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Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application

Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool...

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Autores principales: Singh, Rishabh, Timbadia, Devansh, Kapoor, Vidhi, Reddy, Rishabh, Churi, Prathamesh, Pimple, Omkar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903027/
https://www.ncbi.nlm.nih.gov/pubmed/33642919
http://dx.doi.org/10.1007/s10639-021-10461-y
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author Singh, Rishabh
Timbadia, Devansh
Kapoor, Vidhi
Reddy, Rishabh
Churi, Prathamesh
Pimple, Omkar
author_facet Singh, Rishabh
Timbadia, Devansh
Kapoor, Vidhi
Reddy, Rishabh
Churi, Prathamesh
Pimple, Omkar
author_sort Singh, Rishabh
collection PubMed
description Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage better results for students. Therefore, it is important to understand each student their capacities and create a different test based on the required difficulty. Students should be graded based on their capabilities. The purpose of the research study is to develop the progressive model with the calibration of difficulty level according to the student capacity. To achieve the goal, a test of 20 python questions was conducted on 120 students with each question having difficulty given by 8 field experts. To verify the model, 5 categories were formed with different difficulty levels which in turn gave satisfactory results. To find a relation between the initial difficulty and the calculative difficulty based on the student response, a correlation test was conducted. After careful analysis of the question difficulty and student responses, it was observed that both are highly dependent on each other wherein the difficulty level of any question can be calculated using incorrect answers. The correlation coefficient obtained between them was 0.9833. Upon collecting the difficulty of the questions and student responses, respective grading could be done using the stated formula. Later on, the progressive model was simulated with five different cases (Best case, above-average case, below average case, the average case, worst case). The model outperformed in all the cases with appropriate difficulty levels. Online Tests have ushered a revolution in the assessment of students but yet they tend to be unpopular in India as the evaluation based on pen-paper approach is preferred. The main reasons for this are difficult to grade everyone at the same level, susceptible to cheating, and transition to open books. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the mobile-based online test and make easy migration from paper-based test to online test.
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spelling pubmed-79030272021-02-24 Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application Singh, Rishabh Timbadia, Devansh Kapoor, Vidhi Reddy, Rishabh Churi, Prathamesh Pimple, Omkar Educ Inf Technol (Dordr) Article Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage better results for students. Therefore, it is important to understand each student their capacities and create a different test based on the required difficulty. Students should be graded based on their capabilities. The purpose of the research study is to develop the progressive model with the calibration of difficulty level according to the student capacity. To achieve the goal, a test of 20 python questions was conducted on 120 students with each question having difficulty given by 8 field experts. To verify the model, 5 categories were formed with different difficulty levels which in turn gave satisfactory results. To find a relation between the initial difficulty and the calculative difficulty based on the student response, a correlation test was conducted. After careful analysis of the question difficulty and student responses, it was observed that both are highly dependent on each other wherein the difficulty level of any question can be calculated using incorrect answers. The correlation coefficient obtained between them was 0.9833. Upon collecting the difficulty of the questions and student responses, respective grading could be done using the stated formula. Later on, the progressive model was simulated with five different cases (Best case, above-average case, below average case, the average case, worst case). The model outperformed in all the cases with appropriate difficulty levels. Online Tests have ushered a revolution in the assessment of students but yet they tend to be unpopular in India as the evaluation based on pen-paper approach is preferred. The main reasons for this are difficult to grade everyone at the same level, susceptible to cheating, and transition to open books. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the mobile-based online test and make easy migration from paper-based test to online test. Springer US 2021-02-24 2021 /pmc/articles/PMC7903027/ /pubmed/33642919 http://dx.doi.org/10.1007/s10639-021-10461-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Singh, Rishabh
Timbadia, Devansh
Kapoor, Vidhi
Reddy, Rishabh
Churi, Prathamesh
Pimple, Omkar
Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application
title Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application
title_full Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application
title_fullStr Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application
title_full_unstemmed Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application
title_short Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application
title_sort question paper generation through progressive model and difficulty calculation on the promexa mobile application
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903027/
https://www.ncbi.nlm.nih.gov/pubmed/33642919
http://dx.doi.org/10.1007/s10639-021-10461-y
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