Improved Content Personalization in E-Learning using MQACP

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Improved Content Personalization in E-Learning using MQACP

  1. Vijayalakshmi I. Shamili D. Sundaranarayana

    Assistant Professor, Dept of CSE, Final Year M.Tech, Dept of CSE, Final Year M.Tech, Dept of CSE,

    Christ College of Engg. & Tech, Pondicherry, India.

    Christ College of Engg. & Tech, Pondicherry, India.

    Christ College of Engg. & Tech, Pondicherry, India.

    AbstractData Mining is a process of identifying invisible patterns and relationship within data that can help for decision making. E-learning is a tool which supports teaching and learning using a computer web technology. The main aim of the E-learning is to provide exact content to the well known students profiles and preferences and also to specific the better content for each student. In this paper we proposed the comparison of techniques and algorithms which used in the system to improve the student knowledge level and academic progress, but the quality of e-learning service is low and so there is a lack of interaction among e-learners. The proposed System deals with a comprehensive metadata quality pledge method, specifically the (MQACP) Metadata Quality Assurance Certification Process. The basic of this method is to propose a set of methods that can be deployed throughout the lifecycle of a repository to ensure that metadata generated from content providers are of high quality. This system deals with Metadata quality assurance certification process to improve the high quality of learning object in digital repository and proper identification of metadata labeling. We measured the resulting metadata quality to certify that the anticipated improvement in metadata quality actually took place.

    KeywordContent Personalization; E-learning Planning; Intelligent E-Learning System; Educational technology.

    1. INTRODUCTION

      E-learning is the use of electronic instructive technology in knowledge and education. The development of e-learning systems and online courses has made instructive objects broadly available by students of changing ability, backgrounds and manners. An important necessity of the current e-learning systems is the personalization that is a role able to adapt the e-earning content or serves to the customer profile. The personalization

      Includes how to find and filter the studying information that suits the user preferences and wants a most important part of instructional plan and e-learning growth is to know the behavior of learners. This way, an e-learning experience can be created that offers the most benefit for the learners and ensure that every component of the e- learning course is helping them to reaching their objectives and goals.

      Personalized content in E-learning can particularly improve the learning routes personalization in an almost transparent way to the user. The fundamental thought is to build a powerfully-related and planned leaning route capable of fulfilling each students profile. The proposed System deals with a comprehensive metadata quality pledge method, specifically the (MQACP) Metadata Quality Assurance Certification Process. A digital repository is a method for supervising and storing digital content. Repositories can be focus or institutional in their centre of attention. Placing content into an institutional repository enables employees and institutions to supervise and maintain it, and thus gain maximum value from it.

      A repository can support explore, learning, and executive processes. Repositories use open principles to certify that the content they contain is available in that it can be sought and recalled for later use. The use of these approved international standards permits mechanisms to be place high which classify, store, retrieve, import and export the digital content inside the repository.The basic idea of this method is to propose a set of methods that can be deployed throughout the lifecycle of a repository to ensure that metadata generated from content providers are of high quality. We supported the metadata creation process throughout the different phases of the repositories involved by setting up the MQACP.

      During each level, we evaluated the ensuing metadata quality to attest that the expected improvement in metadata quality essentially took Place. The results showed that the MQACP was successfully adapted to the latest contexts, with smallest amount adjustment desired, through related outcome formed and also with comparable costs. We

      Can also determine high Quality assurance methods

    2. LITERATURE SURVEY

      For checking metadata documentation quality that will have an optimistic outcome on the resulting quality of the metadata records of a given group of repositories. Our Proposed Metadata Quality Assurance Certification Process that can be applies in several domains with proven effectiveness.

