%0 Journal Article %1 jesudoss2014automatic %A Jesudoss, J.Stanley %A Kumar, S.Selva %D 2014 %J International Journal of Computer Networks and Applications (IJCNA) %K Architecture Artificial CAA Class Different Intelligence Taxonomy The assessment concept education hierarchy map objectives of tool types %N 1 %P 40-47 %T Automatic Feedback Framework for Deriving Educational Ontologies %U http://www.ijcna.org/Manuscripts/Volume-1/Issue-1/Vol-1-issue-1-M-06.pdf %V 1 %X Automatic feedback generation is an important feature of Computer Assisted Assessment (CAA) systems. Feedback can help learners to diagnose their learning status and educational knowledge. The education ontology is created in the protégé tool. Questions are generated and the examinee’s is to provide the answers for the given questions. System will generate the adaptive feedback based on the user’s response. Learner’s answer will be assessed from the ontology. And then based on the examinee’s response the adaptive feedback is generated for right and wrong answers. Adaptive Feedback can be both human readable format and machine readable format. Users learning status can be identified from the adaptive feedback. Feedback can be generated from the metadata of items. Adaptive feedback can help learner’s knowledge level and examinees can improve their knowledge level.

@article{jesudoss2014automatic, abstract = {Automatic feedback generation is an important feature of Computer Assisted Assessment (CAA) systems. Feedback can help learners to diagnose their learning status and educational knowledge. The education ontology is created in the protégé tool. Questions are generated and the examinee’s is to provide the answers for the given questions. System will generate the adaptive feedback based on the user’s response. Learner’s answer will be assessed from the ontology. And then based on the examinee’s response the adaptive feedback is generated for right and wrong answers. Adaptive Feedback can be both human readable format and machine readable format. Users learning status can be identified from the adaptive feedback. Feedback can be generated from the metadata of items. Adaptive feedback can help learner’s knowledge level and examinees can improve their knowledge level.}, added-at = {2016-10-11T22:55:45.000+0200}, author = {Jesudoss, J.Stanley and Kumar, S.Selva}, biburl = {https://www.bibsonomy.org/bibtex/2b9f468ca5a3c72b0160d40c1f8a126e4/ijcnaeditor}, interhash = {482b9115c83a74b37768e9a6e6f03f1c}, intrahash = {b9f468ca5a3c72b0160d40c1f8a126e4}, journal = {International Journal of Computer Networks and Applications (IJCNA)}, keywords = {Architecture Artificial CAA Class Different Intelligence Taxonomy The assessment concept education hierarchy map objectives of tool types}, month = {December}, number = 1, pages = {40-47}, timestamp = {2016-10-11T22:55:45.000+0200}, title = {Automatic Feedback Framework for Deriving Educational Ontologies}, url = {http://www.ijcna.org/Manuscripts/Volume-1/Issue-1/Vol-1-issue-1-M-06.pdf}, volume = 1, year = 2014 }