DISTANCE LEARNING BY INTELLIGENT TUTORING SYSTEM

Part II: Student/teacher adaptivity in an Engineering Course

José Manuel Gascueña, Antonio Fernández-Caballero

Computer Science Reseach Institute of Albacete, University of Castilla-La Mancha, Albacete, Spain

Enrique Lazcorreta , Federico Botella

Operations Research Centre, University Miguel Hernandez of Elche, Elche, Spain

Keywords: Distance learning, Intelligent Tutoring System

s, E-learning, E-teaching, Education, Adaptivity.

Abstract: Intelligent Tutoring Systems (ITS) have proven their worth in multiple ways and in multiple domains in

Education. In this article the application of an Intelligent Tutoring System to an Engineering Course is

introduced. The paper also introduces an explanation of how the course adapts to the students as well as to

the teachers. User adaptation is provided by means of the so called pedagogical strategies, which among

others specify how to proceed in showing the contents of the matter for a better assimilation of the

knowledge by the student. Thus, in this paper the adaptation mechanisms implemented in the ITS, which

permit that the students learn better and the professors teach better, are explained in extensive.

1 INTRODUCTION

One of the main problems in Intelligent Tutoring

Systems (ITS) consists in adapting to the needs of

the user who interacts at each moment. A way to

provide user adaptation is by means of the so called

pedagogical strategies, which specify how to

sequence the contents, what kind of feedback has to

be given during education, when and how the tutor’s

contents (problems, definitions, examples, and so

on) have to be shown or explained (Murray, 1999).

ITS have proven their worth in multiple ways

d in multiple domains in Education (Anderson,

Corbett, Koedinger & Pelletier, 1995; Woolf, Beck,

Eliot & Stern, 2001). ITS are growing in acceptance

and popularity for several reasons, including: (i) an

increased student performance, (ii) a deepened

cognitive development, and, (iii) a reduced time for

the student to acquire skills and knowledge (Sykes

& Franek, 2003). Currently, ITS can be found in

core databases, mathematics, physics, language,

medicine, and other courses in many schools.

In the field of databases, KERMIT (Suraweera &

Mitrovic,

2002) teaches the conceptual modelling of

databases using the entity - relation data model; and

SQL-Tutor (Mitrovic, Martin & Mayo, 2002)

teaches databases SQL language. In both

approaches, a model based on restrictions has been

used. In the field of physics, Andes (Gertner &

VanLehn, 2000) allows the students to solve

problems of classic physics in an environment that

offers visualization, immediate feedback, and

procedural and conceptual help; and Why2-Atlas

system (VanLehn et al., 2002) teaches qualitative

physics by having students write paragraph-long

explanations of simple mechanical phenomena.

In the field of programming languages,

ELMART

(Weber & Brusilovsky, 2001) teaches

programming in LIPS, while JITS (Sykes & Franek,

2003) teaches JAVA, and Bits (Butz, Hua &

Maguire, 2004) teaches C++. In the field of

mathematics, PAT (Koedinger & Anderson, 1997),

Ms. Lindquist (Heffernan & Koedinger, 2002), and

Aplusix-Editor (Nicaud, Bouhineau & Huguet,

2002) help the students to learn algebra.

ITS also exist in many other newer but relevant

fields. For

example, SlideTutor (Crowley &

Medvedeva, 2003) is an ITS in dermatopathology,

and Design Pattern (Jeremic, Devedzic & Gasevic,

2004) is used to learn design patterns.

On the other hand, there has been a great

researc

h effort in learning strategies to be

incorporated into ITS (Boulay & Luckin, 2001). As

an example, Meyer has used the analogy (Meyer,

2002) to teach a less known domain from a more

familiar one. The case based reasoning paradigm has

148

Manuel Gascueña J., Fernández-Caballero A., Lazcorreta E. and Botella F. (2005).

DISTANCE LEARNING BY INTELLIGENT TUTORING SYSTEM - Part II: Student/teacher adaptivity in an Engineering Course.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 148-153

DOI: 10.5220/0002519601480153

 SciTePress

also been an inspiration to help in obtaining new

incrementing knowledge (Martens, 2004). Even,

reinforcement learning has been used (Bennane,

2002). When various strategies are together

implemented in an ITS, as for instance in (Prentzas,

Hatzilygeroudis & Garofalakis, 2002), the system

selects the most appropriate one for the activity that

the student is performing.