      PAPER NAME

      YEAR

      EXISTING

      DISADVANTAGE

      PROPOSED

      ADVANTAGE

      TECHNIQUE

      ALGORITHMS

      E-Learning and

      Feb 2014

      1.Adjacency

      To Monitor and Adapt

      The myPTutor

      The planning

      Case-Based

      Intelligent

      Matrices

      the learning object of

      approach

      Techniques helps

      Planning

      planning:

      2.Integer

      each learning route

      to bridge the gap

      Technique

      Improving

      Programming

      against unexpected

      between the e-

      Content

      Constraints

      contingencies

      learning

      Personalization

      Satisfaction

      necessities and

      Models

      the student

      3.Neural

      Content

      Network

      Adaptation

      4.Soft

      Computing

      Methods

      A Generic

      July 2013

      One Size-Fits

      Low Quality of e-

      Personalized

      1.Improve the

      Intelligent e-

      Inherited

      Adaptive

      Model of

      All

      learning Service

      search engine

      Quality of e-

      learning System

      Object-Oriented

      student-based

      learning Service

      Structure

      of the

      Adaptive

      2.To Achieve the

      Course Material

      Intelligent Web-

      Student learning

      Based learning

      goals effectively

      Environment

      E-Learning

      2013

      Educational

      Lack of Interest in

      Data Mining in

      1.Improve

      Visualization

      Using Data

      Data Mining

      Education Data Mining

      E-learning

      corporate the

      Technique

      Mining

      (EDM)

      System

      learning task

      2.Successfully

      incorporate to e-

      learning

      environment

      Quality

      June

      Numerous

      Low Quality of e-

      QA

      Increasing the

      Quality Assurance

      Grade

      Assurance of e-

      2011

      initiative have

      learning

      Methodology

      Quality of e-

      Correspondence

      learning

      been developed

      learning

      cluster analysis

      Processes

      on QA in

      Education

      Applying case-

      2011

      AI Planning

      Sequence of learning

      Approach to

      Allow the best

      Case-Based plan

      Learning

      object

      Based Planning

      Objects (LO)

      Automatically

      learning routes for

      Merging technique

      Algorithm

      to personalized

      extract

      each student

      e-learning

      information

      profile and course

      from the LO

      objectives

      Planning and

      2011

      Adjacency

      Execution of the learning

      Adaptation

      Reuse the original

      LPG-

      Execution in

      Matrics, Integer

      routes, check its progress

      Approach (Plan

      Route as much as

      ADAPT,LPG &

      personalized E-

      Programming

      and act when

      Stability)

      possible and adapt

      SGPLAN6

      learning Setting

      Model,

      discrepancies appears

      /replan

      Neural Network

      and AI Planning

      Data Mining

      2010

      Educational

      Resulted in a high Drop

      Dynamically

      1.During the

      Cluster difficulty

      K-means

      For

      Data Mining

      out of Participants

      tailored hints

      challenge in the

      ranking

      algorithms

      Individualized

      (EDM)

      for users

      form of hints and

      Hints in e-

      easier access to

      learning

      notes

      2.hinting system

      were evaluated

      through a large

      scale participants

      Discovering

      2007

      Filter or

      Not paying too much

      Adaptive

      User can make a

      Stretch-Text

      IB algorithm

      student

      Sort

      attention to student

      Machine

      better use of it

      techniques

      preferences in

      preferences

      learning system

      e-learning

      E-Learning

      2007

      Electronic

      Lack on Interaction of

      Learners

      Improving the

      Data driven

      Frequent

      Episode

      Process

      Learning

      Learners

      Profile

      Flexibility and

      Approach

      Discovery

      Characterization

      Environment

      Characterization

      Authenticity of

      Algorithm(FED)

      using Data

      the Learners and

      driven

      cost Benefits

      Approaches

      Ratio

      In intelligent planning Antonio Garrido and Lluvia Morales rescues a victorious technique called myPTutor is a combined effort to take improvement of Artificial Intelligence planning techniques in the adaptation of sequences of Learning Objects to pedagogic and students' necessities[1]. In [5, 6] Ivan Serina prove that the imminent is a real valuable to increase the stability of the education course as well as to increase the performance and quality of the education routes. The most important intention of intelligent e-learning environments is to get better students learning progression by generous higher educational techniques, so that intelligent learning environments are supported on different pedagogical approach and theories that have been originated in the education field [2, 3] Nedhal A. M. Al Saiyd, Intisar A. M. Al-Sayed and Shimaa Abd Elkader Abd Elaal recommended the suitable architectural design for the growth modules of intelligent and contented with the intelligent Web-based learning environment about the effectiveness and competence. Zbigniew Mrozek [4] a quality assurance system (QA) had better to make sure the Necessitates is satisfied. Similarly the significance of fundamental guarantee plus learner perceptions, Increase in Educational facility, and also improves efficiency of E- learning system. In [7] Anna Katrina Dominguez, Kalina Yacef, James R. Curran well again Higher average mark achieved from user who be permissible with preventative hints to put off future mistakes. In [8] Cristina Carmona, Gladys Castillo, Eva Millán brought in an adaptive learning user model aimed at

