INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.11, No.9, Sep. 2019

Student Testing and Monitoring System (Stms) Using Nlp

Full Text (PDF, 457KB), PP.26-34


Views:196   Downloads:3

Author(s)

Muhammad Saad, Shanzah Aslam, Warda Yousaf, Moeed Sehnan, Sidra Anwar, Danish Rehman

Index Terms

Web based Student Testing and Monitoring System (STMS);Natural Language Processing;Artificial Intelligence;Semantic Role Labelling;Machine Learning;Wikipedia Scraping;Text Mining

Abstract

In the domain of knowledge, there is a rising demand for such a System to provide learning support via a platform which can generate any sort of questions automatically from provided source either (PDF) books or simply any keyword against a user needs to perform a test where STMS serves the purpose. Regarding Keyword operation, the System scraps all the text from Wikipedia and converts it into multiple choice questions. Moreover, it summarizes raw text from Wikipedia and parse the text from provided content to generate Multiple-Choice Questions(MCQs).  The System also finds all the Named Entities and POS (Parts of speech tags) in the content to create relevant questions. The questions include Multiple-Choice Questions(MCQs), Cloze based questions and WH- questions (why, where, when etc.). 
In addition, when users score standard points in the test then they qualify for earning zone where they can earn money ($ Dollars) for scoring points in each test. The Income comes from AdSense applied on the website and other Local ads, Affiliating marketing and advertisements. All in all, the System would help in educational learning by providing helping material in the lacking knowledge areas after analyzing the tests users have performed while the Web-Traffic is the key to Success for monetary benefits.

Cite This Paper

Muhammad Saad, Shanzah Aslam, Warda Yousaf, Moeed Sehnan, Sidra Anwar, Danish Rehman, " Student Testing and Monitoring System (Stms) Using Nlp ", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.9, pp. 26-34, 2019.DOI: 10.5815/ijmecs.2019.09.03

Reference

[1]Baumeister, G. J. (2017). Knowledge Engineering and Software Engineering. 36 German Conference on Artificial Intelligence.

[2]Gopal Sakarkar, S. M. (2012). Intelligent Online e-Learning Systems: A Comparative Study. International Journal of computer Applications, 65(4).

[3]Divate, M., & Salgaonkar, A. (2017). Automatic Question Generation Approaches and Evaluation Techniques. Current Science, 113(09), 1683. doi: 10.18520/cs/v113/i09/1683-1691

[4]Mrs. Subitha Sivakumar, M. V. (2015). A User-Intelligent Adaptive Learning Model for Learning Management System Using Data Mining And Artificial Intelligence. International Journal for Innovative Research in Science & Technology, 1(10)

[5]Padmaja Appalla, V. M. (2017). An efficient educational data mining approach to support e-learning. 24(4).

[6]Draper, S. (2009). Catalytic assessment: understanding how MCQs and EVS can foster deep learning. British Journal Of Educational Technology, 40(2), 285-293. doi: 10.1111/j.1467-8535.2008.00920.x.

[7]Bhattacharya, S. a. (2016). Intelligent e-Learning Systems: An Educational Paradigm Shift. International Journal of Interactive Multimedia and Artificial Intelligence, 4.

[8]Chinaguravaiah Makkena, K. A. (2017). A Study on efficiency of Data Mining Approaches to Online -. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2(4).

[9]D.Suresh, S. (2013). The Impact of E-learning system Using Rank-based Clustering Algorithm (ESURBCA). International Journal of Computer Applications, 83.

[10]Géryk, J. (2015). Using Visual Analytics Tool for Improving Data Comprehension. International conference on Educational Data Mining.

[11]Guohui Xiao, D. C. (2018). Ontology-Based Data Access: A Survey. Proceedings of the twenty-Seventh International Joint Conference on Artificial Intelligence.

[12]Jacopo Amidei, P. P. (2018). Evaluation Methodologies in Automatic Question Generation 2013-2018.

[13]Lauren Fratamico, S. P. (2017). A Visual approach towards knowledge Engineering and Understanding How Students Learn in Complex Environments.

[14]Shabina Dhuria, S. C. (2014). Ontologies for Personalized E-Learning in the Semantic Web. International Journal of Advanced Engineering and Nano Technology, 1(4).

[15]Xindong Wu, H. C. (2015). Knowledge Engineering with Big Data. Intelligent Systems, IEEE.

[16]Zachary A. Pardos, L. H. (2018). Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization.

[17]Dahler, D. (2016). IMPROVING WRITING DESCRIPTIVE TEXT BY USING NLP STRATEGY AT VIII GRADE OF SMPN 11 DURI. Lectura : Jurnal Pendidikan, 7(2). doi: 10.31849/lectura.v7i2.247

[18]Divate, M., & Salgaonkar, A. (2017). Automatic Question Generation Approaches and Evaluation Techniques. Current Science, 113(09), 1683. doi: 10.18520/cs/v113/i09/1683-1691

[19]Pishghadam, R., & Shayesteh, S. (2014). Neuro-linguistic Programming (NLP) for Language Teachers: Revalidation of an NLP Scale. Theory And Practice In Language Studies, 4(10). doi: 10.4304/tpls.4.10.2096-2104