Automatic True/False Question Generation for Educational Purpose (2022)

Abstract: In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate a large number of questions. However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content. In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test. TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy. Human evaluation shows that our approach can generate high-quality and valuable true/false questions. In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks.

## introduction for educational purposes, questioning not only assesses the acquisition of knowledge, but also reinforces the engagement and critical thinking of learners during effective teaching, which in turn enables learners to clearly guide their learning efforts and enhance their skills @xcite . with the ever-growing educational content on the internet and the increasing popularity of online tutoring applications during the covid-19 pandemic, an automatic question creation process becomes a key technique to reduce the efforts in manually constructing questions and facilitate adaptive learning. ## tf-qg model given a passage as reading material, tf-qg aims to generate t/f questions to test learners' understanding of the passage. the overall architecture is shown in figure 1 . the passage is first preprocessed to obtain the basic syntactic and semantic information (section 2.1). then, two unsupervised frameworks including the template-based framework (section 2.2) and the generative framework (section 2.3) are applied to generate t/f questions targeting the question-worthy contents in the passage. the question-worthy contents are selected according to our designed templates/protocols in the two frameworks which will be described in the respective sections. ## conclusion in this paper, we propose an automatic true/false question generation approach, which provides a feasible scheme for large-scale generation of educational content. two unsupervised frameworks including template-based framework and generative framework are proposed to select questionworthy contents from the passage and generate high-quality questions. the novel masking-andinfilling strategy enables our model to generate more flexible and complicated true/false questions. in future work, we will focus on how to design templates and mask selection protocols to match with pedagogically valuable test points proposed by domain experts. in addition, we will perform controlled lab or online studies to measure students' learning gains after studying the content generated by tf-qg. furthermore, we expect to deploy the proposed approach on real educational platforms, including an interactive language learning and assessment system (for students), and a question generation assistance system (for teachers), to measure how much the approach could reduce the workload of educators in practical application scenarios.