      discovery out the students preferences about the educational materials terminate Time, such model is actually appropriate in e-learning systems so as to require to filtering the large intensities o information available, and so users can build a better use of it. Silvia Rita Viola

      [9] recommended the approaches used for learners profiles characterization. significant the value of the learning system used by learners in Learners profiles characterization in both the area by means of deference to different ways of non linear navigation. By comparing all techniques and algorithms is shown in survey table [10] those techniques and algorithms which used in the system to recover the student knowledge level and academic progress, although the quality of e-learning examine is low and so there is a lack of interaction among e-learners.

    3. EXISTING SYSTEM

      Data mining extract the data as of the e-learning content in order to adjust, stores, reprocess and to select the righter learning method according to the students profile and preference. By applying the myPTutor techniques [12] they have suggested how to monitor and adjust the studying objectives of every education routes against surprising contingencies.

      The techniques comprise of 4 steps. The First one is Knowledge Extraction consist Automated techniques of information technology to mine their necessary features. Second one is the Solve Problem It involves the usage of a

      smart planner to discover a plan or education path. Third is Execute & Monitor the implementation of the education path inside a LMS that supports supervising.

      We assure whether the actual state meets the awaited state. If a disagreement is noticed, that is if changeability happens on the route implementation, we apply plan- validation technique to assure if the path is quiet possible. The last and final one is Repair/Adapt the plan it requires fixing or adjusting the plan to make it executable again. The drawbacks found in the existing system is the explanation of the contents is not sufficient and the quality of the produced metadata records is not in high quality.

    4. PROPOSED SYSTEM

      The Present System proposing a complete metadata quality assurance technique, namely the Metadata Quality Assurance Certification Process (MQACP). The fundamental estimate of this technique is to propose a set of methods that can be deployed during the lifecycle of a repository to ensure that metadata generated from content providers are of high quality. This method handles with Metadata quality assurance certification process to improve the high quality of learning object in digital repository and proper recognition of metadata labeling. We evaluated the resulting metadata quality to certify that the excepted improvement in metadata quality really took place. The main advantages of proposed system it gives Accuracy, Correctness, Completeness, and Appropriateness of Metadata labeling. The automated mechanisms of measuring information quality that can easily be scaled up and maintained. The metadata quality is an integral part of any digital repository project and a critical success factor.

    5. OVERALL ARCHITECTURE

      Fig 1: System Architecture

    6. ALGORITHMS

      Fig 2: K-Means Clustering Algorithms Flowchart

    7. MODULES DESCRIPTION

      In [11] MQACP method personalized to the contents, with lowest adaptations required, and moreover with equivalent expenses. Metadata lies at the spirit of each digital repository plan in the logic that it determines and causes the explanation of digital content stored in the repositories. Metadata permits content to be successfully stored, managed and retrieved but also conserved in the long-term. Although the large significance of metadata in digital repositories, solitary that is broadly established, examines specify that what is defined as metadata quality is comparatively low in nearly events of digital repositories [13]. Metadata superiority is freely defined as suitability for purpose meaning that low quality of metadata means that metadata cannot complete its principle which is to permit for the retrieval of resources, management and successful storage. In present low metadata quality conducts to useless searches for content, no more resources which make them unseen to the aimed user, which is the customer of each digital repository.

