An Automatic Question Generation System Using Rule-Based Approach in Bloom’s Taxonomy

Deena, G.; Raja, Kothandaraman; Kannan, Kaliyan

doi:10.2174/2213275912666191113143335

Cited by 4 publications

(2 citation statements)

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“…Each time before taking the course, the learners are advised to take up the pre-requisite test to know their basic knowledge in the subject. Using Bloom's taxonomy, preassessment questions are generated from low order to high order thinking [41]. In traditional E-Learning, assessment questions were framed by a teacher who takes much time for it.…”

Section: Assessment Modelmentioning

confidence: 99%

Designing an Automated Intelligent e-Learning System to Enhance the Knowledge using Machine Learning Techniques

Deena¹,

Raja²

2019

IJACSA

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The modern digital world requires its users to learn continuously in order to enhance their knowledge in the working environment and the academic sector. This kind of learning is significantly facilitated by the E-Learning platform, which is better than the traditional methods. As E-Learning offers benefits like time and space independence, many learners have made it their choice. However, since an abundant of E-Learning courses are available on websites, learners are confused as to which is the right one to choose. This paper proposes an Automated Intelligent Learning (AIL) methodology which covers the entire Teaching-Learning Process (TLP) to overcome this issue. It enables the selection of suitable topics and framing an appropriate course syllabus and assessment questions for the users. In it, the learner satisfies topic selection based on Bloom's taxonomy. This enables high-quality knowledge outcomes in the learner. The subject curriculum is framed by using Hierarchical clustering techniques. This helps the user to fix suitable topics and conveniently generate questions using machine learning techniques. The proposed methodology was evaluated by carrying out post and pre-assessment tests on undergraduate students from computer science courses. The performance analysis of the proposed methodology was compared with that of the existing methodology. It was observed that the proposed methodology is effective in applying the topic selection hierarchical method to make a perfect syllabus for the course, and assessment questions. Besides, it was found to enable the learner to learn without any confusion or distraction.

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Section: Assessment Modelmentioning

confidence: 99%

Designing an Automated Intelligent e-Learning System to Enhance the Knowledge using Machine Learning Techniques

Deena¹,

Raja²

2019

IJACSA

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“…However, the notion of quality in question generation raises another concern. Quality can be measured based on relevance, difficulty level, clarity, and the type of cognitive skills they target, such as recall, analysis, or evaluation (Gnanasekaran et al, 2021;Sen Gupta et al, 2019;Xie et al, 2020). Developing an assessment model incorporating these factors would be necessary for a successful automatic question-generation system.…”

Section: Introductionmentioning

confidence: 99%

Generative Agents to support students learning progress

Schacht,

Kamath Barkur,

Lanquillon

2023

Libro De Actas.

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Ongoing assessments in a course are crucial for tracking student performance and progress. However, generating and evaluating tests for each lesson and student can be time-consuming. Existing models for generating and evaluating question-answer pairs have had limited success. In recent years, large language models (LLMs) have become available as a service, offering more intelligent answering and evaluation capabilities. This research aims to leverage LLMs for generating questions, model answers, and evaluations while providing valuable feedback to students and decentralizing the dependency on faculty.We finetune existing LLMs and employ prompt engineering to direct the model toward specific tasks using different generative agents. One agent generates questions, another generates answers, and the third takes the human answers to evaluate and ensure the quality. Human evaluation is conducted through focus group analysis, and student progress and faculty feedback are tracked. Results demonstrate the potential of LLMs to provide automatic feedback and learning progress tracking for both students and faculty.In conclusion, this paper demonstrates the versatility of LLMs for various learning tasks, including question generation, model answer generation, and evaluation, all while providing personalized feedback to students. By identifying and addressing knowledge gaps, LLMs can support continuous evaluation and help students improve their understanding before semester exams. Furthermore, knowledge gaps from students identified by the agent can be highlighted and addressed through additional classes or support materials, potentially generated by the same model, leading to a more personalized learning experience.

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Automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models

Velmurugan,

Pandiarajan,

Ravi

et al. 2024

Proceedings of International Conference on Recent Trends in Computing

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An Automatic Question Generation System Using Rule-Based Approach in Bloom’s Taxonomy

Cited by 4 publications

References 0 publications

Designing an Automated Intelligent e-Learning System to Enhance the Knowledge using Machine Learning Techniques

Designing an Automated Intelligent e-Learning System to Enhance the Knowledge using Machine Learning Techniques

Generative Agents to support students learning progress

Automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models

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