The structure of the paper is as follows. Section 2

shows the adaptivity mechanisms provided for the

student based on the overall e-learning capabilities

offered. Section 3 is devoted to the adaptivity to the

teacher. Finally some conclusions are provided at

the end of the article.

2 STUDENT ADAPTIVITY

2.1 E-learning Capabilities

First of all let us focus on the functionality that the

ITS offers to the student (see figure 1).

Of course, the student must register in the course

(use case “Register for the course”) as stated in

sequence diagram of figure 2. The registered

students can change their passwords (through use

case “Change password”) each time they enter the

course to begin a new study session.

Once a new study session has been started

through use case “Enter the course”, the student

reads pages of theory (”Read theory”), answers

exercises (“Solve exercise”) or test questionnaires

(“Solve test”) depending on the task that a

pedagogic module proposes through time. While

completing an exercise, the student can consult the

theory (use case “Consult theory”) closely related to

the exercise.

During a study session the student can also

change the style of presentation of the matter (that is

to say the visual preferences – “Change

preferences”). The student may also consult at any

time his state after performing any task.

2.2 Student Learning Mechanism

Figure 3 shows the steps followed by the pupil when

studying each topic of the course (“Matter learning”).

(1) Firstly, the student has to read the whole

theory for the current topic.

(2) Afterwards, the student has to solve the

exercises proposed. If the student is a level-1 (low

level) student, firstly he has to solve the basic

exercises and then the complex ones. O the other

side, if the student is a level-2 (high level) student,

he will only have to solve the complex exercises.

The basic exercises are all shown in a sequential

way, and then the ITS evaluates if the student has

reached a minimum score associated to the topic. On

the contrary, the complex exercises are shown in

blocks (composed of a pre-determined quantity of

exercises), and, after showing each block, there is an

evaluation to ask for a minimum mark before

composing the next block. After correctly fulfilling a

number of complex exercises, the system goes on to

the test questionnaires.

(3) Lastly, the student has to solve the test

questionnaire offered.

(4) If there are more topics in the course, the

system goes back to step (1). Otherwise, the student

has finished studying the matter.

Figure 1: The student’s requirements

Figure 2: Sequence diagram for use case

“Register for the course”

DISTANCE LEARNING BY INTELLIGENT TUTORING SYSTEM. Part II: Student/teacher adaptivity in an Engineering

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Figure 3: Activity diagram for “Matter learning”

During steps (2) and (3), if the student does not

obtain the minimum scores fixed for the topic, he

gets reinforcement in order to reach the objectives

for the course.

2.2.1 Reinforcement for Basic Exercises

Figure 4 shows how the alumni are reinforced

during their activity of solving basic exercises. The

system selects one of the exercises previously

proposed and not well solved from the set of basic

exercises and gets the reinforcement material (based

on previous topics studied). This way the system

helps the student to correctly solve the exercise.

After proposing the reinforcement material, and

before the student has to solve again the basic

exercise, the ITS shows the bad response that the

student gave previously. When the student passes

the minimum score, the system does not go on

providing reinforcement. But, and this is the worst

situation, if the system has provided reinforcement

to all badly answered exercises, and even so the

student has not been able to solve them, the ITS tells

the student to consult the tutor personally. After

having his meeting with the teacher, the student is

permitted to advance in the study of the course.

2.2.2 Reinforcement for Complex Exercises

The strategy for providing reinforcement to the

student in complex exercises is very similar to the

strategy followed to give reinforcement in basic

exercises. The only difference is that the ITS firstly

tries to reinforce with material of the current topic;

and, if the student is still not able to solve the

complex exercise, he is reinforced by material from

previous topics of the course.

If the student does not have seen all selected

complex exercises, he will only get reinforcement

for those exercises offered to the user in the last

block of complex exercises. But, if he already has

been offered all the complex exercises blocks, he

will be reinforced for all complex exercises

incorrectly solved and not yet reinforced previously.

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Figure 4: Activity diagram for “Reinforcement for basic

exercises”

2.2.3 Reinforcement for Tests

If the student does not get a minimum mark in the test

questionnaire proposed for the current topic, the ITS

builds a new test questionnaire, offers it to the student, and,

if the student does not perform well, the professor

personally must reinforce in order to proceed with the

learning activity – activity “Go to the teacher” - (see figure

5).

Figure 5: Activity diagram for “Reinforcement for test”

3 TEACHER ADAPTIVITY

3.1 E-teaching Capabilities

Let us also talk about what the teacher can do with

the ITS (see figure 6).

