Introspective knowledge acquisition for case retrieval networks in textual case base reasoning.

Textual Case Based Reasoning (TCBR) aims at effective reuse of information contained in unstructured documents. The key advantage of TCBR over traditional Information Retrieval systems is its ability to incorporate domain-specific knowledge to facilitate case comparison beyond simple keyword matching. However, substantial human intervention is needed to acquire and transform this knowledge into a form suitable for a TCBR system. In this research, we present automated approaches that exploit statistical properties of document collections to alleviate this knowledge acquisition bottleneck. We focus on two important knowledge containers: relevance knowledge, which shows relatedness of features to cases, and similarity knowledge, which captures the relatedness of features to each other. The terminology is derived from the Case Retrieval Network (CRN) retrieval architecture in TCBR, which is used as the underlying formalism in this thesis applied to text classification. Latent Semantic Indexing (LSI) generated concepts are a useful resource for relevance knowledge acquisition for CRNs. This thesis introduces a supervised LSI technique called sprinkling that exploits class knowledge to bias LSI's concept generation. An extension of this idea, called Adaptive Sprinkling has been proposed to handle inter-class relationships in complex domains like hierarchical (e.g. Yahoo directory) and ordinal (e.g. product ranking) classification tasks. Experimental evaluation results show the superiority of CRNs created with sprinkling and AS, not only over LSI on its own, but also over state-of-the-art classifiers like Support Vector Machines (SVM). Current statistical approaches based on feature co-occurrences can be utilized to mine similarity knowledge for CRNs. However, related words often do not co-occur in the same document, though they co-occur with similar words. We introduce an algorithm to efficiently mine such indirect associations, called higher order associations. Empirical results show that CRNs created with the acquired similarity knowledge outperform both LSI and SVM. Incorporating acquired knowledge into the CRN transforms it into a densely connected network. While improving retrieval effectiveness, this has the unintended effect of slowing down retrieval. We propose a novel retrieval formalism called the Fast Case Retrieval Network (FCRN) which eliminates redundant run-time computations to improve retrieval speed. Experimental results show FCRN's ability to scale up over high dimensional textual casebases. Finally, we investigate novel ways of visualizing and estimating complexity of textual casebases that can help explain performance differences across casebases. Visualization provides a qualitative insight into the casebase, while complexity is a quantitative measure that characterizes classification or retrieval hardness intrinsic to a dataset. We study correlations of experimental results from the proposed approaches against complexity measures over diverse casebases

An Educator’s Perspective of the Tidyverse

Computing makes up a large and growing component of data science and statistics courses. Many of those courses, especially when taught by faculty who are statisticians by training, teach R as the programming language. A number of instructors have opted to build much of their teaching around use of the tidyverse. The tidyverse, in the words of its developers, “is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next” (Wickham et al. 2019). These shared principles have led to the widespread adoption of the tidyverse ecosystem. A large part of this usage is because the tidyverse tools have been intentionally designed to ease the learning process and make it easier for users to learn new functions as they engage with additional pieces of the larger ecosystem. Moreover, the functionality offered by the packages within the tidyverse spans the entire data science cycle, which includes data import, visualisation, wrangling, modeling, and communication. We believe the tidyverse provides an effective and efficient pathway for undergraduate students at all levels and majors to gain computational skills and thinking needed throughout the data science cycle. In this paper, we introduce the tidyverse from an educator’s perspective. We provide a brief introduction to the tidyverse, demonstrate how foundational statistics and data science tasks are accomplished with the tidyverse, and discuss the strengths of the tidyverse, particularly in the context of teaching and learning

Advanced Computer Graphics Aided Molecular Visualization and Manipulation Softwares: The Hierarchy of Research Methodologies

In the present day, the huge obstacles, and the major technical problems encountered by the teaching and research faculties, academicians, industrial specialists, laboratory demonstrators and instructors, fellow students and researchers, etc. are to adopt integrative approaches of demonstrating (learning) chemistry and chemical education, and the realistic ways of delivering (grasping) scientific materials articulately with graceful and effortless manner. Towards minimizing these challenges, various audio-visual tools and technologies, advanced computer aided molecular graphics, freely available electronic gadgets assisted chemistry and chemical education apps, human unreadable data reading and accessing softwares, etc. are being incorporated worldwide as the most indispensable physical and electronic means for successful professionalisms. This short article is essentially a collective report underscoring extraordinary approaches, incredible efforts, and innovative skills of the computer based chemical and molecular graphics towards illuminating intrinsic parts of the chemistry and chemical education, and revealing various aspects of the cutting -edge research. As their representatives, herein, the different type computer coding languages based graphical tools such as Molden, GaussView, Jmol, and Visual Molecular Dynamics (VMD) are referred, and elucidated their potential applications and remarkable attempts in the advancement of diverse areas of chemistry and chemical education. Beside this, the most essential graphical features, unique rendering abilities with magnificent views, splendid visualizing skills, awesome data accessing functionalities, etc. of each of them, and their invaluable roles for studying complex molecules, biomolecules, crystals, and the entire material assemblies as well as for investigating global and local molecular physicochemical properties are presented concisely with the special stresses on their relatively better and comparatively more applicable distinctive attributes explicitl

