50120140506012 2-3

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 99-110 © IAEME
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 6, June (2014), pp. 99-110
© IAEME:

Journal Impact Factor (2014): 8.5328 (Calculated by GISI)

IJCET
© I A E M E
IMPROVED INTEROPERABLE INTELLIGENT TUTORING SYSTEM
USING SCORM COURSE
Prof. Vina M. Lomte
Assistant Professor, RMD Sinhgad School of Engineering, Warje, Pune-52
Ms. Vinita R. Kawalkar
M.E. Student, RMD Sinhgad School of Engineering, Warje, Pune-52
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I. ABSTRACT
Because of ability problems, intelligent tutoring systems area unit troublesome to deploy in
current instructional platforms while not extra work. This limitation is critical as a result of tutoring
systems need wide time and resources for his or her implementation. Additionally, as a result of
these tutors have a high instructional worth, it's fascinating that they may be shared, utilized by
several stakeholders, and simply loaded onto totally different platforms. This paper describes a
replacement approach to implementing ASCII text file and practical intelligent tutors through
standardization. In distinction to alternative ways, our technique doesn't need exploitation non
standardized peripheral systems or databases, which might limit the ability of learning objects. Thus,
our approach has the advantage of yielding tutors that area unit totally conformant to e-learning
standards which area unit freed from external resource dependencies. In step with our technique,
“automatic” tutoring systems area unit sorted. Additionally, given the ability of our technique, tutors
can even be combined to make courses that have distinct granularities, topics, and target students. In
addition we are combining this SCORM LOs with our LMS which gives all the e-learning facilities
to learner (students). Our proof of construct improved Interoperable Intelligent Tutoring Systems
Using SCORM Standards.
Index Terms: Computers and Education, Computer-Assisted Instruction, Computer-Managed
Instruction, Distances Learning.
II. INTRODUCTION
Adaptive and personalized educational systems can provide very high quality educational
assistance. For instance, ITS are adaptive educational tools that offer direct personalized instruction
and feedback to students (using expert system, cognitive psychology and learning sciences). ITS

have been used in several domains, from middle school math (Ritter et al. 2007) and physics
(Vanlehn et al. 2005), to programming languages (Corbett and Anderson 1992) and military
applications (McCarthy 2008). Many experiments have proved that ITS can be beneficial to learning
(Ritter et al. 2007; Vanlehn et al. 2005; Corbett and Anderson 1992). However, their popularity
outside the academia is relatively low.
Learning technologies and educational systems have become part of the infrastructure in most
of the educational institutions around the world. LMS, PLE and other kinds of educational platforms
are now very common in our schools and universities (Beatty and Ulasewicz 2006). Regrettably,
these educational tools have been mainly used to store plain educational content (Sabbir-Ahmed
2004). This type of content (such as PDF and PPT) cannot provide the high quality educational
assistance that technology can (Brusilovsky et al. 2007).
Some of the main reasons for the reduced attractiveness of ITS include: 1) the intrinsic
complexity of their development process (Aleven et al. 2009); 2) the impossibility of loading them in
different platforms (Rey-López et al. 2008); 3) the extra effort required to make them available over
the Web (Wijekumarr et al. 2003; Mia 1997). To address some of the limitations mentioned above,
we have studied an approach (Santos and Figueira 2010a) and also prototype (Santos and Figueira
2010b) for making ITS more viable to educational institutions.
Intelligent tutoring systems (ITSs) are interactive educational systems that are built by
combining from expert system and concepts from the learning sciences. These systems proved to be
beneficial for learning in several domains, from programming languages and middle school math, to
physics and military applications. Unfortunately, because of interoperability issues, ITSs cannot be
loaded into most educational platforms that are currently available and that require dedicated
nonstandard frameworks. Thus, this approach has the advantage of yielding tutors which are fully
conformant to e-learning standards and that are free of external resource dependencies. According to
our method, “atomic” tutoring systems are grouped to create “molecular” tree structures that cover
course modules. In addition, given the interoperability of my technique, tutors can also be combined
to create courses that have distinct granularities, topics, and target students. The key to my method is
the focus on assuring what defines a tutor in terms of behavior and functionalities (inner loops and
outer loops). Our proof of concept was developed using SCORM standards.
To overcome this issue recently method is presented in [1]. To increase the accessibility of
ITSs, authors have developed an approach for implementing interoperable tutors with the support of
standards [1]. This method target the sharable content object reference model (SCORM) e-learning
standards. This method allows implementing web-based ITSs as learning objects (LOs) and using a
novel structural design that focuses on supporting the essential features of intelligent tutors, the inner
loop and the outer loop. However this recent method needs to improve in many ways further in
future. In this project we are extending this method by using the SCORM Tin Can API. This is new
version of SCORM which is more efficient than previous one and hence will improve the
performance of ITS.In contrast to other methods, our technique does not require using peripheral
systems or databases, which would restrict the interoperability of learning objects [2] Having a
functional web-based learning environment is a norm for a large number of educational institutions
today. [3] The current widespread use of the software is allowing us to test hypotheses across large
numbers of students. [5]Andes is a mature intelligent tutoring system that has helped hundreds of
students improve their learning of university physics. It replaces pencil and paper problem solving
homework.[6]They explore impediments to widespread adoption of these interventions throughout
the military, methods to overcome these impediments, and the migration of this technology into other
domains. [7]
Problem is some of the main reasons for the reduced attractiveness of ITS include the
intrinsic complexity of their development process, the compatibility of loading them in different
platforms, The extra effort necessary to make them available over the Web.
100