Let us firstly focus on the general requirements

of the professor. Evidently, the teacher must be

authenticated successfully to accede to the ITS

functionality (use case “Authenticate”). Once the

teacher has been authenticated, he can consult all the

didactic material (theory –“Consult theory”-,

exercises –“Consult exercises”-, and test

questionnaires –“Consult test battery questions”) of

each of the topics of the matter and obtain quiet

statistics fruit of the interaction of the students with

the system. It can give reinforcement to the students

who need his help, by means of use case “Reinforce

the student”. This is because the student has not

managed to advance in the study of the subject

because the material that has provided to him the

pedagogic module is not sufficient to overcome the

goals of the topic that he is studying. The teacher

may change the style of presentation (colours,

margins, interlineate, size and type of source) of the

interface.

But, the biggest benefit is that the teacher may

consult statistics fruit of the interaction of the

students with the system. For every topic of the

matter, the teacher also obtains the number of times

there has been a need to reinforce the students to be

able to advance in the study.

The information provided to the teacher is

gathered during interaction of the students with the

ITS. Respect to the theory read by the students, the

teacher can know the number of times that the

students have acceded to every page of theory and

the average time that they have spent in every visit.

The system also records when students have done

scroll when they visit a page of theory and it may

reproduce all movements performed. In the same

sense, and in accordance with the exercises proposed

to the students, the teacher is able to know the

average time that students have spent in performing

them, the percentage of pupils that have not been

able to perform them correctly, and how many

students have answered well or badly. The teacher

can also know the number of times that every

exercise has had to be explained again by means of

theory pages. Lastly, for the case of test

questionnaires, the teacher can know the number of

times that every test question has been shown to the

students, the percentage of tests answered well or

badly, and so on.

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Figure 6: The professor’s capabilities

3.2 Teacher Learning Mechanism

The teacher may learn how to enhance teaching of

the course from the statistics gotten from the

interaction of the students with the ITS.

The teacher will be able to know the efficiency

of the implemented mechanism to reinforce the

students. For each topic of the course, the teacher

may consult the information on the number of times

that the students have needed reinforcement and how

many times the students have personally been

reinforced by the professor. Moreover, the teacher

may consult statistics of information gathered during

theory, exercises and test phases, respectively.

The teacher may know the number of times that

the students have accessed each theory page and the

mean time the students have been on each visited

page. The statistics are classified as (a) pages read

during the theory phase, (b) pages consulted during

the exercises phase, either solicited by the student or

due to the reinforcement mechanism. The ITS also

offers the possibility to know which students have

performed scrolling when visiting theory pages and

to reproduce the scroll movements as performed.

In relation to the exercises statistics, the

professor may consult to how many students an

exercise has been shown, the mean time the students

have spent to solve the exercise and the percentage

of blank, correct and incorrect answers to the

exercise. There is a classification in exercises

presented as reinforcement and normal exercises.

The information on how many times an exercise has

been explained personally by the teacher is also

provided.

In relation to test questionnaires, the teacher may

look for the number of times that each test question

has been presented to the students and the number of

times that the students have left the test blank, have

answered correctly and have answered incorrectly. It

is possible to know if the test question was presented

as reinforcement to an exercise, or if it was part of a

test questionnaire. Furthermore, the teacher may

know the number of times that he had to personally

explain the test question.

4 CONCLUSIONS

The ITS have turned into a technology of increasing

interest to complement traditional education so

much from the perspective of the students as from

that of the teachers. In this article the application of

an ITS architecture to an engineering course has

been introduced. The aim of the ITS is that the

students can learn more and better, and on the other

hand that the teachers can extract conclusions that

help them to improve their teaching activities.

In this paper, we have introduced an explanation

of how the course adapts to the students as well as to

the teachers. User adaptation is provided by means

of the so called pedagogical strategies, which among

others specify how to proceed in showing the

contents of the matter for a better assimilation of the

knowledge by the student.

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ACKNOWLEDGEMENTS

This work is supported in part by the Spanish Junta

de Comunidades de Castilla-La Mancha PBC-03-

003 and the Spanish CICYT TIN2004-08000-C03-

01 grants.

REFERENCES

Anderson, J.R., Corbett, A.T , Koedinger, K.R., Pelletier,

R., 1995. Cognitive tutors: Lessons learned. The

Journal of the Learning Sciences, 4: 167-207.

Bennane, A., 2002. An approach of reinforcement learning

use in tutoring systems. International Conference on

Intelligent Tutoring System, ITS 2002. Lecture Notes

in Computer Science, 2363: 993.

