Automating question generation from educational text

• URL: http://arxiv.org/abs/2309.15004v1
• Date: Tue, 26 Sep 2023 15:18:44 GMT
• Title: Automating question generation from educational text
• Authors: Ayan Kumar Bhowmick and Ashish Jagmohan and Aditya Vempaty and Prasenjit Dey and Leigh Hall and Jeremy Hartman and Ravi Kokku and Hema Maheshwari
• Abstract summary: The use of question-based activities (QBAs) is wide-spread in education, forming an integral part of the learning and assessment process. We design and evaluate an automated question generation tool for formative and summative assessment in schools.
• Score: 1.9325905076281444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.

Related papers

• The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models [0.0]
  Large language models (LLMs) and generative AI have revolutionized natural language processing (NLP) This chapter explores the transformative potential of LLMs in automated question generation and answer assessment.
  arXiv  Detail & Related papers  (2024-10-12T15:54:53Z)
• Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning [43.83422798569986]
  Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and reliable form of assessment. To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers. We propose a simple, in-context learning-based solution for automated distractor and corresponding feedback message generation.
  arXiv  Detail & Related papers  (2023-08-07T01:03:04Z)
• An Empirical Comparison of LM-based Question and Answer Generation Methods [79.31199020420827]
  Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches.
  arXiv  Detail & Related papers  (2023-05-26T14:59:53Z)
• Learning to Reuse Distractors to support Multiple Choice Question Generation in Education [19.408786425460498]
  This paper studies how a large existing set of manually created answers and distractors can be leveraged to help teachers in creating new multiple choice questions (MCQs) We built several data-driven models based on context-aware question and distractor representations, and compared them with static feature-based models. Both automatic and human evaluations indicate that context-aware models consistently outperform a static feature-based approach.
  arXiv  Detail & Related papers  (2022-10-25T12:48:56Z)
• Multiple-Choice Question Generation: Towards an Automated Assessment Framework [0.0]
  transformer-based pretrained language models have demonstrated the ability to produce appropriate questions from a context paragraph. We focus on a fully automated multiple-choice question generation (MCQG) system where both the question and possible answers must be generated from the context paragraph.
  arXiv  Detail & Related papers  (2022-09-23T19:51:46Z)
• Automatic Short Math Answer Grading via In-context Meta-learning [2.0263791972068628]
  We study the problem of automatic short answer grading for students' responses to math questions. We use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model. Second, we use an in-context learning approach that provides scoring examples as input to the language model.
  arXiv  Detail & Related papers  (2022-05-30T16:26:02Z)
• Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation [87.34509878569916]
  This paper focuses on the use case of helping teachers automate the generation of reading comprehension quizzes. In our study, teachers building a quiz receive question suggestions, which they can either accept or refuse with a reason.
  arXiv  Detail & Related papers  (2022-05-03T18:59:03Z)
• Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice [59.705062519344]
  One proposed solution involves AI-enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog models trained for virtual job interviews.
  arXiv  Detail & Related papers  (2022-03-29T10:25:13Z)
• Learning from Lexical Perturbations for Consistent Visual Question Answering [78.21912474223926]
  Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. We propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations. We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations.
  arXiv  Detail & Related papers  (2020-11-26T17:38:03Z)
• Personalized Multimodal Feedback Generation in Education [50.95346877192268]
  The automatic evaluation for school assignments is an important application of AI in the education field. We propose a novel Personalized Multimodal Feedback Generation Network (PMFGN) armed with a modality gate mechanism and a personalized bias mechanism. Our model significantly outperforms several baselines by generating more accurate and diverse feedback.
  arXiv  Detail & Related papers  (2020-10-31T05:26:49Z)
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms [50.19997675066203]
  We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
  arXiv  Detail & Related papers  (2020-05-16T02:17:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.