Regarding SCORM, ADL does not forbid the use of external resources for the development of
standardized LOs. ADL makes it clear that tying standardized LOs to nonstandard peripheral systems
compromises the interoperability of the application. External systems can preclude access to parts of
the source code.
Learning technologies and educational systems are now part of the infrastructure. LMS in
schools and universities, Mainly used to store plain educational content, Adaptive and personalized
educational systems need to provide very high quality educational assistance, ITS are adaptive
educational tools that offer direct personalized instruction and feedback to students. In this paper, the
main aim is to present the extended method for ITSs:
To present the present new framework and method, To present the practical simulation of proposed
solution and evaluate its performances, To present the comparative analysis of existing and proposed
methods in order to claim the efficiency.
Scope of this project is ITS is a wide area where intelligence applied to the distance learning,
It will brings significant improvement in learning system, It will enhanced Interoperability.
101
III. LITERATURE REVIEW
In the literature survey we are going to discuss Interoperable Intelligent Tutoring Systems as
Open Educational Resources: Below in literature we are discussing some of them.
Gustavo Soares Santos and JoaquimJorge[1]- Because of interoperability issues, intelligent
tutoring systems are difficult to deploy in current educational platforms without additional work.
This limitation is important as tutoring systems require considerable time and resources for their
implementation. In addition, because these tutors have a high educational value, it is desirable that
they could be shared, used by many stakeholders, and easily loaded onto different platforms. A new
approach to implementing open-source and interoperable intelligent tutors through standardization is
explained in this paper. In contrast to other methods, our technique does not require using non
standardized peripheral systems or databases, which would restrict the interoperability of learning
objects. Thus, this approach has the advantage of yielding tutors which are fully conformant to e-learning
standards and that are free of external resource dependencies. According to our method,
atomic tutoring systems are grouped to create molecular tree structures that cover course
modules. In addition, given the interoperability of our technique, tutors can also be added to create
courses that have distinct granularities, topics, and target students. The key to our method is the
focus on assuring what defines a tutor in terms of behavior and functionalities (inner loops and outer
loops). Our proof of concept was developed using SCORM standards. The implementation details of
our technique, including the theoretical concepts, technical specifications, and practical examples are
presented in this paper.
K. SabbirAhmed[3]- Having a functional web-based learning environment is a norm for a
large number of educational institutions today. But publishing plain e-Learning materials in this
environment does not contribute significantly to student’s learning unless a sound pedagogical
framework is adopted behind this process. Substantial researches have been done in the area of
Adaptive and Intelligent Tutoring Systems to develop web-based intelligent learning environments
(WILE) where the student’s current knowledge about the subject matter is stored in a student model
database and therefore the materials are presented according to the student’s learning need. Usually,
contents are an intrinsic part of these kind of learning environments, and difficult to port to another
environment in the case of reuse. This paper introduces a framework to develop dynamic content for
a SCORM-conformant web-based intelligent learning environment that can be ported to another
similar kind of learning environment.
S. Ritter, J.R. Anderson, K.R. Koedinger, and A. Corbett[5]-For 25 years, we have been
working to build cognitive models of mathematics, which have become a basis for middle- and high-

