefore, research provides an extension of OWL to Knowledge Graph (KG) to represent knowledge model with custom relations.

Yu Yan, Kohei Hara, Takenobu Kazuma et al. in [14] proposed a technique SKP-LS (Syntax Knowledge Point based Learning Status ) in order to overcome the demerits of existing computer systems like the inefficiency of computer to define learner’s individual learning status as the as the information used is not detailed, the student model cannot be handled by automated computer and to tackle all these problems authors proposed SKP-LS as SKP includes individual student’s learning status .

Sanae Zrigui, Abdelaziz Ait Moussa in [15] proposed a method to provide the student’s specific needs by automatically providing compatible courses and giving analysis based on the past and current student’s profile. Authors proposed an adoptive method to change the interface model such that tutor can update donnish knowledge and related pedagogical knowledge as per the learner.

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Comparative Analysis of Intelligent Tutoring System Approaches (J4R/ Volume 03 / Issue 10 / 002)

III. COMPARATIVE ANALYSIS OF INTELLIGENT TUTORING SYSTEM Table - 1 ITS Techniques with Different Parameters

Techniques Parameters

PC-BKT

CBL

Fuzzy Logic

Application

Prediction of student’s performance on the basis of former skill and learning ability of student

Educating Project Managers based on real time scenarios/cases

Adjustment of student level and question difficulty

Target Audience

Clustered student having varying learning ability

Project Manager

Students with different levels of skills

Data Set

Knowledge Components in the form of steps taken by student High High

Case description of previous project in the form of problems faced in the previous project and answer set High High

Student’s level(high, medium, low) based on Pretests Medium Medium

Yes

Yes

No

Algorithms/Methodologies to execute techniques

Capability Matrix K-Means Clustering Algorithm

Jaccard Index

Limitation/Challenges

Sparse Data Matrix

Accuracy Performance Prior Knowledge required for system

Hidden Markov Model To help trainee physicians learn diagnose correctly on virtual patients

Flowchart based ITS

To enhance programming skills of novice programmers

Novice Physicians

Programmer having no prior knowledge of programming

Learner’s action during diagnosis

Class lecture notes and their solutions

High High

Medium Medium

Yes

No Bayesian Network

Pre-test is always required IV. CONCLUSION

Comparative Analysis of ITS is mainly based upon the various techniques used to implement it. Each technique has some advancement, limitation, application, data set in comparison to other techniques. Techniques like Fuzzy Logic which is the most basic technique for the implementation of intelligent systems is used to implement ITS where it is applied to adjust student’s level by taking pre-tests and accordingly set the difficulty level of question. Similarly, Bayesian Network is used in several ITS for improved predictions. Several other techniques have also been developed for the implementation of ITS and on the basis of different parameters discussed in Table 1 many applications have been developed.

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H. L. Burns, J. W. Parlett and J. B. Bushman, "Intelligent Tutoring Systems- Perspective," 1989.

R. Zatarain-Cabada, M. L. Barrón-Estrada, J. O. Camacho and C. A. Reyes-García, "Integrating Learning Styles and Affect with an Intelligent Tutoring System," Proceedings - 2013 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, pp. 247-253, 2013.

M. Otsuki and M. Samejima, "An intelligent tutoring system for case-based e-learning on project management," Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, pp. 3471-3476, 2013.

D. Arnau, M. Arevalillo-Herraez and J. A. Gonzalez-Calero, "Emulating Human Supervision in an Intelligent Tutoring System for Arithmetical Problem Solving," IEEE Transactions on Learning Technologies, vol. 7, no. 2, pp. 155-164, 2014.

P. D. Reddy and M. Sasikumar, "Student Model for an Intelligent Language Tutoring System," Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, pp. 441-443, 2014.

P. Nedungadi and M.S.Remya, "Predicting Students’ Performance on Intelligent Tutoring System - Personalized Clustered BKT," Proceedings - Frontiers in Education Conference, FIE, Vols. 2015-Febru, no. February, 2015.

I. Widiastuti and N. Z. Fanani, "Adjustment Levels for Intelligent Tutoring System using Modified Items Response Theory," 2014 1st International Conference on Information Technology, Computer, and Electrical Engineering: Green Technology and Its Applications for a Better Future, ICITACEE 2014 - Proceedings, pp. 217-221, 2014.

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