The document presents an overview of automatic question paper generators (AQPG). It discusses how AQPGs work by gathering questions from banks and generating papers based on algorithms that consider factors like difficulty levels, topic weights, and syllabus coverage. The document reviews various algorithms used in AQPGs, such as randomized algorithms and artificial intelligence techniques like genetic algorithms and natural language processing. It also provides a literature survey summarizing over 20 research papers on AQPGs and the algorithms they employed. Finally, it concludes that AQPGs can help standardize the question paper generation process and reduce the workload for educators.

1. Presented by
A . Vijaya Lakshmi,
Department of Computer Science ,Pondicherry University
Co-author
Dr. K. Suresh Joseph,
Department of Computer Science ,Pondicherry University
1
International Conference on
Technology Enabled Online & Distance Learning for Education: In the Context of NEP 2020
27th & 28th October 2020
Organized by
Directorate of Distance Education & Department of Computer Science
PONDICHERRY UNIVERSITY
03-11-2022

2. Agenda
 Introduction
 Why question Paper Generator?
 How It works!
 Important Design Factors for AQPG
 Method Used
 Randomised Algorithm
 Artificial Intelligence Based Techniques
 Literature Survey
 Conclusion
 References
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3. Introduction
• Examination plays a crucial role in the online education system.
• It relies heavily on the production of test questions.
• Automatic Question Paper Generator is special software which is
useful to schools, Institutes, publishers and test paper setters who want
to have a huge database of questions and generate test papers
frequently with ease [1].
• It mainly deals with the gathering, sorting and administration of a
large amount of questions about different levels of toughness .
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4. Why question Paper Generator?
• A quality question paper is a real combination of questions supervised by
varied criteria such as difficulty level, distribution of marks across the
question paper in form of paper pattern and the type of examination.
• The recent dispute regarding Class X and XII question papers has raised
quite a question and the need for a proper system for question paper setters
in the field of education [2].
• The traditional setting of the paper process needs revamping and utilizing
the emerging trends in technology to stay futuristic. So, changes from a
manual process to an automated one are necessary for question paper
setting.
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5. How It works!
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Fig. 1. Building Blocks of AQPG
ADMIN
EXAMINER
Question
Paper
Template
Blooms
Taxonomy
QUESTION
BANK
QUESTION PAPER
GENERATOR
ALGORITHM
Insert Questions,
Taxonomy, Templates,
Maintains System
Initiate QP Generation
Question Paper
5