school curricula. The theoretical background of this approach and evidence that the resulting
curricular are more effective than other approaches to instruction are discussed. We also discuss how
embedding a well specified theory in our instructional software allows us to dynamically evaluate the
effectiveness of our instruction at a more detailed level than was previously possible. The present
widespread use of the software is allowing us to test hypotheses across large numbers of students.
We believe that this will lead to new approaches both to understanding mathematical cognition and
to improving instruction.
K. Vanlehn, C. Lynch, K. Schulze, J. Shapiro, R. Shelby, L. Taylor[6]-Andes is a mature
intelligent tutoring system that has helped hundreds of students improve their learning of university
physics. It replaces pencil and paper problem solving homework. Students continue to attend the
same lectures, labs and recitations. Five years of experimentation at the United States Naval
Academy indicates that it significantly improves student learning. This report describes the
evaluations and what was learned from them.
SCORM Standards
The SCORM is a set of standards and specifications for web based learning [12]. SCORM is
developed and maintained by the advanced distributed learning (ADL) initiative [13]; however,
SCORM is a product of several entities, such as IEEE, AICC, Ariadne, and IMS Global.
Intelligent Tutoring Systems
Intelligent tutoring systems are educational systems that can engage students in interactive
reasoning activities that require a deep understanding of the domain being taught and that also
require considerable comprehension of students’ behaviors. Intelligent tutors usually employ theories
of learning by doing [15] and can also apply a series of different technologies for implementation.
The classic architecture of a tutoring system comprises four elements or modules [16], [17],
[18]. The traditional instructional model of an ITS is based on students engaging in problem solving
activities through a user interface. The domain module (typically an expert system) evaluates the
actions that are performed by the students. The student model records what the ITS knows about the
students and the pedagogical module provides instructional interventions and feedback to the
apprentices.
This traditional view of ITSs is still very accepted by the community. However, recent papers
stress functionality over structure [19], [20], [21], describing ITSs as having two main loops [21]: 1)
the inner loop and 2) the outer loop. The inner loop is responsible for providing personalized
feedback, hints, and direct problem solving assistance to students. The inner loop also assesses
students’ competence and registers it on the student model. Using the information that is obtained
about the student, the outer loop performs task selection. Pseudo Code 1 illustrates this functional
view of ITSs.
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Related Work on the Interoperability of ITSs
Previous research on interoperable and adaptive educational systems has already presented
some excellent results [22], [23], [24], [25]. Project GRAPPLE focused on integrating LMSs with
adaptive learning environments, by developing architecture for a generic adaptive webserver, a
browser-based authoring environment, and a distributed user modeling framework. GRAPPLE can
be used for creating and serving web-based adaptive educational software.
GRAPPLE supports some types of adaptation, such as content, link, and presentation; in addition, it
has the capacity to support several types of user model information.
SCORM Standards
The SCORM is a set of standards and specifications for web based learning. SCORM is
developed and maintained by ADL, as a product of several entities, such as IEEE, AICC, Ariadne,
and IMS Global.

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Intelligent Tutoring Systems
ITSs are educational systems that can engage students in interactive reasoning activities that require
a deep understanding of the domain being taught and that also require considerable comprehension
of students’ behaviors.
Related Work on the Interoperability of ITSs
Project GRAPPLE focused on integrating LMSs with adaptive learning environments, by
developing architecture for a generic adaptive web server, a browser-based authoring environment,
and a distributed user modeling framework.
IV. OUR APPROCH TOWORDS INTEROPERABLE SYSTEM
Our approach to developing interoperable ITSs as OERs Through e-learning standards builds
on what defines a tutor in terms of behavior and functionality. Figure 1 shows Digramatic view of
sample ITS. For this ITSs organizes the tutors into tree structures that assemble two different
constructs:
1. Atomic tutoring systems: problem solving
2. Molecular tutoring systems: task selection
Figure 1: Digramatic view of sample ITS
For implementing the ITS loops, we rely on some SCORM constructs, especially the tracking
data and the sequencing definition, the ability to record information about students’ performances in
runtime.
The ability to use this information later for selecting activities
Inner Loop Implementation
ATs are responsible for providing Problem solving support and for implementing inner loop
services (such as the assessment of knowledge, hints, and error-specific feedback),Responsible for
providing problem solving support and for implementing inner loop services.
As required by SCORM, we use standard web-development technologies. The SCORM RTE
functions are used to handle the user model and to store and retrieve data about the students on the
server.

