Mukta Majumder

Bio: Mukta Majumder is an academic researcher from University of North Bengal. The author has contributed to research in topics: Biochip & Computer science. The author has an hindex of 6, co-authored 20 publications receiving 102 citations. Previous affiliations of Mukta Majumder include Birla Institute of Technology, Mesra & Vidyasagar University.

A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection

Abstract: Multiple Choice Question (MCQ) plays a major role in educational assessment as well as in active learning. In this paper we present a system that generates MCQs automatically using a sports domain text as input. All the sentences in a text are not capable of generating MCQs; the first step of the system is to select the informative sentences. We propose a novel technique to select informative sentences by using topic modeling and parse structure similarity. The parse structure similarity is computed between the parse structure of an input sentence and a set of reference parse structures. In order to compile the reference set we use a number of existing MCQs collected from the web. Keyword selection is done with the help of occurrence of domain specific word and named entity word in the sentence. Distractors are generated using a set of rules and name dictionary. Experimental results demonstrate that the proposed technique is quite accurate.

A Novel System for Generating Simple Sentences from Complex and Compound Sentences

Abstract: In the field of natural language processing, simple sentence has a great importance; especially for multiple choice question generation, automatic text summarization, opinion mining, machine translation and information retrieval etc. Most of these tasks use simple sentences and include a sentence simplification module as pre-processing or post-processing task. But dedicated tasks for sentence simplification are hardly found. Here we have proposed a novel system for generating simple sentences from complex and compound sentences. Our proposed system is an initiative for simplifying sentence by converting complex and compound sentences into simple ones. Along with this the system classifies the simple sentences of an input corpus from other types of sentences. To generate simple sentences from complex and compound sentences we have proposed a novel algorithm which takes the dependency parsing of the input text and produce simple sentences as output. The experimental result demonstrates that the proposed technique is a promising one.

Automatic selection of informative sentences: The sentences that can generate multiple choice questions

Abstract: Traditional education cannot meet the expectation and requirement of a Smart City; it require more advance forms like active learning, ICT education etc Multiple choice questions (MCQs) play an important role in educational assessment and active learning which has a key role in Smart City education MCQs are effective to assess the understanding of well-defined concepts A fraction of all the sentences of a text contain well-defined concepts or information that can be asked as a MCQ These informative sentences are required to be identified first for preparing multiple choice questions manually or automatically In this paper we propose a technique for automatic identification of such informative sentences that can act as the basis of MCQ The technique is based on parse structure similarity A reference set of parse structures is compiled with the help of existing MCQs The parse structure of a new sentence is compared with the reference structures and if similarity is found then the sentence is considered as a potential candidate Next a rule-based post-processing module works on these potential candidates to select the final set of informative sentences The proposed approach is tested in sports domain, where many MCQs are easily available for preparing the reference set of structures The quality of the system selected sentences is evaluated manually The experimental result shows that the proposed technique is quite promising

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.

Factual open cloze question generation for assessment of learner’s knowledge

Abstract: Factual objective type questions are effectively used in active learning, information and communication technology based education and intelligent tutoring system for the assessment of learner’s content knowledge. In this paper, we have presented an automatic factual open cloze question generation system which can generate fill-in-the-blank questions without alternatives. In order to generate the questions, the system first extracts a set of informative sentences from the given input corpus. The sentences are considered as informative based on part-of-speech tags and certain rules. After the identification of the informative sentences the questions are generated by omitting the answer-keys which are selected by identifying domain specific words in the sentences. The unbound option set of an open cloze question often confuses the examinees. However, open cloze questions require more productive knowledge from learners than cloze questions. Finally, we have also suggested answer hints for the examinees to reduce the number of possible answers that make assessment easier.

A Systematic Review of Automatic Question Generation for Educational Purposes

Abstract: While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience, and resources. This, in turn, hinders and slows down the use of educational activities (e.g. providing practice questions) and new advances (e.g. adaptive testing) that require a large pool of questions. To reduce the expenses associated with manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (AQG) techniques were introduced. This review extends a previous review on AQG literature that has been published up to late 2014. It includes 93 papers that were between 2015 and early 2019 and tackle the automatic generation of questions for educational purposes. The aims of this review are to: provide an overview of the AQG community and its activities, summarise the current trends and advances in AQG, highlight the changes that the area has undergone in the recent years, and suggest areas for improvement and future opportunities for AQG. Similar to what was found previously, there is little focus in the current literature on generating questions of controlled difficulty, enriching question forms and structures, automating template construction, improving presentation, and generating feedback. Our findings also suggest the need to further improve experimental reporting, harmonise evaluation metrics, and investigate other evaluation methods that are more feasible.

Human-centered artificial intelligence in education: Seeing the invisible through the visible

Abstract: The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

Automatic Multiple Choice Question Generation From Text: A Survey

Abstract: Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's. Since then, many systems have been developed for MCQ generation. We perform a systematic review of those systems. This paper presents our findings on the review. We outline a generic workflow for an automatic MCQ generation system. The workflow consists of six phases. For each of these phases, we find and discuss the list of techniques adopted in the literature. We also study the evaluation techniques for assessing the quality of the system generated MCQs. Finally, we identify the areas where the current research focus should be directed toward enriching the literature.

Classification of Smart City Research - a Descriptive Literature Review and Future Research Agenda

Abstract: Smart City (SC) has been a popular area of research and practice during the last decade. An in-depth examination of the existing literature reviews on SCs divulges the scarcity of studies classifying the literature into multiple themes and identifying the popular and less popular themes based on the number of peer reviewed research papers under respective theme. Hence, in this study, a descriptive literature review of 86 peer-reviewed papers on SCs has been conducted to bridge this gap. The findings demonstrate that themes such as SC services design and management, innovation and technology, and citizens’ engagement in design and development of SCs have been extensively studied, whereas, themes such as the social impact, governance and policy, and performance indicators and standards have received moderate attention. However, there are also less popular themes such as the implementation barriers and SC strategy. Further, this study provides a reference source to future researchers. It also delivers valuable information to the policymakers and government bodies, which are actively, involved in the SC projects.