6. Important Design Factors for AQPG
• Distribution of cognitive levels weights based on Blooms taxonomy.
• Distribution of topic weights.
• Toughness Level of Question.
• Syllabus Coverage [3]-[5] .
• Course Outcome
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7. Algorithms used
• Many researches being conducted on automated exam questions
generator. Automated exam questions generator can be categorized
based on the algorithms that are utilized in order to generate the
questions.
• Randomised Algorithms Techniques
• AI based Algorithms
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8. Algorithms used(cont.)
Randomised Algorithms Techniques
• Randomized algorithms use random numbers to
solve problems in their logic [6] .
• The randomized Algorithm in AQPG is worked in
two ways.
• Randomized algorithms ensure question paper
without duplication and randomness
AI based Algorithms
• Researchers started to adopt artificial intelligence in
their researches to improve on the performance of
Automated Exam Questions Generator and the
quality of exam questions
• It incorporate all the aspects of the syllabus and
made fully customizable
• Ant colony algorithm, simulated annealing
algorithm ,Genetic Algorithm ,Natural Language
Processing , Ontology [7] .
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9. Literature survey
Ref. no Year Author Algorithm /Method Features
[8] 2013
Dimple V. Paul, Jyoti D.
Pawar
Evolutionary Multi-Objective
Optimisation Algorithm (EMOOA)
Topic weightage is used for calculation in the initial population, and
multiple different question paper template for the same examination is
generated by multi-objective optimisation
[9] 2013 Vaibhav M. Kale Multi constraint Algorithm
Qp format, difficulty level, and syllabus coverage are considered for QP
generation. The system is modelled as a multi-constraint optimisation
problem
[10] 2014
Ibrahim Teo, Noor
Hasimah
Abu Bakar2
Genetic Algorithm
text matching is used to set questions depending on Bloom's framework,
and the author used a Genetic algorithm for producing questions with the
optimal combination of question sequence
[11] 2014 Dimple V. Paul
Multi-objective Differential Evolution
Approach (MDEA)
MDEA executes a global parallel search by applying its operators such as
mutation, cross over and selection to produce optimal solutions
[12] 2014 Naik, Kapil Sule, S. Randomisation Algorithm
The author developed a desktop and browser-based application system to
produce qp using a shuffling randomised algorithm without duplication
and repetition.
[13] 2014 Paul, Dimple V. Evolutionary Algorithm
AQPG is modelled as a multi-constraint optimisation problem and
proposed a new modified evolutionary algorithm to produce qP with
randomness.
[14] 2017
Kiran, Fenil
Gopal, Hital2 Randomisation Algorithm
This desktop-based software produces a unique set of question papers
based on a constraints table, leading to precise output with a minimum
probability of errors.
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10. Literature survey
Ref. no Year Author Algorithm /Method Features
[15] 2018
Naglot, Deepali
Gaikwad, Seema
Keyword-based Shuffling Algorithm
The author employed a logical keyword-based shuffling approach to
producing a question paper without duplication. Keyword is used for
checking the repetition of the question
[16] 2018 Song, Wanli Genetic Algorithm
The author developed a framework that dynamically produced composite
assessment questions depending on the difficulty of the subject, curriculum
coverage, and levels of the toughness of questions.
[17] 2017
Rahim, Tengku
Nurulhuda Tengku Abd
Genetic Algorithm
Generate Qp depending on Bloom's taxonomy and cognitive level of
students utilising a genetic algorithm
[18] 2018
Aanchal Jawere, Anchal
Soni
Natural language processing
It automatically generates questions from documents. The burden of
manual question insertion in the Question bank is eliminated
[19] 2020
Mohammed, Manal
Omar, Nazlia2
Support Vector Machine (SVM), K-
Nearest Neighbour and Logistic
Regression, and
TFPOS-IDF and word2vec features are employed to extract essential
words from documents to generate questions
[20] 2022
Das, Bidyut Majumder,
Mukta
natural language processing
The author produced QP by extracting critical concepts from the
curriculum, and later depending on those concepts, various kinds of
subjective questions were generated. Finally, a multi-criteria decision-
making strategy is employed to evaluate student response.
[21] 2022
Kusuma, Selvia Ferdiana
Siahaan
knowledge ontology
The author Concentrated on creating ontology and template generation
models, which can able applicable across domains. Integrating sentence
and taxonomy ontology to produce domain independent question
framework
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11. Conclusion
• The conventional method for generating exam questions is tedious and time-consuming.
• It is difficult for educators to cover every aspect of the course content and prevent
redundancy in future assessments. And there are no defined methodologies.
• Thus the quality of the question paper and classification of question toughness levels is
entirely dependent on the competence ,knowledge and perspectives of the particular
teacher.
• This paper compares and contrasts various algorithms employed in question paper
generation; this work can be a guide for new researchers in the field of automatic question
generation.
• Future Research direction includes employing AI techniques to reclassify the category of
the question toughness based on the student-answerable ratio of questions, which in turn
will provide an adaptable and robust platform to generate question papers for various
student levels.
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12. References
[1] “Attributes of a good question paper — deccan herald.”
[2] “Cbse term i controversy: How does the central board set questionpapers? — the financial express.”
[3] L. W. Anderson and D. R. Krathwohl, A taxonomy for learning, teaching, and assessing : a revision of Bloom’s taxonomy of educational objectives. Longman, 2001.
[4] “Taxonomy of educational objectives - google books.”
[5] D. V. Paul, S. B. Naik, P. Rane, and J. Pawar, “Use of an evolutionary approach for question paper template generation,” undefined, 2012.
[6] “Randomized algorithms - google books.”
[7] A. Darwish, “Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications,” Future Computing and Informatics
Journal, vol. 3, pp. 231–246, 12 2018.
[8] D. V. Paul and J. D. Pawar, “Pareto-optimal solutions for question paper template generation,” Proceedings of the 2013 International Conference on Advances in
Computing, Communications and Informatics, ICACCI 2013, pp. 747–751, 2013.
[9] V. M. Kale and A. W. Kiwelekar, “An algorithm for question paper template generation in question paper generation system,” 2013 The International Conference on
Technological Advances in Electrical, Electronics and Computer Engineering, TAEECE 2013, pp. 256–261, 2013.
[10] N. H. I. Teo, N. A. Bakar, and M. R. A. Rashid, “Representing examination question knowledge into genetic algorithm,” IEEE Global Engineering Education
Conference, EDUCON, pp. 900–904, 2014.
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13. References(cont.)..
[11] D. V. Paul and J. D. Pawar, “A multi-objective differential evolution approach for the question selection problem,” 5th International Conference on the
Applications of Digital Information and Web Technologies, ICADIWT 2014, pp. 219–225, 2014.
[12] K. Naik, S. Sule, S. Jadhav, and S. Pandey, “Automatic question paper generation system using randomization algorithm,” International Journal of Engineering
and Technical Research (IJETR), 2014.
[13] D. V. Paul, S. B. Naik, and J. D. Pawar, “An evolutionary approach for question selection from a question bank,” International Journal of ICT Research and
Development in Africa, vol. 4, pp. 61–75, 2014.
[14] F. Kiran, H. Gopal, and A. Dalvi, “Automatic question paper generator system,” International Journal of Computer Applications, vol. 166, pp. 42–47, 2017.
[15] D. Naglot, S. Gaikwad, P. Gaikwad, A. Salvi, and S. Mutyal, “Keyword based shuffling algorithm for question paper generator,” International Journal of Computer
Applications, vol. 179, pp. 36–40, 2018.
[16] W. Song, “Online test paper composition based on genetic algorithm,” springer series, vol. 160, pp. 158–161, 2018.
[17] T. N. T. A. Rahim, Z. A. Aziz, R. H. A. Rauf, and N. Shamsudin, “Automated exam question generator using genetic algorithm,” 2017 IEEE Conference on e-
Learning, e-Management and e-Services, IC3e 2017, pp. 12–17, 2018.
[18] A. Jawere, A. Soni, and N. Tejra, “Implementation of automatic question paper generator system,” The International Journal of Creative Research Thoughts
(IJCRT), pp. 1–7, 2017.
[19] M. Mohammedid and N. Omar, “Question classification based on bloom’s taxonomy cognitive domain using modified tf-idf and word2vec,” PLOS ONE, vol. 15,
p. e0230442, 2020.
[20] B. Das, M. Majumder, A. A. Sekh, and S. Phadikar, “Automatic question generation and answer assessment for subjective examination,” Cognitive Systems
Research, vol. 72, pp. 14–22, 3 2022.
[21] S. F. Kusuma, D. O. Siahaan, and C. Fatichah, “Automatic question generation with various difficulty levels based on knowledge ontology using a query
template,” Knowledge-Based Systems, vol. 249, p. 108906, 8 2022.
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