Authors: Asst. Prof. Mrs. M. M. Phadatare, Tejas Chakankar, Tejas Shinkar, Shreyash Waghdhare, Srushti Waichal
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Automated question paper generator using Natural Language Processing (NLP) is a system that leverages the capabilities of machine learning and artificial intelligence to generate question papers automatically. This system uses NLP techniques to analyse a given text, identify the important concepts, and generate questions based on those concepts. The generated questions are diverse in nature and follow a predefined format. This system eliminates the need for manual question paper generation, which can be time-consuming and error-prone. The proposed system can be a useful tool for educators to quickly generate question papers and assess student performance. This system works by analysing the topic, subject, and complexity level of the course material to generate a set of questions that assess the students\' understanding. The system employs NLP techniques to process natural language data and extract relevant information for question generation. By automating the process of question paper generation, this system can save time and effort for educators while ensuring the quality of the assessment. This abstract explores the features, benefits, and potential applications of an automated question paper generator using NLP. The system also aims to improve the quality of assessments by ensuring that the questions are well-structured, varied, and aligned with the learning outcomes. Additionally, the automated question paper generator can adapt to the needs of individual learners, allowing for a more personalized and effective learning experience.
The "Automated Question Generator" has been developed This software program is supported to take away and, in a few cases, reduce the hardships confronted by way of this present system. to override the issues winning in the training guide system.
The utility is reduced as an awful lot as viable to avoid mistakes at the same time as coming into the facts. No formal information is needed for the user to use this system. accordingly, utilizing this all proves its miles are person-friendly. computerized question Paper Generator, as defined above, can result in error-free, relaxed, dependable, and rapid management structures. it can help the consumer to pay attention to their other sports instead of concentrating on record maintenance.
Those automatic systems help us with many price and time-efficient answers. within the training area, the academicians are majorly dependent on their personnel for producing questions for numerous examinations. however, numerous successful tries were made for the development of automated evaluation structures. The paintings executed within the field of AQG, focus basically on the technology of easy conceptual questions, this may no longer show to be very green for judging the scholars' learning. So, on the way to efficiently investigate the students, step one is to design a question paper that covers all of the necessary elements to check his/her understanding. Generally, the three major components of Question Generation are input pre-processing, sentence selection, and question formation. The input text is filtered by removing unnecessary words and punctuations that do not contribute to the meaning of the sentence. The sentences or phrases from which questions can be formed are segregated from the remaining text. These are mapped to the type of question (what, where, when, etc.) that can be formulated with the selected sentence, followed by the final step of framing a grammatically sound question.
There are many changes being made now in various fields that tend to move from manual systems to automated systems. These automatic systems help us with less cost and time-efficient solutions. In the education field, the academicians are majorly dependent on their own for generating questions for various examinations.
II. LITERATURE SURVEY
There have been several studies and research papers on the use of Natural Language Processing (NLP) in automated question paper generation. Here are some of the significant contributions in the literature:
Overall, these studies demonstrate the potential of NLP in automated question paper generation and provide insights into the development of effective systems for educational purposes.
The methodology for an automated question paper generator using Natural Language Processing (NLP) typically involves the following steps:
4. Question Generation: The extracted features are then used to generate questions. The question-generation process could involve different techniques such as template-based, rule-based, or machine learning-based approaches. In the template-based approach, predefined question templates are filled with the extracted features to generate questions. In the rule-based approach, a set of rules is defined to generate questions based on the extracted features. Machine learning-based approaches involve training models on a labelled dataset to generate questions automatically.
5. Evaluation: The generated questions are evaluated to ensure that they are of high quality and align with the learning outcomes. The evaluation could involve measures such as grammaticality, coherence, and relevance.
6. Answer Construction: The final step involves constructing answers by selecting a set of questions generated in step 4. The answers construction process could involve techniques such as random selection or intelligent selection based on the learning outcomes and difficulty level.
7. Quality Assessment: The generated questions are evaluated for their quality, relevance, and difficulty level using automated techniques and expert judgment.
8. Personalization: The system can be personalized based on individual learner needs by adapting the difficulty level and type of questions generated according to their performance and progress.
9. Deployment: The final step is to deploy the system for practical use, either as a standalone application or integrated with a learning management system.
Overall, the methodology for an automated question paper generator using NLP involves collecting text data, pre-processing the data, extracting features, generating questions, evaluating the questions, and constructing a question paper. Throughout the development process, it is crucial to ensure the accuracy and validity of the generated questions, as well as the usability and scalability of the system. Evaluating the system on different datasets and gathering feedback from educators and learners can help to refine the methodology and improve the system's performance.
V. FUTURE SCOPE
The future scope for an automated question paper generator using Natural Language Processing (NLP) is promising, with several potential areas for improvement and expansion. Some of the future scope for such a system includes:
Overall, the future scope for an automated question paper generator using NLP is significant, with the potential to improve the educational process's efficiency and effectiveness while providing personalized learning experiences for learners.
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Copyright © 2023 Asst. Prof. Mrs. M. M. Phadatare, Tejas Chakankar, Tejas Shinkar, Shreyash Waghdhare, Srushti Waichal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.