Outer Loop Implementation
SCORM provides a good built-in mechanism for implementing outer loops.
First, MTs aggregate ATs. Second, their main functionality is achieved by a set of task
selection rules. These rules reflect the educational guidelines that were established in the expert and
pedagogical models. Accordingly, the basic mechanism comprises using the rules to access the user
model, which is stored in the SCORM objectives and subsequently using the student information to
select tasks.
In proposed work we are presenting the approach for the development of interoperable ITSs
using e-learning standards. In contrast to other approaches, this proposed method does not require
extending standards with non standardized peripheral systems or databases.
Figure 2: System Architecture
This proposed method is based on the development of atomic tutoring systems that are
grouped to create molecular tutors, covering the curriculum of courses. In addition, our approach
focuses on assuring what defines a tutor in terms of behavior and functionalities (inner loops and
outer loops). In addition to this in this project we are using the SCORM Tin Can API; this is new
version of SCORM (Tin Can is promising more powerful ways of storing data about the users and
groups of users) which will further improve the performance of our ITSs. Also, we are combining
this SCORM LOs with our LMS which gives all the e-learning facilities to learner (students) as
whole learning system..
Algorithmic flow of overall LMS
1 Start
2 On Home Page
3 Click on Login
4 Login as an Admin go to step 7
5 Teachers goto step 7
6 Students go to step 7
7 Check authentication from DB.
8 If Admin go to step 11
9 If Teacher go to step 15
10 If Student go to step 20
11 Admin Login
12 Add and Launch training courses to SCORM
13 Perform Operations
14 Sign Out go to step 2
15 Teacher Login
104

16 Perform Operations
17 Add and Launch training courses to SCORM
18 If any exam Then add Exam for particular subject with date-time
19 Sign Out go to step 2
20 Student Login
21 Perform Operations such as download assignment, materials, and view notice
22 Take SCORM Training
24 If any exam then attend exam
25 Get Result
26 Sign out go to step 2
27 Stop
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Algorithm: SCORM TRAIN COURSE
1 Start
2 Enter in Course for training
3 Initialization: k=20, skill=k, Status=null
4 Repeat until (skill == 100)
5 Answer the Question
6 if(Correct Answer)
7 k=k+20;
8 skill=k;
9 if(Wrong Answer)
10 k=k-10;
11 skill=k;
12 if (Terminate Course)
13 Status=incomplete;
14 break;
15 Generate Result
16 if( Re-enter the course)
17 if(Status==incomplete)
18 Goto step 4.
19 else
20 Goto step 3.
21 Exit
V. APPROCH TO EVALUATION
The basic procedure that was used for testing was the following algorithmic approach:
1. Create SCORM Course using Standards
2. Access the SCORM compliant educational Platform and import the ITS in Test Suite.
3. Check for import errors or warnings.
4. Load the ITS.
5. Check for loading errors or warnings.
6. Verify if the outer loop selected the correct problem.
7. Solve the problem to verify the inner loop functionalities, Step by step, one by one.
8. Repeat Steps 6 and 7 until instruction is complete.

To evaluate our approach for implementing interoperable ITSs, the first step was to submit
our prototype to a SCORM conformance test using the ADL SCORM test suite.
Verifying compliance is an important step because it guarantees the correctness of the
SCORM package. Accordingly, the ADL test suite uses a step-by-step process to validate the whole
SCORM application, including the required API calls of each SCO.
Figure 3 shows a snapshot with the results of the conformance test of our prototype. This
figure shows that the package is compliant with all of the ADL requirements, and therefore, the
SCORM PIF should run correctly, assuring the interoperability of the educational software.
Figure 3: Conformance test result with success.
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Adding Content to SCORM
1. Login to SCORM Cloud
2. Add Content
3. Import Package
4. Dispatch content
5. Launch the Training
6. Invite People (Privately or publicly)
7. Logout
We have to add content in zip format which is done with successful conformance test figure 4
shows the snapshot when the content upload successfully figure 5 shows the snapshot when
content does not follow the standards.
Figure 4: Add Content successfully to SCORM Figure 5: Add Invalid Content to SCORM

After launching the course, we can take training. Figure 6 Shows the sample course.
Figure 6: Sample Course
The following figure 7 shows the screenshot of homepage of our LMS as a whole system
Figure 7: Homepage of our system
However, to guarantee that everything works appropriately, we have tested our prototype in
different educational platforms, using different browsers and also different operating systems which
shows in figure 8. The figure 9 shows the graph of performance.
The table 1 gives the idea of Comparison with existing system
Figure 8: Functionality Test
107

