Search engines are the most popular tools for finding answers to questions, but unfortunately they do not always provide complete direct answers. Answers often need to be extracted by the user, from the web pages returned by the search engine. This research addresses this problem, and shows how an automated theorem prover, combined with existing ontologies and the web, is able to reason about world knowledge and return direct answers to users' questions. The use of an automated theorem prover also allows more complex questions to be asked. Automated theorem provers that exhibit these capabilities are called World Knowledge Reasoning systems. This research discusses one such system, the CNL-WKR system. The CNL-WKR system uses the ACE controlled natural language as its user-input language. It then calls upon external sources on the web, as well as internal ontological sources, during the theorem proving process, in order to find answers. The system uses the automated theorem prover, SPASS-XDB. The result is a system that is capable of answering complex questions about the world.

AN ABSTRACT OF THE DISSERTATION OF Valerie D. Newberry, for the Doctor of Philosophy degree in Workforce Education and Development, presented on June 2, 2009, at Southern Illinois University Carbondale. TITLE: EFFECTIVENESS OF A CLINICAL QUESTIONING ACTIVITY IN DEVELOPING CARDIAC SONOGRAPHY STUDENT LEARNING LEVELS MAJOR PROFESSOR: Dr. Marcia Anderson, Ph.D. The purpose of the study was to contribute to a better understanding of learning activities used in clinical education. In particular, this study aimed to investigate whether use of a question/answer (Q/A) activity in clinical learning promoted higher levels of learning. Results of this study will be beneficial to faculty in programs with a clinical component, clinical educators, employers, and to the students who participate in the programs. The two-fold problem addressed in this research study was (a) What is the level of student learning achieved with a Q/A clinical learning activity, as perceived by clinical instructors; and (b) is the students' perceived level of learning related to student characteristics? The study included students and clinical instructors from accredited cardiac sonography programs in the United States. Demographic data revealed the students were mostly female with a mean age of 30 and varying levels of education. Experts reviewed the Q/A activity for content validity and the level of Bloom's taxonomy. The instrument was revised based on their recommendations. Program directors in 53 programs were sent requests to participate in the study and nine programs had participants who agreed to be in the study. Participants were mailed the materials and asked to complete the Q/A assignment. Clinical instructors completed a teaching module prior to giving student scores for thinking levels. The demographic sheets, consents, and score sheets were mailed back and data analysis was completed. Findings of the study suggest the Q/A activity is successful at progressively increased means in the thinking scores of students. The teaching tutorial is adequate training for the clinical instructors on the levels of Bloom's taxonomy. The level of thinking score achieved is compared to the student's age, gender, and educational level with no statistically significant difference. Recommendations for practice, further research and instrumentation are discussed.

