Improving the interfaces of online discussion forums to enhance learning support : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Information Systems at Massey University, Palmerston North, New Zealand

This thesis describes a research work aimed at improving the interfaces of online discussion forums (ODFs) in relation to their functional support to enhance learning. These ODFs form part of almost all Learning Management Systems (LMSs) such as WebCT, Moodle and Blackboard, which are widely used in education nowadays. Although ODFs are identified as valuable sources to learning, their interfaces are limited in terms of providing support to students, such as in the areas of managing their postings as well as in facilitating them to quickly locate and obtain specified information. In addition, these systems lack features to support inter-institutional cooperation that could potentially increase knowledge sharing between students and educators of different institutions. The interface design objective of this study therefore was to explore and overcome the limitations identified as above, and enhance the effectiveness and efficiency of ODFs’ support to learning. Using a task centered design approach; the required features were developed, and implemented in a working prototype called eQuake (electronic Question answer knowledge environment). eQuake is a shared online discussion forum system developed as an add-on to a well-known open source e-learning platform (Moodle). This system was intended for use among interinstitutional students in New Zealand tertiary institutions that teach similar courses. The improved interface functionalities of eQuake are expected to enhance learning support in terms of widening communication among users, increasing knowledge base, providing existing matching answer(s) quickly to students, and exposing students to multiple perspectives. This study considers such improvements to ODF interfaces as vital to enable users to enjoy the benefits of technology-mediated environment. The perceived usefulness and ease-of-use of improved features in eQuake were evaluated using a quantitative experimental research method. The evaluation was conducted at three tertiary institutions in New Zealand, and the overall results indicated positive response, although some suggestions for improvement have been made in the evaluation. This thesis presents a review of the related literature, describes the design and development of a user interface, followed by its implementation in eQuake, and a description of the evaluation. The thesis concludes with recommendations for better interface design of ODFs and provides suggestions for future research in this area

Contextual information, answerability, and the logical construction of social how-to questions

For technical-knowledge workers seeking information about how to complete software tasks, online social question and answer (SQA) sites represent a valuable resource as an emerging form of software documentation. However, because answerers on these sites respond to questions on a volunteer basis, not all questions receive answers. Current research shows that askers provide contextual information in varying amounts, yet researchers have not yet reliably described contextual information types, disagree on whether more or less information associates with answerability, and have not yet compared the coherence of answered and unanswered questions. To assist technical-knowledge workers posting questions on SQA sites, this study explores the relationship between contextual information and answerability and between logical coherence and answerability. This study analyzes 3,529 contextual-information t-units and 690 comment t-units from social how-to questions about Microsoft Word that askers posted on the popular SQA site Super User. Content analysis enabled a close examination of not only the amounts of contextual information that askers provided, but also the types of information, relationships among types, and relationships between types and answerability. Establishing and using three reliable codebooks related to social how-to questions, to contextual information, and to answerers’ follow-up comments, the study presents descriptive statistics and examples of contextual-information types and comment types. Further analyzing contextual-information types, the study presents and explores statistical differences in the distinctness, magnitude, variation, efficiency, word count, and logical coherence of contextual information in answered and unanswered questions

Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross-platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed

Community based Question Answer Detection

Each day, millions of people ask questions and search for answers on the World Wide Web. Due to this, the Internet has grown to a world wide database of questions and answers, accessible to almost everyone. Since this database is so huge, it is hard to find out whether a question has been answered or even asked before. As a consequence, users are asking the same questions again and again, producing a vicious circle of new content which hides the important information. One platform for questions and answers are Web forums, also known as discussion boards. They present discussions as item streams where each item contains the contribution of one author. These contributions contain questions and answers in human readable form. People use search engines to search for information on such platforms. However, current search engines are neither optimized to highlight individual questions and answers nor to show which questions are asked often and which ones are already answered. In order to close this gap, this thesis introduces the \\emph{Effingo} system. The Effingo system is intended to extract forums from around the Web and find question and answer items. It also needs to link equal questions and aggregate associated answers. That way it is possible to find out whether a question has been asked before and whether it has already been answered. Based on these information it is possible to derive the most urgent questions from the system, to determine which ones are new and which ones are discussed and answered frequently. As a result, users are prevented from creating useless discussions, thus reducing the server load and information overload for further searches. The first research area explored by this thesis is forum data extraction. The results from this area are intended be used to create a database of forum posts as large as possible. Furthermore, it uses question-answer detection in order to find out which forum items are questions and which ones are answers and, finally, topic detection to aggregate questions on the same topic as well as discover duplicate answers. These areas are either extended by Effingo, using forum specific features such as the user graph, forum item relations and forum link structure, or adapted as a means to cope with the specific problems created by user generated content. Such problems arise from poorly written and very short texts as well as from hidden or distributed information