      The present thesis approaches such problem by suggesting a broad metadata quality pledge method, specifically the (MQACP) Metadata Quality Assurance Certification Process. The basic idea of this thesis is to suggest a set of methods that can be deployed all over the development of a repository to assure that metadata generated from content providers are of high quality. These methods have to be direct, simple to apply with important outcome. They also have to be adaptable with minimum effort so that they can be used in different contexts easily. This set of methods was described systematically, taking into account the actors needed to apply them, drawing the tools required and significant the expected outcomes.

    8. CONCUSLION

Modern courses in remoteness education center of attention on exhibiting digital contents on LMSs and on covering such contents as learning objects labeled according to SCORM standards. The aim of these standards is perhaps to sell, exchange among platforms and/or schools, or else just entrée the contents distantly and all of these by using LMS-based environments. subsequent this thread, in this paper we have presented a general system implemented on top of Moodle that facilitates the use of generic GUIs to build up, incorporate and manage the alteration system based on intelligent planning. The preparation tools do not permit us to personalize the learning paths, other than in addition to carry out, observe their advance and adapt them when unexpected contingencies are found.

REFERENCES

  1. Antonio Garrido and Lluvia Morales, E-Learning and Intelligent Planning: Improving Content Personalization, IEEE Revista Iberoamericande Technologies Del Aprendizaje, Vol.9, No.1, Feb.2014.

  2. Nedhal A.M.AI Saiyd, And Intisar A.M.AI -Sayed , A Generic Model Of Student-Based Adaptive Intelligent Web-Based Learning Environment, Proceedings Of The World Congress On Engineering 2013 Vol II, WCE 2013, July 3-5, 2013, London, U.K.

  3. Shimaa Abd Elkader Abd Elaal, E-Learning Using Data Mining, Chinese-Egyptian Research Journal Helwan University 2013.

  4. Zbigniew Mrozek, Quality Assurance Of E-Learning, Presented On 22-Nd EAEEIE Annual Conference, EAEEIE 2011, Maribor, Slovenia, and JUNE 13-15, 2011.

  5. Antonio Garrido, Lluvia Morales and Ivan Serina, Applying Case-Base Planning To Personalize E-Learning, Proceedings/ Dms11/ DET/_Antonio_ GARRIDO.PDF 2011.

  6. L. Morales, A. Garrido, and I. Serina, Planning and Execution in A Personalized E-Learning Setting, In Advances in Artificial Intelligence (Lecture Notes In Computer Science). New York, NY, USA: Springer- Verlag, 2011, Pp. 232242.

  7. Anna Katrina Dominguez, Kalina Yacef, James R. Data Mining for Individualized Hints in E-learning, University Of Sydney, Australia 2010.

  8. Cristina Carmona1, Gladys Castillo2 and Eva Millán. Discovering Student Preferences In E-Learning, International Workshop On Applying Data Mining In E-Learning (ADML07) As Part Of The Second European Conference On Technology Enhanced Learning (EC-TEL07) 2007.

  9. Silvia Rita Viola, E-Learning Process Characterization Using Data Driven Approaches, International Workshop On Applying Data Mining In E-Learning (ADML07) As Part Of The Second European Conference On Technology Enhanced Learning (EC-TEL07) 2007.

  10. V.vijayalakshmi, I.shamili, K.kaarguzhali, Survey on E-learning: Comparison Techniques and Algorithms International Conference on Evolving Trends in Engineering & Technology (ICETET 2015).

  11. Palavitsinis. N, Manouselis. N, and Sanchez. S, (2011). "Metadata quality in learning repositories: Issues and considerations, in Proc. of the World Conference on Educational Multimedia, Hypermedia & Telecomunications (ED-MEDIA 2011), Lisbon, Portugal

  12. Phivos Mylonas, Paraskevi Tzouveli and Stefanos Kollias, E- learning and intelligent content adaptation: an integrated approach, Int. J. Cont. Engineering Education and Life-Long Learning, Vol. x,

    No. x, 200x 273 2007

  13. Sarah Currier, Jane Barton, Rónán OBeirne & Ben Ryan,

Quality assurance for digital learning object repositories: issues for the metadata creation process, ALT-J, Research in Learning Technology Vol.12, No. 1, March 2004

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