StackOverflow (SO) is a widely used question-and-answer (QandA) website for software developers and computer scientists. GitHub is a code hosting platform for collaboration and version control. Popular software libraries are open-source and published in repositories on GitHub. Preliminary observation shows developers cite SO questions in their GitHub repository. This observation inspired us to explore the relationship between SO posts and GitHub repositories; to help software developers better understand the characterization of SO answers that are reused by GitHub projects. For this study, we conducted an empirical study to investigate the SO answers reused by Java code from public GitHub projects. We used a hybrid approach to ensure precise results: code clone detection, keyword-based search, and manual inspection. This approach helped us identify the leveraged answers from developers. Based on the identified answers, we further investigated the topics of the discussion threads; answer characteristics (e.g., scores, ages, code lengths, and text lengths) and developers' reuse practices. We observed both reused and unused answers. Compared with unused answers, We found that the reused answers mostly have higher scores, longer code, and longer plain text explanations. Most reused answers were related to implementing specific coding tasks. In one of our observations, 9% (40/430) of scenarios, developers entirely copied code from one or multiple answers of an SO discussion thread. Furthermore, we observed that in the other 91% (390/430) of scenarios, developers only partially reused code or created brand new code from scratch. We investigated 130 SO discussion threads referred to by Java developers in 356 GitHub projects. We then arranged those into five different categories. Our findings can help the SO community have a better distribution of programming knowledge and skills, as well as inspire future research related to SO and GitHub. / Master of Science / StackOverflow (SO) is a widely used question-and-answer (QandA) website for software developers and computer scientists. GitHub is a code hosting platform for collaboration and version control. Popular software libraries are open-source and published in repositories on GitHub. Preliminary observation shows developers cite SO questions in their GitHub repository. This observation inspired us to explore the relationship between SO posts and GitHub repositories; to help software developers better understand the characterization of SO answers that are reused by GitHub projects. Our objectives are to guide SO answerers to help developers better; help tool builders understand how SO answers shape software products. Thus, we conducted an empirical study to investigate the SO answers reused by Java code from public GitHub projects. We used a hybrid approach to refine our dataset and to ensure precise results. Our hybrid approach includes three steps. The first step is code clone detection. We compared two code snippets with a code clone detection tool to find the similarity. The second step is a keyword-based search. We created multiple keywords to search within GitHub code to find the referenced answers missed by step one. Lastly, we manually inspected the outputs of both step one and two to ensure zero false positives in our data. This approach helped us identify the leveraged answers from developers. Based on the identified answers, we further investigated the topics of the discussion threads, answer characteristics, and developers' reuse practices. We observed both reused and unused answers. Compared with unused answers, We found that the reused answers mostly have higher scores, longer code, and longer plain text explanations. Most reused answers were related to implementing specific coding tasks. In one of our observations, 9% of scenarios, developers entirely copied code from one or multiple answers of an SO discussion thread. Furthermore, we observed that in the other 91% of scenarios, developers only partially reused code or created brand new code from scratch. Our findings can help the SO community have a better distribution of programming knowledge and skills, as well as inspire future research related to SO and GitHub.

Answer set programming (ASP) is a declarative programming paradigm oriented towards difficult combinatorial search problems. Syntactically, ASP programs look like Prolog programs, but solutions are represented in ASP by sets of atoms, and not by substitutions, as in Prolog. Answer set systems, such as Smodels, Smodelscc, and DLV, compute answer sets of a given program in the sense of the answer set (stable model) semantics. This is different from the functionality of Prolog systems, which determine when a given query is true relative to a given logic program. ASP has been applied to many areas of science and technology, from the design of a decision support system for the Space Shuttle to graph-theoretic problems arising in zoology and linguistics. The "native" answer set systems mentioned above are based on specialized search procedures. Usually these procedures are described fairly informally with the use of pseudocode. We propose an alternative approach to describing algorithms of answer set solvers. In this approach we specify what "states of computation" are, and which transitions between states are allowed. In this way, we define a directed graph such that every execution of a procedure corresponds to a path in this graph. This allows us to model algorithms of answer set solvers by a mathematically simple and elegant object, graph, rather than a collection of pseudocode statements. We use this abstract framework to describe and prove the correctness of the answer set solver Smodels, and also of Smodelscc, which enhances the former using learning and backjumping techniques. Answer sets of a tight program can be found by running a SAT solver on the program's completion, because for such a program answer sets are in a one-to-one correspondence with models of completion. SAT is one of the most widely studied problems in computational logic, and many efficient SAT procedures were developed over the last decade. Using SAT solvers for computing answer sets allows us to take advantage of the advances in the SAT area. For a nontight program it is still the case that each answer set corresponds to a model of program's completion but not vice versa. We show how to modify the search method typically used in SAT solvers to allow testing models of completion and employ learning to utilize testing information to guide the search. We develop a new SAT-based answer set solver, called Cmodels, based on this idea. We develop an abstract graph based framework for describing SAT-based answer set solvers and use it to represent the Cmodels algorithm and to demonstrate its correctness. Such representations allow us to better understand similarities and differences between native and SAT-based answer set solvers. We formally compare the Smodels algorithm with a variant of the Cmodels algorithm without learning. Abstract frameworks for describing native and SAT-based answer set solvers facilitate the development of new systems. We propose and implement the answer set solver called SUP that can be seen as a combination of computational ideas behind Cmodels and Smodels. Like Cmodels, solver SUP operates by computing a sequence of models of completion of the given program, but it does not form the completion. Instead, SUP runs the Atleast algorithm, one of the main building blocks of the Smodels procedure. Both systems Cmodels and SUP, developed in this dissertation, proved to be competitive answer set programming systems. / text

