To develop an approach to find one’s expertization level in a given field. Methods/Statistical An... more To develop an approach to find one’s expertization level in a given field. Methods/Statistical Analysis: The search engines were utilized to extract the expert’s data available in the internet. The results generated by the search engines were downloaded in the database. Intern these results were used as an input to the relevance and expertise mapping process. We have used Relevance Algorithm and Expertise Mapping Algorithm to process the data generated by the search engines. These process yields one’s expertization level on a particular area. Findings: E-learning systems were automated to identify an expert by using two methods self-classification and document-based-relevance. These methods assume that relevance of one’s specified keywords or documents to the query is positively related to their expertise. In reality, one can be identified as an expert in a specific domain by the contribution made on that particular domain. The expert expertization data available in websites could be utilized by the e-learning systems to evaluate the expert’s. Hence it is proposed to ensure the expertise level of an expert using a Dynamic Expertization Estimating System (DEES) in an e-learning environment. In this approach, search engines were utilized as an agent to extract the expert’s data available in the internet and weightage were given to the expert according to their contribution made by the expert towards the given expertise area. The results retrieved by applying this mechanism yielded data with high accuracy levels to ensure the expertization level of an expert. Application/Improvements: Connecting the Dynamic Expertization Estimating System (DEES) with social media to fetch more data pertaining to the expert expertization and produce accurate expertise level for each expert.
The International Review of Research in Open and Distributed Learning, 2014
E-learning or electronic learning platforms facilitate delivery of the knowledge spectrum to the... more E-learning or electronic learning platforms facilitate delivery of the knowledge spectrum to the learning community through information and communication technologies. The transfer of knowledge takes place from experts to learners, and externalization of the knowledge transfer is significant. In the e-learning environment, the learners seek subject expertise to clarify their subject queries, and a learner query can be routed to an expert for externalization of expert knowledge provided the learner knows the subject expert or the expertise group. However, learners new to e-learning systems are not aware of the expertise group to which the query should be sent, which results in time delays, non-response, inaccurate solutions and loss of knowledge capture. Several models have been proposed to resolve this task, but thus far, these efforts have focused completely on returning the most conversant people as experts on a particular topic to retrieve valuable knowledge. To address this problem, we propose an approach that externalizes the tacit knowledge of a subject expert by creating a dynamic query handling system that automatically transfers a user query to the best subject expert.
To develop an approach to find one’s expertization level in a given field. Methods/Statistical An... more To develop an approach to find one’s expertization level in a given field. Methods/Statistical Analysis: The search engines were utilized to extract the expert’s data available in the internet. The results generated by the search engines were downloaded in the database. Intern these results were used as an input to the relevance and expertise mapping process. We have used Relevance Algorithm and Expertise Mapping Algorithm to process the data generated by the search engines. These process yields one’s expertization level on a particular area. Findings: E-learning systems were automated to identify an expert by using two methods self-classification and document-based-relevance. These methods assume that relevance of one’s specified keywords or documents to the query is positively related to their expertise. In reality, one can be identified as an expert in a specific domain by the contribution made on that particular domain. The expert expertization data available in websites could be utilized by the e-learning systems to evaluate the expert’s. Hence it is proposed to ensure the expertise level of an expert using a Dynamic Expertization Estimating System (DEES) in an e-learning environment. In this approach, search engines were utilized as an agent to extract the expert’s data available in the internet and weightage were given to the expert according to their contribution made by the expert towards the given expertise area. The results retrieved by applying this mechanism yielded data with high accuracy levels to ensure the expertization level of an expert. Application/Improvements: Connecting the Dynamic Expertization Estimating System (DEES) with social media to fetch more data pertaining to the expert expertization and produce accurate expertise level for each expert.
The International Review of Research in Open and Distributed Learning, 2014
E-learning or electronic learning platforms facilitate delivery of the knowledge spectrum to the... more E-learning or electronic learning platforms facilitate delivery of the knowledge spectrum to the learning community through information and communication technologies. The transfer of knowledge takes place from experts to learners, and externalization of the knowledge transfer is significant. In the e-learning environment, the learners seek subject expertise to clarify their subject queries, and a learner query can be routed to an expert for externalization of expert knowledge provided the learner knows the subject expert or the expertise group. However, learners new to e-learning systems are not aware of the expertise group to which the query should be sent, which results in time delays, non-response, inaccurate solutions and loss of knowledge capture. Several models have been proposed to resolve this task, but thus far, these efforts have focused completely on returning the most conversant people as experts on a particular topic to retrieve valuable knowledge. To address this problem, we propose an approach that externalizes the tacit knowledge of a subject expert by creating a dynamic query handling system that automatically transfers a user query to the best subject expert.
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Papers by Dr.ABDUL AZEEZ KHAN
search engines were utilized to extract the expert’s data available in the internet. The results generated by the search
engines were downloaded in the database. Intern these results were used as an input to the relevance and expertise
mapping process. We have used Relevance Algorithm and Expertise Mapping Algorithm to process the data generated by
the search engines. These process yields one’s expertization level on a particular area. Findings: E-learning systems were
automated to identify an expert by using two methods self-classification and document-based-relevance. These methods
assume that relevance of one’s specified keywords or documents to the query is positively related to their expertise. In
reality, one can be identified as an expert in a specific domain by the contribution made on that particular domain. The
expert expertization data available in websites could be utilized by the e-learning systems to evaluate the expert’s. Hence
it is proposed to ensure the expertise level of an expert using a Dynamic Expertization Estimating System (DEES) in an
e-learning environment. In this approach, search engines were utilized as an agent to extract the expert’s data available
in the internet and weightage were given to the expert according to their contribution made by the expert towards the
given expertise area. The results retrieved by applying this mechanism yielded data with high accuracy levels to ensure the
expertization level of an expert. Application/Improvements: Connecting the Dynamic Expertization Estimating System
(DEES) with social media to fetch more data pertaining to the expert expertization and produce accurate expertise level
for each expert.
search engines were utilized to extract the expert’s data available in the internet. The results generated by the search
engines were downloaded in the database. Intern these results were used as an input to the relevance and expertise
mapping process. We have used Relevance Algorithm and Expertise Mapping Algorithm to process the data generated by
the search engines. These process yields one’s expertization level on a particular area. Findings: E-learning systems were
automated to identify an expert by using two methods self-classification and document-based-relevance. These methods
assume that relevance of one’s specified keywords or documents to the query is positively related to their expertise. In
reality, one can be identified as an expert in a specific domain by the contribution made on that particular domain. The
expert expertization data available in websites could be utilized by the e-learning systems to evaluate the expert’s. Hence
it is proposed to ensure the expertise level of an expert using a Dynamic Expertization Estimating System (DEES) in an
e-learning environment. In this approach, search engines were utilized as an agent to extract the expert’s data available
in the internet and weightage were given to the expert according to their contribution made by the expert towards the
given expertise area. The results retrieved by applying this mechanism yielded data with high accuracy levels to ensure the
expertization level of an expert. Application/Improvements: Connecting the Dynamic Expertization Estimating System
(DEES) with social media to fetch more data pertaining to the expert expertization and produce accurate expertise level
for each expert.