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 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.
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.
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 distractor generation. The sentence is selected using a coarse‐grain part‐of‐speech tagset. The answer‐key 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 distractor generation technique to produce multiple‐choice fill‐in‐the‐blank test items.
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