A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Student Query Trend Assessment with Semantical Annotation and Artificial Intelligent Multi-Agents
2017
Eurasia Journal of Mathematics, Science and Technology Education
Research in era of data representation to contribute and improve key data policy involving the assessment of learning, training and English language competency. Students are required to communicate in English with high level impact using language and influence. The electronic technology works to assess students' questions positively enabling semantics and intelligence in the field concerning education and health. Assessing the importance and complexity of the statement used in a query can save
doi:10.12973/eurasia.2017.00763a
fatcat:cjr6ma4hxjex3fk3nrsbfd4nyq