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50120140506012 2-3

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

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50120140506012 2-3

  1. 1. 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:
  2. 2. 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 99 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
  3. 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 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
  4. 4. 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 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-
  5. 5. 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 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. 102 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.
  6. 6. 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 103 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.
  7. 7. 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 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
  8. 8. 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 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 105 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.
  9. 9. 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 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. 106 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
  10. 10. 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 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
  11. 11. 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
  12. 12. 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 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.
  13. 13. 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 [12] ADL,“SCORM,”http://www.adlnet.gov/Technologies/scorm, 2011. [13] ADL,“WhoWeAre,” http://www.adlnet.gov/About/Pages/Default.aspx, 2011. [14] ADL,“SCORMBenefits,” http://www.adlnet.gov/Documents/SCORM%20FAQ.aspx#scorm, 110 2011. [15] K.R. Koedinger and A.T. Corbett, “Cognitive Tutors: Technology Bringing Learning Science to the Classroom,” The Cambridge Handbook of the Learning Sciences, R.K. Sawyer, ed., pp. 61-78, Cambridge Univ. Press, 2006. [16] P. Brusilovsky, “The Construction and Application of Student Models in Intelligent Tutoring Systems,” J. Computer and Systems Sciences Int’l, vol. 32, no. 1, pp. 70-89, 1994. [17] A.T. Corbett, K.R. Koedinger, and J.R. Anderson, “Intelligent Tutoring Systems,” Handbook of Human-Computer Interaction, second ed., M.G. Helander, T.K. Landauer, and P.V. Prabhu, eds., Elsevier, 1997. [18] M.C. Polson and J.J. Richardson, Foundations of Intelligent Tutoring Systems. Lawrence Erlbaum, 1988. [19] V. Aleven, B.M. McLaren, and J. Sewall, “Scaling Up Programming by Demonstration for Intelligent Tutoring Systems Development: An Open-Access Web Site for Middle School Mathematics Learning,” IEEE Trans. Learning Technologies, vol. 2, no. 2, pp. 64-78, Apr.-June 2009. [20] V. Aleven, B.M. McLaren, J. Sewall, and K.R. Koedinger, “A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors,” Int’l J. Artificial Intelligence in Education, vol. 19, pp. 105- 154, 2009. [21] K. Vanlehn, “The Behavior of Tutoring Systems,” Int’l J. Artificial Intelligence in Education, vol. 16, pp. 227-265, 2006. [22] P. De Bra, D. Smits, K. Van der Sluijs, A. Cristea, J. Foss, and C. Steiner, “GRAPPLE: Learning Management Systems Meet Adaptive Learning Environments,” Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends, A. Pen˜ a-Ayala, ed.,vol. 17, pp. 133-160, Springer-Verlag, 2012. [23] M. Rey-Lo´pez, P. Brusilovsky, M. Meccawy, R. Dı´az-Redondo, A. Ferna´ndez-Vilas, and H. Ashman, “Resolving the Problem of Intelligent Learning Content in Learning Management Systems,” Int’l J. E-Learning, vol. 7, pp. 363-381, 2008. [24] M. Rey-Lo´pez, R. Dı´az-Redondo, A. Ferna´ndez-Vilas, J. Pazos Arias, J. Garcı´a-Duque, A. Gil-Solla, and M. Ramos-Cabrer, “An Extension to the ADL SCORM Standard to Support Adaptivity: The T-Learning Case-Study,” Computer Standards Interfaces, vol. 31, pp. 309-318, 2009. [25] P.D. Bra and D. Smits, “A Fully Generic Approach for Realizing the Adaptive Web,” Proc. 38th Int’l Conf. Current Trends in Theory and Practice of Computer Science, 2012. [26] Pooja Manghirmalani Mishra and Dr. Sushil Kulkarni, “Classification of Data using Semi- Supervised Learning (A Learning Disability Case Study)”, International Journal of Computer Engineering Technology (IJCET), Volume 4, Issue 4, 2013, pp. 432 - 440, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [27] Tanmaya Kumar Das, Dillip Kumar Mahapatra and Gopakrishna Pradhan, “An Integrated Framework for Interoperable and Service Oriented Management of Large Scale Software”, International Journal of Computer Engineering Technology (IJCET), Volume 3, Issue 3, 2012, pp. 459 - 483, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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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

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