Automatic question generation and answer assessment: a survey

Abstract

Learning through the internet becomes popular that facilitates learners to learn anything, anytime, anywhere from the web resources. Assessment is most important in any learning system. An assessment system can find the self-learning gaps of learners and improve the progress of learning. The manual question generation takes much time and labor. Therefore, automatic question generation from learning resources is the primary task of an automated assessment system. This paper presents a survey of automatic question generation and assessment strategies from textual and pictorial learning resources. The purpose of this survey is to summarize the state-of-the-art techniques for generating questions and evaluating their answers automatically.

Multi-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism

“…• BLEU-N: A method that measures the precision based on the n-gram overlap between generated questions and references [13]. We compute BLEU- [1,2,3,4] in this experiment. • ROUGE-L: ROUGE-L is a method that measures precision and recall on the longest common subsequence (LCS) overlap between system outputs and references [38].…”

“…For example, QG can be used to augment a question answering (QA) dataset that is expensive to obtain, construct a synthetic QA dataset and facilitate a dialogue system by controlling conversation flow through generating questions. Besides, QG can be used for an educational purpose as it can improve and enhance children's comprehension and retention by proposing questions based on textbook passages [1][2][3][4]. Especially, in the QG research community, multi-hop QG has recently been the focus of its potential applications in understanding complex human questions generated through the compositionality of questions, and the goal of multi-hop QG is to generate complex questions that require evidence across multiple passages to be answered [5].…”

“…Results of different QG models on the HotpotQA testset, the evaluation metrics are ROUGE-L, METEOR and BLEU-[1,2,3,4]. A score in bold indicates the model performs best according to that metric.…”

An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

“…The natural question generation is a growing research domain to generate questions from text input. In literature AI, specifically natural language processing (NLP) algorithms are extensively used in AQG [4]. In particular, the advancement of deep learning techniques in NLP [4] and readily available natural language dataset (e.g, SquAD [14]) expanded the research potential in the AQG area.…”

“…One key innovation of AI in the context of assessment process is through Automatic Question Generation (AQG) [4]. This is the process of creating questions automatically based on the context of the content being delivered to assess what the learner knows and understands [4]. .…”