Ontologies for automatic question generation

Assessment is an important tool for formal learning, especially in higher education. At present, many universities use online assessment systems where questions are entered manually into a question bank system. This kind of system requires the instructor’s time and effort to construct questions manually. The main aim of this thesis is, therefore, to contribute to the investigation of new question generation strategies for short/long answer questions in order to allow for the development of automatic factual question generation from an ontology for educational assessment purposes. This research is guided by four research questions: (1) How well can an ontology be used for generating factual assessment questions? (2) How can questions be generated from course ontology? (3) Are the ontological question generation strategies able to generate acceptable assessment questions? and (4) Do the topic-based indexing able to improve the feasibility of AQGen. We firstly conduct ontology validation to evaluate the appropriateness of concept representation using a competency question approach. We used revision questions from the textbook to obtain keyword (in revision questions) and a concept (in the ontology) matching. The results show that only half of the ontology concepts matched the keywords. We took further investigation on the unmatched concepts and found some incorrect concept naming and later suggest a guideline for an appropriate concept naming. At the same time, we introduce validation of ontology using revision questions as competency questions to check for ontology completeness. Furthermore, we also proposed 17 short/long answer question templates for 3 question categories, namely definition, concept completion and comparison. In the subsequent part of the thesis, we develop the AQGen tool and evaluate the generated questions. Two Computer Science subjects, namely OS and CNS, are chosen to evaluate AQGen generated questions. We conduct a questionnaire survey from 17 domain experts to identify experts’ agreement on the acceptability measure of AQGen generated questions. The experts’ agreements for acceptability measure are favourable, and it is reported that three of the four QG strategies proposed can generate acceptable questions. It has generated thousands of questions from the 3 question categories. AQGen is updated with question selection to generate a feasible question set from a tremendous amount of generated questions before. We have suggested topic-based indexing with the purpose to assert knowledge about topic chapters into ontology representation for question selection. The topic indexing shows a feasible result for filtering question by topics. Finally, our results contribute to an understanding of ontology element representation for question generations and how to automatically generate questions from ontology for education assessment

A study on one way hashing function and its application for FTMSK webmail / Noor Hasimah Ibrahim Teo

Password is a normal way for securing data from intruders. The widespread use of password is in email account. The advances of technology have reduced the function of password in security, where there are many chances of password to be sniffed or hack by intruders. FTMSK webmail is using password as an authentication method. The problem was, their lecturer is not allowed to send examination question through email. This means that they do not trust the security of webmail. There are several techniques use to transform plaintext password to other form of password. One of it is call one-way hashing function. One-way hashing function consists of several algorithms. However MD5 is the most common hashing function currently in use. The research are aim to detemriine security of using one way hashing function at client side for FTMSK webmail login system and design framework for one way hashing function. A prototype is developed using MD5 algorithm and based on prototype approach, since it study on existing system. Tests are run for both FTMSK webmail and prototypes to determine whether the plain text password can be retrieved. Furthermore framework for one way hashing function is designed. Password need to be store in database on server in the form of hashing value. Secure password during transmission can be obtained by running protection on the client side of client server architecture

Exploring YouTube Comments to Understand Public Sentiment on COVID-19 Vaccines through Deep Learning-based Sentiment Analysis

COVID-19 was first found in China in 2019. Since then, it has quickly spread around the world, which has led to a lot of news stories and social media posts about the pandemic. YouTube, a popular video-sharing website, has become a valuable source of information on COVID-19 and other topics. However, it can be difficult to extract useful insights from the vast array of user comments that accompany these videos. One potential method for understanding public sentiment is to use sentiment analysis, which involves classifying text as positive, negative, or neutral. In this study, the dataset of over 44,000 YouTube comments related to COVID-19 vaccines was used, which was filtered to a total of 16,073 comments for analysis. The data was cleaned and organised using NeatText and then processed using GloVe word embedding, a technique for establishing statistical relationships between words. Based on the experiment, the performances of three different types of deep learning techniques: recurrent neural networks (RNN), gated recurrent units (GRU) and long short-term memory (LSTM) are compared in accurately classifying the sentiment of the comments. The study found that the GRU had the highest accuracy of 80.19%, followed by the LSTM with 79.00% accuracy, and the RNN with 67.15% accuracy

Categorized question template generation for ontology-based assessment questions

This paper discusses how to generate question templates by interpreting existing assessment questions from textbooks and validating concepts from existing ontologies. Previous work shows that most of the concepts that exist in the ontologies can be used as keywords to generate useful assessment questions. In this paper, we consider questions generated from a variety of ontologies, and question taxonomies, which include definitions, concept completions, and comparisons. Evaluation of novel techniques for question generation provides insights on how to extract a pattern from assessment questions in a textbook in order to create question templates that can be used with concepts presented in ontologies to generate useful questions. This paper discusses the method used and the experimental result

Evaluation of an automatic question generation approach using ontologies

Advancements in Semantic Web techniques have led to the emergence of ontology based question generation. Ontologies are used to represent domain knowledge in the form of concepts, instances and their relationships as their elements. Many research strategies for generating questions using ontologies have been proposed but little work has been done on investigating whether an ontology is an appropriate source of data for question generation. Since there is no standard guideline for developing an ontology, the representation of ontology elements might vary in many ways, and this paper aims to investigate how the naming of ontology elements would affect the questions generated. In order to achieve this aim, two research questions will be investigated which are: how many correct questions can be generated from an ontology, and what are the reasons for incorrect questions being generated. Categorized question templates and a set of question strategies for mapping templates with a concept in an ontology are proposed. A prototype has been developed with a Reader to read data from input file and 3 question genera tors namely termQG, ClassQG and PropertyQG to generate questions for 3 ontological approaches. After questions have been generated, the number of correct questions generated is calculated and the reasons for incorrect questions are identified. Two ontologies have been used, an Operating System Ontology and a Travel Ontology. Twenty question templates from three question categories – definition, concept completion and comparison – together with 5 question generation strategies have been used in this evaluation. Results shows that more than half of the questions generated are correct and there are 3 distinct reasons why incorrect questions may be generated. The main contribution to incorrect question generation was inappropriate naming of ontology elements where 4 distinct categories are further identified. In addition, evaluation shows that the object type should be considered when designing question templates. Furthermore, the evaluation indirectly shows the effectiveness of the ontological approaches for generating questions from a real-world ontology

Validation of course ontology elements for automatic question generation

Recent research has led to the emergence of ontology-based question generation, and aims to benefit instructors by providing support and intelligent assistance for the automatic generation of questions. However, existing ontologies are not designed mainly for this purpose and the concern is that an ontology will not be competent enough to act as a semantic source for the question generation process. Therefore, the aim of this work is to validate how well the elements represented in a course ontology can be used for the purpose of automatic question generation. In this work, we choose to validate Operating System ontologies and identify related question sources from textbooks on this subject as competency questions. Finally, the result shows that the evaluated ontologies need more modification if they are to be used for question generation and we also suggest a list of concept naming patterns that need to be considered for such ontology modification