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp.
Table 1:
Features
Support
Comparison with existing system
Web-Based LOs
Personalization and adaptation
Integration with LMSs
LOs fully complaint to standards
Interoperable LOs free of
significant external dependencies
Open source and reusable
implementation code
Provides
Authoring tools
Open source implementation
template
Input:
GRAPPLE
Approach
There are number of Logins of Student. Teacher, Parents, in which different facility provided
by this application for learning.
Hardware and Software Used
Hardware Configuration
- Processor - Pentium –IV
- Speed - 1.1 GHz
- RAM - 256 MB (min)
- Hard Disk - 20 GB
- Key Board - Standard Windows Keyboard
- Monitor - SVGA
Software Configuration
- Operating System: Windows
- Programming Language: C#.Net, Asp.Net
- Database: SQL Server 2008
- Tool: MS Visual Studio 2010
- Server: IIS 6.0 or IIS 7.0
Figure
99-110 © IAEME
108
Intelligent
Approach
yes yes
yes yes
yes yes
no yes
no no
no no
no no
no no
9: Performance of System
0976-6367(Print),
Our
Approach
yes
yes
yes
yes
yes
yes
no
yes

109
VI. CONCLUSION AND FUTURE SCOPE
This paper describes an approach for the development of interoperable ITSs using e-learning
standards, The development of atomic tutoring systems that are grouped to create molecular tutors,
covering the curriculum of courses, Some new technologies such as Massive Open Online Courses
(MOOCs), and the new version of SCORM, are opening interesting research opportunities, Since
MOOCs target large communities of students, they naturally have diverse audiences that comprise
very distinct types of users.
ACKNOWLEDGEMENT
We are thankful to Gustavo Soares Santos and Joaquim Jorge, Technical University of
Lisbon as our work is solely based on their paper titled “Interoperable Intelligent Tutoring Systems
as Open Educational Resources.
REFERENCE
[1] Gustavo Soares Santos and Joaquim Jorge, Senior Member, IEEE Computer Society,
“Interoperable Intelligent Tutoring Systems as Open Educational Resources”, IEEE
Transaction on Learning Technologies, Vol.6, No.3, July-September 2013.
[2] A.A. Pin˜ a, “An Overview of Learning Management Systems,” Learning Management
System Technologies and Software Solutions for Online Teaching: Tools and Applications,
Y. Kats, ed., pp. 1-19, Information Science Reference, 2010.
[3] K. Sabbir Ahmed, “A Conceptual Framework for Web-Based Intelligent Learning
Environments Using SCORM-2004,” Proc. IEEE Int’l Conf. Advanced Learning
Technologies, pp. 12-15, 2004.
[4] A.T. Corbett and J.R. Anderson, “The LISP Intelligent Tutoring System: Research in Skill
Acquisition,” Computer Assisted Instruction and Intelligent Tutoring Systems: Establishing
Communication and Collaboration, J. Larkin and R. Chabay, eds., Erlbaum, 1992.
[5] S. Ritter, J.R. Anderson, K.R. Koedinger, and A. Corbett, “Cognitive Tutor: Applied
Research in Mathematics Education,”Psychonomic Bull. Rev., vol. 14, pp. 249-255,
Apr. 2007.
[6] K. Vanlehn, C. Lynch, K. Schulze, J. Shapiro, R. Shelby, L. Taylor, D. Treacy, A. Weinstein,
and M. Wintersgill, “The Andes Physics Tutoring System: Five Years of Evaluations,”
Proc. 12th Int’l Conf. Artificial Intelligence in Education, pp. 678-685, 2005.
[7] J.E. McCarthy, “Military Applications of Adaptive Training Technology,” Technology
Enhanced Learning: Best Practices, D.G. Lytras, P. Ordo´n˜ez de Pablos, and W. Huang,
eds., pp. 304-347, IGI Global, 2008.
[8] G. Santos and A. Figueira, “Web-Based Intelligent Tutoring Systems Using the SCORM
2004 Specification—A Conceptual Framework for Implementing SCORM Compliant
Intelligent Web-Based Learning Environments,” Proc. IEEE Int’l Conf. Advanced Learning
Technologies (ICALT ’10), pp. 676-678, 2010.
[9] G. Santos and A. Figueira, “Reusable and Inter-Operable Web-Based Intelligent Tutoring
Systems Using SCORM 2004,” Proc. Ninth European Conf. E-Learning, 2010.
[10] G. Santos and J. Jorge, “Interoperable Intelligent Tutoring Systems as SCORM Learning
Objects,” Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends,
Pen˜ a-Ayala, ed., pp. 133-160, Springer-Verlag, 2012.
[11] G. Santos and J. Jorge, “Atomic and Molecular Intelligent Tutoring Systems—A New
Architecture for Interoperable Tutors as Open Educational Resources,” Proc. IEEE Int’l
Conf. Advanced Learning Technologies (ICALT ’13), 2013.

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