Automating question generation from educational text

IRJET, 2022

Multiple Choice Question (MCQ) is a versatile question type where respondents select one correct answer from the other choices. Generation of such MCQs along with different implausible options is tedious and time-consuming work. Existing automated question generation systems reduce manual efforts to some extent as they were at only sentence level information. By looking widespread usage of MCQs from school exams to competitive exams, automated generation of more reliable questions is required. The proposed system generated questions based on an Extractive summary using the BERT model which holds paragraphlevel context. BLEU score is used as an evaluation matrix to demonstrate the quality of model-generated questions across the human generated questions. The achieved BLEU score is 0.66. Though the proposed system focuses on Fill-inthe-blank questions scope can be extended to various different types of objective questions.

Question Generation is a field of research that has been growing in popularity through the years, as educators seek to find ways to make test generation a lot easier with the use of Machine Learning and Artificial Intelligence. In this paper we research how automated end-to-end question generation that utilizes transformers will generate an understandable question by making an inference on sample paragraphs. The model is trained in an end-to-end approach where the model focuses on the context paragraph to understand the sentences and generate a question where the answer is not directly in the context paragraph. An inference approach is proposed to find hidden sentences by using discourse analysis and paraphrasing techniques based on fine tuning transformers to be fed into a model that can generate a question from the hidden or new sentences. The Stanford parser was also utilized to get a clearer view of the parts of speech focusing particularly on the verbs, pronouns, and other key entities in the sentence. Experiments on the context paragraphs was conducted on the SQuAD 1.1 dataset where we attempted to transform the original input paragraphs using the inference rule. After transforming all sentences, we then fed these new shortened paragraphs to the transformer model to generate a deeper level understanding question.

2005

In the REAP system, users are automatically provided with texts to read targeted to their individual reading levels. To find appropriate texts, the user's vocabulary knowledge must be assessed. We describe an approach to automatically generating questions for vocabulary assessment. Traditionally, these assessments have been hand-written. Using data from WordNet, we generate 6 types of vocabulary questions. They can have several forms, including wordbank and multiple-choice. We present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject performance on independently developed humanwritten questions. In addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge.

arXiv (Cornell University), 2023

The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our extensive experiments demonstrate that EduQG can produce superior educational questions by further pre-training and fine-tuning a pre-trained language model on the scientific text and science question data.

SSRN Electronic Journal, 2019

Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers while getting questions from external sources like assessment books or question bank might not be relevant to content studied by students. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content, which can be text. Automatic question generation (QG) is a very important yet challenging problem in NLP. It is defined as the task of generating syntactically sound, semantically correct and relevant questions from several input formats like text, a structured database or a knowledge base. Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. AQG has the got the immense attention from researchers in a field of computational linguistics. The review paper focuses on the recants ongoing research on NLP for generating automatic questions from the text through various methods. http://ssrn.com/link/ICAESMT-2019.html=xyz Information Systems &eBusiness Network (ISN) Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. Despite its usefulness, manually creating meaningful and relevant questions is a timeconsuming and challenging task. For example, while evaluating students on reading comprehension, it is tedious for a teacher to manually create questions, find answers to those questions, and thereafter evaluate answers. Traditional approaches have either used a linguistically motivated set of transformation rules for transforming a given sentence into a question or a set of manually created templates with slot fillers to generate questions. Recently, neural network-based techniques such as sequence-to-sequence (Seq2Seq) learning have achieved remarkable success in various NLP tasks, including question generation. A modern approach that is deep learning is used to generates question. The approach proposed by author explore a straightforward task of question generation only from a triplet of subject, relation and object. Sequence-to-sequence prototype with attention for question generation from passages. The proposed algorithm generates questions and answers from corpus using pointer networks.

2012

Humans are not often very skilled in asking good questions because of their inconsistent mind in certain situations. Thus, Question Generation (QG) and Question Answering (QA) became the two major challenges for the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System, and Information Retrieval (IR) communities, recently. In this thesis, we consider a form of Sentence-to-Question generation task where given a sentence as input, the QG system would generate a set of questions for which the sentence contains, implies, or needs answers. Since the given sentence may be a complex sentence, our system generates elementary sentences from the input complex sentences using a syntactic parser. A Part of Speech (POS) tagger and a Named Entity Recognizer (NER) are used to encode necessary information. Based on the subject, verb, object and preposition information, sentences are classified in order to determine the type of questions to be generated. We conduct extensive experiments on the TREC-2007 (Question Answering Track) dataset. The scenario for the main task in the TREC-2007 QA track was that an adult, native speaker of English is looking for information about a target of interest. Using the given target, we filter out the important sentences from the large sentence pool and generate possible questions from them. Once we generate all the questions from the sentences, we perform a recall-based evaluation. That is, we count the overlap of our system generated questions with the given questions in the TREC dataset. For a topic, we get a recall 1.0 if all the given TREC questions are

Computer Applications in Engineering Education, 2019

Knowledge acquisition is the prime objective of a learner from an educational system and evaluating the learner's knowledge is the eventual goal of an examination process. This paper introduces a system which is able to produce fill-in-the-blank questions to test the knowledge of a learner that he or she has accumulated after reading a course material. The question generation task is subdivided into three modules: sentence selection, answer-key identification and question formation along with distractors generation. The sentence is selected using a coarse-grain part-of-speech tagset. The answerkey is extracted by identifying topic-word in the sentence and question is formed by omitting this topic-word from the sentence. This paper also highlights an efficient corpus-based distractors generation technique to produce multiple-choice fill-in-the-blank test items.

2021 IEEE International Conference on Computing (ICOCO), 2021

Designing and setting assessment questions for examinations is always a necessary task for educators. In this article, we identify the research gaps in (semi-) automatically generating questions by evaluating all the available approaches developed thus far. We then propose a framework that puts together previous approaches and suggests ways to fill in their gaps. One hundred and thirteen pieces of literature relevant to question generation approaches have been reviewed and compared. For each of the approaches, the uniqueness of the techniques is explained. The PolyAQG Framework is presented with an explanation of how it would contribute to the solution of the problem, by improving the variety of the questions, increasing the total number of possible choices of question selections, as well as providing a better quality of questions. Apart from the framework, another novelty in this work is the innovative way a domain ontology is used to generate a wider variety of questions.

ArXiv, 2021

In education, open-ended quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answeraware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.

ArXiv, 2022

We conduct a feasibility study into the applica-bility of answer-agnostic question generation models to textbook passages. We show that a significant portion of errors in such systems arise from asking irrelevant or uninterpretable questions and that such errors can be amelio-rated by providing summarized input. We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33% → 83%) as determined by expert annotators. We also find that, in the absence of human-written summaries, automatic summarization can serve as a good middle ground.