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Towards development of a system for automatic assessment of the quality of a question paper
- Source :
- Smart Learning Environments, Vol 8, Iss 1, Pp 1-14 (2021)
- Publication Year :
- 2021
- Publisher :
- SpringerOpen, 2021.
-
Abstract
- In this paper, we present a system for automatic evaluation of the quality of a question paper. Question paper plays a major role in educational assessment. The quality of a question paper is crucial to fulfilling the purpose of the assessment. In many education sectors, question papers are prepared manually. A prior analysis of a question paper might help in finding the errors in the question paper, and better achieving the goals of the assessment. In this experiment, we focus on higher education in the technical domain. First, we conducted a student survey to identify the key factors that affect the quality of a question paper. The top factors we identified are question relevance, question difficulty, and time requirement. We explored the strategies to handle these factors and implemented them. We employ various concepts and techniques for the implementation. The system finally assigns a numerical quality score against these factors. The system is evaluated using a set of question papers collected from various sources. The experimental results show that the proposed system is quite promising.
- Subjects :
- Question assessment
Higher education
Computer science
media_common.quotation_subject
Question difficulty
Question paper quality
02 engineering and technology
computer.software_genre
Education
Domain (software engineering)
Development (topology)
Educational assessment
0202 electrical engineering, electronic engineering, information engineering
Question relevance
Quality (business)
Relevance (information retrieval)
Set (psychology)
media_common
lcsh:LC8-6691
lcsh:Special aspects of education
business.industry
05 social sciences
050301 education
Computer Science Applications
Risk analysis (engineering)
Quality Score
020201 artificial intelligence & image processing
business
0503 education
computer
Subjects
Details
- Language :
- English
- ISSN :
- 21967091
- Volume :
- 8
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Smart Learning Environments
- Accession number :
- edsair.doi.dedup.....71e01e2a5ed251b6cb7be2c4ed8f7fa8