Boulay, B., Luckin, R., 2001. Modelling human teaching

tactics and strategies for tutoring systems.

International Journal of Artificial Intelligence in

Education, 12:235-256.

Butz, C.J., Hua, S., Maguire, R.B., 2004. Bits: A Bayesian

Intelligent Tutoring System for computer

programming. Western Canadian Conference on

Computing Education.

Crowley, R.S., and Medvedeva, O., 2003. A general

architecture for intelligent tutoring of diagnostic

classification problem solving. Proceedings of the

Annual AMIA Syposium, pages 185-189.

Gertner, A., VanLehn, K. 2000. Andes: A coached

problem solving environment for physics. Intelligent

Turing Systems: 5th International Conference, ITS

2000, Lecture Notes in Computer Science, 1839:133-

142.

Heffernan, N.T., Koedinger, K.R., 2002. An Intelligent

Tutoring System incorporating a model of an

experienced human tutor. International Conference on

Intelligent Tutoring System, ITS 2002. Lecture Notes

in Computer Science, 2363:596-608.

Jeremic, Z., Devedzic, V., Gasevic, D., 2004. An

Intelligent Tutoring System for learning design

patterns. International Workshop on Adaptative

Hypermedia and Collaborative Web-based Systems,

AHCW’ 04.

Koedinger, K.R., Anderson, J.R., 1997. Intelligent tutoring

goes to school in the big city. International Journal of

Artificial Intelligence in Education, 8:30-43.

Martens, A., 2004. Case-based training with Intelligent

Tutoring Systems. Proceedings of the The 4th IEEE

International Conference on Advanced Learning

Technologies.

Meyer, C., 2002. Hypermedia environment for learning

concepts based on inter-domain analogies as an

educational strategy. International Conference on

Intelligent Tutoring System, ITS 2002. Lecture Notes

in Computer Science, 2363:281-290.

Millán, E., Agosta, J.M, Pérez J.L., 1999. Application of

bayesian networks to student modelling. Proceedings

of PEG'99: Intelligent Computer and Communications

Technology: Teaching & Learning for the 21st

Century.

Mitrovic, A., Martin, B., Mayo, M., 2002. Using

evaluation to shape ITS design: Results and

experiences with SQL-Tutor. International Journal of

User Modeling and User-Adapted Interaction, 12(2-

3):243–279.

Murray, T., 1999. Authoring Intelligent Tutoring Systems:

An analysis of the state of the art. International Journal

of Artificial Intelligence in Education, 10:98-129.

Nicaud, J.F, Bouhineau, D., Huguet, T., 2002. The

Aplusix-Editor: A new kind of software for the

learning of algebra. Intelligent Tutoring Systems, ITS

2002. Lectures Notes in Computer Science, 2363:178-

187.

Prentzas, J., Hatzilygeroudis, I., Garofalakis J., 2002.

Web-based Intelligent Tutoring Systems using hybrid

rules as its representational basis. Intelligent Tutoring

Systems, ITS 2002. Lectures Notes in Computer

Science, 2363:119-128.

Suraweera, P., Mitrovic, A., 2002. KERMIT: A constraint-

based tutor for database modeling. Intelligent Tutoring

Systems, ITS 2002. Lectures Notes in Computer

Science, 2363:337-387.

Sykes, E.R., and Franek, F., 2003. A prototype for an

Intelligent Tutoring System for students learning to

program in Java. Proceedings of IASTED

International Conference on Computers and Advanced

Technology in Education, CATE2003, pages. 78-83 .

VanLehn, K., Jordan P.W., Rosé, C.P., Bhembe, D.,

Böttner, M., Gaydos, A., Makatchev, M.,

Pappuswamy, U., Ringenberg, M, Roque, A., Siler, S.,

Srivastava, R., 2002. The architecture of Why2-Atlas:

A coach for qualitative physics essay writing.

Intelligent Tutoring Systems, ITS 2002. Lectures

Notes in Computer Science, 2363:158-167.

Weber, G., Brusilovsky, P., 2001. ELM-ART: An adaptive

versatile system for Web-based instruction.

International Journal of Artificial Intelligence in

Education, 12:351-384

Woolf, B.P., Beck, J., Eliot, C., Stern, M., 2001. Growth

and maturity of intelligent tutoring systems: A status

report. In Smart Machines in Education, AAAI/MIT

Press, pages 99-144.

DISTANCE LEARNING BY INTELLIGENT TUTORING SYSTEM. Part II: Student/teacher adaptivity in an Engineering

Course

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