BibTeX
@article{IJASEIT15945,
author = {Jaurès S. H. Kameni and Bernabé Batchakui and Roger Nkambou},
title = {Optimization of Search Environments for Learning Contexts},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {12},
number = {2},
year = {2022},
pages = {521--529},
keywords = {Information retrieval; search engine; search-as-learning; bloom’s taxonomy; natural language processing; question classification.},
abstract = {This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15945},
doi = {10.18517/ijaseit.12.2.15945}
}
EndNote
%A Kameni, Jaurès S. H.
%A Batchakui, Bernabé
%A Nkambou, Roger
%D 2022
%T Optimization of Search Environments for Learning Contexts
%B 2022
%9 Information retrieval; search engine; search-as-learning; bloom’s taxonomy; natural language processing; question classification.
%! Optimization of Search Environments for Learning Contexts
%K Information retrieval; search engine; search-as-learning; bloom’s taxonomy; natural language processing; question classification.
%X This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15945
%R doi:10.18517/ijaseit.12.2.15945
%J International Journal on Advanced Science, Engineering and Information Technology
%V 12
%N 2
%@ 2088-5334
IEEE
Jaurès S. H. Kameni,Bernabé Batchakui and Roger Nkambou,"Optimization of Search Environments for Learning Contexts," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 2, pp. 521-529, 2022. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.12.2.15945.
RefMan/ProCite (RIS)
TY - JOUR
AU - Kameni, Jaurès S. H.
AU - Batchakui, Bernabé
AU - Nkambou, Roger
PY - 2022
TI - Optimization of Search Environments for Learning Contexts
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 12 (2022) No. 2
Y2 - 2022
SP - 521
EP - 529
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - Information retrieval; search engine; search-as-learning; bloom’s taxonomy; natural language processing; question classification.
N2 - This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15945
DO - 10.18517/ijaseit.12.2.15945
RefWorks
RT Journal Article
ID 15945
A1 Kameni, Jaurès S. H.
A1 Batchakui, Bernabé
A1 Nkambou, Roger
T1 Optimization of Search Environments for Learning Contexts
JF International Journal on Advanced Science, Engineering and Information Technology
VO 12
IS 2
YR 2022
SP 521
OP 529
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Information retrieval; search engine; search-as-learning; bloom’s taxonomy; natural language processing; question classification.
AB This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15945
DO - 10.18517/ijaseit.12.2.15945
