The key objective of the teaching-learning process (TLP) is to impart the knowledge to the learner. In the digital world, the computer-based system emphasis teaching through online mode known as e-learning. The expertise level of the learner in learned subjects can be measured through e-assessment in which multiple choice questions (MCQ) is considered to be an effective one. The assessment questions play the vital role which decides the ability level of a learner. In manual preparation, covering all the topics is difficult and time consumable. Hence, this article proposes a system which automatically generates two different types of question helps to identify the skill level of a learner. First, the MCQ questions with the distractor set are created using named entity recognizer (NER). Further, based on blooms taxonomy the Subjective questions are generated using natural language processing (NLP). The objective of the proposed system is to generate the questions dynamically which helps to reduce the occupation of memory concept.
The modern digital world requires its users to learn continuously in order to enhance their knowledge in the working environment and the academic sector. This kind of learning is significantly facilitated by the E-Learning platform, which is better than the traditional methods. As E-Learning offers benefits like time and space independence, many learners have made it their choice. However, since an abundant of E-Learning courses are available on websites, learners are confused as to which is the right one to choose. This paper proposes an Automated Intelligent Learning (AIL) methodology which covers the entire Teaching-Learning Process (TLP) to overcome this issue. It enables the selection of suitable topics and framing an appropriate course syllabus and assessment questions for the users. In it, the learner satisfies topic selection based on Bloom's taxonomy. This enables high-quality knowledge outcomes in the learner. The subject curriculum is framed by using Hierarchical clustering techniques. This helps the user to fix suitable topics and conveniently generate questions using machine learning techniques. The proposed methodology was evaluated by carrying out post and pre-assessment tests on undergraduate students from computer science courses. The performance analysis of the proposed methodology was compared with that of the existing methodology. It was observed that the proposed methodology is effective in applying the topic selection hierarchical method to make a perfect syllabus for the course, and assessment questions. Besides, it was found to enable the learner to learn without any confusion or distraction.
: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.
The Current scenario of the educational system is highly utilizing computer-based technology. For the Teaching-Learning process, both the learners and teachers are highly preferred the online system i.e, E-Learning because of its user-friendly approach such as learning at anytime and anywhere. In the Online educational system, the E-Content plays a major role so the critical importance has to be provided in generating the E-Content. Currently, a large number of study materials are dumped into the internet which has reached the highest limit. The enormous amount of content with high volume leads the learner to skim or frustration in learning. Learners have to spend too much of time to understand their concept from the selected web page. The Tutor also faces the challenges in setting the question paper from this high volume of learning content. We have proposed the computer-assisted system to summarize the learning content of the material using Machine Learning techniques. The Latent Semantic Analysis reduces the size of the content without changing their originality. Finally, the singular value decomposition is used to select the important sentences in order to generate the Multiple Choice Questions (MCQ) to assess the knowledge level of the learner
This paper proposes a new rule-based approach to automated question generation. The proposed approach focuses on the analysis of both sentence syntax and semantic structure. The design and implementation of the proposed approach is also described in detail. Although the primary purpose of a design system is to generate query from sentences, automated evaluation results show that it can also perform great when reading comprehension datasets that focus on question output from paragraphs. With regard to human evaluation, the designed system performs better than all other systems and generates the most natural (human-like) questions. We present a fresh approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems. In our system, we will take data from various sources for a particular topic and summarize it for the convenience of the people, so that they don't have to go through so multiple sites for relevant data.
Automatic Question Generation (AQG) is a research trend that enables teachers to create assessments with greater efficiency in right set of questions from the study material. Today's educational institutions require a powerful tool to correctly assess learner's mastery of concepts learned through study materials. Objective type questions are an excellent method of assessing a learner's topic understanding in learning process, based on Information and Communication Technology (ICT) and Intelligent Tutoring Systems (ITS).Creating a set of questions for assessment can take a significant amount of time for teachers, and obtaining questions from external sources such as assessment books or question banks may not be relevant to the content covered by students during their studies. This proposed system involves to generate the familiar objective type questions like True or False, 'Wh', Fill up with double blank space, match the following type question have generated using Natural Language Processing(NLP) techniquesfrom the given study material. Different rules are created to generate T/F and 'Wh' type questions. Dependence parser method has involved in 'Wh' questions. Proposed system is tested with Grade V Computer Science text book as an input. Experimental result shows that the proposed system is quite promising to generate the amount of objective type assessment questions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.