abstract: Online learning communities have changed the way users learn due to the technological affordances web 2.0 has offered. This shift has produced different kinds of learning communities like massive open online courses (MOOCs), learning management systems (LMS) and question and answer based learning communities. Question and answer based communities are an important part of social information seeking. Thousands of users participate in question and answer based communities on the web like Stack Overflow, Yahoo Answers and Wiki Answers. Research in user participation in different online communities identifies a universal phenomenon that a few users are responsible for answering a high percentage of questions and thus promoting the sustenance of a learning community. This principle implies two major categories of user participation, people who ask questions and those who answer questions. In this research, I try to look beyond this traditional view, identify multiple subtler user participation categories. Identification of multiple categories of users helps to provide specific support by treating each of these groups of users separately, in order to maintain the sustenance of the community. In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy. The goal of this thesis is to analyze user participation in three steps: a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data. b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques. c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community. This thesis presents new insights into participation because of the broad range of features extracted and their significance in understanding the behavior of users in this learning community. / Dissertation/Thesis / Masters Thesis Computer Science 2015

The goal of this paper is to benchmark Answer Set Programming (ASP) systems to test their performance when dealing with a complex optimization problem. In particular, the problem tackled is resource allocation in the area of Business Process Management (BPM). Like many other scheduling problems, the allocation of resources and starting times to business process activities is a challenging optimization problem for ASP solvers. Our problem encoding is ASP Core-2 standard compliant and it is realized in a declarative and compact fashion. We develop an instance generator that produces problem instances of different size and hardness with respect to adjustable parameters. By using the baseline encoding and the instance generator, we provide a comparison between the two award-winning ASP solvers clasp and wasp and report the grounding performance of gringo and i-dlv. The benchmark suggests that there is room for improvement concerning both the grounders and the solvers. Fostered by the relevance of the problem addressed, of which several variants have been described in different domains, we believe this is a solid application-oriented benchmark for the ASP community. / Series: Working Papers on Information Systems, Information Business and Operations

It is recognised that normative systems, and in particular electronic institutions and contracts are a potentially powerful means for making agent interactions in multi-agent systems effective and efficient. However, correctly specifying the behaviour of such systems is a difficult problem. Designers are faced with two concurrent, complex tasks: firstly they must specify the relationships (over time) between agents’ actions and their effects, and secondly they must also consider how agents’ actions are to be regulated through the definition of agents’ permissions and obligations. Such systems are typi- cally complex, and given this complexity it may be difficult for a designer to determine whether their original objectives have been captured by the specification of the system. In this dissertation we seek to address some of the problems associated with institu- tional specification. In order to do this we present a model for specifying institutions based on the notion of socially constructed reality that accounts not only for how the action and events which constitute the institution are described, but also how they are regulated. Institutions may be used in a number of ways, and may account for concepts at varying levels of abstraction. Recognising this we also investigate how several insti- tutions, each accounting for a particular aspect of a society may be composed and how the relationships between these institutions may be expressed. Given this model, we then demonstrate how, using the answer set programming paradigm institutional spec- ifications based on our model may be checked for the absence or presence of certain (un)desirable properties.

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Previous studies have shown some students with good word recognition skills and high levels of reading fluency fail to comprehend what they read.This study examined the explicit teaching of the rule based Question Answer Response (QAR) strategies to overcome specific learning difficulties in reading comprehension. Case study methods were used on five Year 4 students in Western Australia who were at different developmental levels in reading comprehension.The study revealed that within the conceptual framework of the transactional model of reading, the Question Answer Response strategies were effective in improving literal, inferential, critical and creative comprehension at varying levels for each of the five students in the study. The strategies also proved to be an effective way to teach the resolution of anaphoric pronouns that are particularly problematic for students with poor comprehension.The study indicates that the teaching of reading comprehension skills to students with reading comprehension problems through explicit rule based instruction is an important component of classroom-based instruction in the literacy learning area.

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