SOCIALQ&A: A NOVEL APPROACH TO NOTIFIYING THE CORRECT USERS IN QUESTION AND ANSWERING SYSTEMS

Question and Answering (Q&A) systems are currently in use by a large number of Internet users. Q&A systems play a vital role in our daily life as an important platform for information and knowledge sharing. Hence, much research has been devoted to improving the performance of Q&A systems, with a focus on improving the quality of answers provided by users, reducing the wait time for users who ask questions, using a knowledge base to provide answers via text mining, and directing questions to appropriate users. Due to the growing popularity of Q&A systems, the number of questions in the system can become very large; thus, it is unlikely for an answer provider to simply stumble upon a question that he/she can answer properly. The primary objective of this research is to improve the quality of answers and to decrease wait times by forwarding questions to users who exhibit an interest or expertise in the area to which the question belongs. To that end, this research studies how to leverage social networks to enhance the performance of Q&A systems. We have proposed SocialQ&A, a social network based Q&A system that identifies and notifies the users who are most likely to answer a question. SocialQ&A incorporates three major components: User Interest Analyzer, Question Categorizer, and Question- User Mapper. The User Interest Analyzer associates each user with a vector of interest categories. The Question Categorizer algorithm associates a vector of interest categories to each question. Then, based on user interest and user social connectedness, the Question-User Mapper identifies a list of potential answer providers for each question. We have also implemented a real-world prototype for SocialQ&A and analyzed the data from questions/answers obtained from the prototype. Results suggest that social networks can be leveraged to improve the quality of answers and reduce the wait time for answers. Thus, this research provides a promising direction to improve the performance of Q&A systems

IMPROVING PEER LEARNING AND KNOWLEDGE SHARING IN STEM COURSES VIA PATTERN BASED GRAPH VISUALIZATION

High quality education in Science, Technology, Engineering and Math (STEM) majors expects not only the acquisition of comprehensive domain knowledge, but also the mastery of skills to solve open-ended and even ill-defined problems in real world. Problem-based Learning (PBL) is usually adopted to achieve such goals by encouraging students to learn by solving real-life problems. However, successful PBL requires sustained and in-depth involvement of faculty members, hence making PBL not scalable. Even though discussion forums and Q&A systems can help address the scalability problem of faculty involvement on large class sizes, it introduces new problems. First, as knowledge bases grow in size, the sheer size of the accumulated knowledge makes it harder to locate the desired information. Second, existing knowledge discovery techniques do not provide effective facilities for the capture and reuse of solutions to recurring problems. To address these challenges, we developed MicroBrowser, an innovative and interactive Question & Answer (Q&A) system augmented with pattern-based expertise-sharing interfaces and 2D knowledge graph discussion visualization. MicroBrowser provides a set of pattern-based expertise-sharing interfaces to allow both learners and instructors to refine, reuse, and share knowledge. MicroBrowser also allows learners to browse and navigate important discussions based on topic similarity encoded by node proximity in a knowledge graph. Results of empirical evaluations of our proposed solution show that ask difficulty improves with MicroBrowser when compared with a state-of-the-art Q&A system for knowledge discovery and reuse tasks. In addition, success rate for knowledge discovery tasks using keywords was higher with MicroBrowser. Moreover, we show that, students found the pattern-based expertise-sharing interface easy to use and were able to contribute new knowledge in the form of new knowledge connections and even recommend new design patterns

AN APPROACH FOR AUTO-GENERATING SOLUTION TO USER-GENERATED MEDICAL CONTENT USING DEEP LEARNING TECHNIQUES

One of many things humans are obsessive about is health. Presently, when faced with a health-related issue one goes to the web first, to find closure to his/her problem. The community Question Answering (cQA) forum allows people to pose their query and/or discuss it. Due to alike or unique nature of the health query it may go unanswered. Many a time the answers provided are ill-founded, leaving the user discontent. This indicates that the process is dependent on supplementary users or experts, in relation to their ability and/or the time taken to answer the question. Hence, the need to create an answer predictor which provides instant and better-quality result. We, therefore propose a novel scheme where deep learning is used to produce appropriate answer to the given health query. Both historical data i.e. cQA and general medical data are used to form a powerful Knowledge Base (KB), to assist the health predictor

An ant-inspired, deniable routing approach in ad hoc question & answer networks

The ubiquity of the Internet facilitates electronic question and answering (Q&A) between real people with ease via community portals and social networking websites. It is a useful service which allows users to appeal to a broad range of answerers. In most cases however, Q&A services produce answers by presenting questions to the general public or associated digital community with little regard for the amount of time users spend examining and answering them. Ultimately, a question may receive large amounts of attention but still not be answered adequately. Several existing pieces of research investigate the reasons why questions do not receive answers on Q&A services and suggest that it may be associated with users being afraid of expressing themselves. Q&A works well for solving information needs, however, it rarely takes into account the privacy requirements of the users who form the service. This thesis was motivated by the need for a more targeted approach towards Q&A by distributing the service across ad hoc networks. The main contribution of this thesis is a novel routing technique and networking environment (distributed Q&A) which balances answer quality and user attention while protecting privacy through plausible deniability. Routing approaches are evaluated experimentally by statistics gained from peer-to-peer network simulations, composed of Q&A users modelled via features extracted from the analysis of a large Yahoo! Answers dataset. Suggestions for future directions to this work are presented from the knowledge gained from our results and conclusion