The unprecedented growth of the Internet, its pervasive accessibility, and ease of use has increased students dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics, such as the individual s learning capability and readiness for specific learning materials. As a result, the same set of search-keywords delivers the same search results. This situation hinders the optimization of the Web search engines in supporting the heterogeneity of its users in their learning endeavors. This Project aims to address the issue. It attempts to augment Web search engines with personalized recommendations of search results which match students learning competencies and behaviors. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students.
E-learning, which refers to learning through the use of electronic tools usually on the Internet such as web search engines, in current situations, enables
learners of all ages, competencies and preferences, to look for information or knowledge ``anywhere and anytime . It also allows learners instant access to particular information. Keywords are required in making a web search and it appears that learners or students are given the same keywords when making their search through any major commercial search engines such as Google, Yahoo or Bing). In this context, user s profile information should be considered together with their queries by Web links. This is particularly important in the educational environment where students have different educational backgrounds and learning behaviours which indirectly, influence their learning progress and acceptability. In this context, user s profile information should be considered together with their queries by Web links. This is particularly important in the educational environment where students have different educational backgrounds and learning behaviours which indirectly, influence their learning progress and acceptability. An ideal search result could be one that is based on a personal evaluation of the user s daily information needs derived from the returned links or contents of the Web search engine. Similarly, in a collaborative learning environment, an assessment taken from a group of similar learner provides can reveal more significant input for the learners rather than just the relevancy of the webpages.
Novice learners often struggle with finding the right keywords particularly when they are new to the learning topics.
It may also have difficulties in selecting the most relevant links from the huge number of link results returned by the search engines.
Some learners with difficulties in searching for contents that match their requirements.
We present a personalised group-based recommendation approach for Web search in e-learning. The primary motivation of this study is to present an adaptive
e-learning method for students of different learning capabilities when using the popular search engines. To achieve this, a Web search recommender system was developed as a gateway between the Google search engine and the institutional .
e-learning portal so as to enable the search engine to deliver personalised search results as recommendations for students based on their individual needs.
Web search engines provide limited capabilities in personalizing the search results according to the student s provide even though the Web search engines were the most popular tool used by students in searching for educational materials. Majority of research looking at adaptive learning approaches had concentrated mainly on the personalization of learning materials in various e-learning systems but not on the Web search engines. Several studies have examined the use of the Web search engines as e-learning tools but thus far, to the best of our knowledge no study was performed in providing personalised recommendations to students using Web search engines. Groupization algorithm has not yet been implemented in personalizing the delivery of learning materials through Web search engines.
ADVANTAGE OF PROPOSED SYSTEM
The participations of each group increases, their browsing histories also become richer hence making it possible for to recommend links that were personalised to their profiles.
Decision Tree Algorithm
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL
C. K. Hsu, G. J. Hwang, and C. K. Chang, ``A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students, Comput. Educ., vol. 63, pp. 327_336, Apr. 2013.