Organic Chemistry in Virtual Reality: Bridging Gaps between Two-Dimensional and Three-Dimensional Representations

The traditional two-dimensional representations in organic chemistry education highlighted the lack of depth and interactivity, impeding student learning, engagement, and comprehension. By emphasizing on the limitations of conventional educational materials, the research advocated for integrating Augmented Reality (AR) and Virtual Reality (VR) technologies, which enhance organic chemistry visualization. The main objective was to bridge the gap between two and three-dimensional perspectives, offering a more dynamic and interactive learning experience. The thesis aimed to assess traditional teaching methods in organic chemistry—lectures, textbooks, and laboratory exercises. It also aimed to identify their challenges in conveying complex molecular structures and reactions effectively. Additionally, it explored the integration of Virtual Reality (VR) and Augmented Reality (AR) with these conventional methods. The goal had been to develop a cohesive educational framework that combined the strengths of both traditional and modern technological approaches. This blended learning model was meant to improve student engagement and understanding by incorporating dynamic visualizations into lectures as well as interactive content into textbooks. Building on this premise, the research focused on the following questions: 1. What challenges do traditional teaching methods face in teaching organic chemistry concepts adequately? 2. What advantages do VR and AR offer in organic chemistry education compared to traditional methods? 3. What impact do VR and AR technologies have on student engagement in organic chemistry compared to traditional teaching methods? 4. How can VR and AR be tailored to meet pedagogical and andragogical needs in organic chemistry education? 5. Why are VR and AR more effective than traditional methods in enhancing learning in organic chemistry? 6. What are the best strategies for integrating VR and AR into the organic chemistry curricula to enhance learning alongside traditional methods? 7. How can AR and VR in organic chemistry education be aligned with Vygotsky’s Zone of Proximal Development to improve learning outcomes? 8. How can AR and VR be personalized in organic chemistry education to support individual learning and Piaget\u27s theory of self-learning? 9. What are the benefits and challenges of applying the \u27Ship Early, Ship Often\u27 approach to developing AR and VR tools in organic chemistry education? Upon the completion of this research, a literature review was conducted additionally as well as visual and content analyses. Based upon the research conducted, a visual solution was created to guide curriculum developers, textbook publishers, researchers, and educators in integrating VR and AR technologies into traditional organic chemistry curricula. The deliverable theory of the visual was a high-fidelity wireframe prototype created for VR and AR in Organic Chemistry, designed to enhance student engagement and understanding by combining immersive technology with traditional teaching methods. The project also featured a responsive website to inform stakeholders about the benefits of this integration, supported by print media like brochures, posters, and billboards for broader outreach and awareness. The high-fidelity wireframe prototype with the responsive website and supporting print media, were crucial elements in reshaping organic chemistry education, bridging the gap between traditional pedagogy and andragogy as well as futuristic learning paradigms

ACCESSIBLE ACCESS CONTROL: A VISUALIZATION SYSTEM FOR ACCESS CONTROL POLICY MANAGEMENT

Attacks on computers today present in many different forms, causing malfunction of operating systems, information leakage and loss of business and public trust. Access control is a technique that stands as the last line of protection restricting the access of users or processes to resources on computers. Throughout the years, many access control models have been implemented to accommodate security requirements under different circumstances. However, the learning of access control models and the management of access control policies are still challenging given its abstract nature, the lack of an environment for practice, and the intricacy of fulfilling complex security goals. These problems seriously reduce the usability of access control models. In this dissertation, we present a set of pedagogical systems that facilitates the teaching and studying of access control models and a visualization system that aids the authoring and analysis of access control policies. These systems are designed to tackle the usability problems in two steps. First, the pedagogical systems were designed for new learners to overcome the obstacles of learning access control and the lack of practicing environment at the very beginning. Contrary to the traditional lecture and in-paper homework method, the tool allows users to write/import a policy file, follow the visual steps to understand the concepts and access mechanisms of a model and conduct self-evaluation through Quiz and Query modules. Each of the four systems is specifically designed for a model of the Domain Type Enforcement, Multi-level Security, Role-based Access Control, or UNIX permissions. Through these systems, users are able to take an active role in exploring the effect of a policy with a safe and intact underlying operating systems. Second, writing and evaluating the effect of a policy could also be challenging and tedious even for security professionals when there are thousands of lines of rules. We believe that writing an access control policy should not include the complexity of learning a new language, and managing the policies should never be manual when automatic examination could take the place. In the aspect of policy writing, the visualization system kept the least number of key elements for specifying a rule: user, object, and action. They describe the active entity who takes the action, the file or directory which the action is applied to, and the type of accesses allowed, respectively. Because of its simple form without requiring the learning of a programming-like language, we hope that specifying policies using our language could be accomplished effortlessly not only by security professionals but also by anyone who is interested in access control. Moreover, policies can often be left unexamined when deployed. This is similar to releasing program which was untested and could lead to dangerous results. Therefore, the visualization system provides ways to explore and analyze access control policies to help confirm the effect of the policies. Through interactive textual and graphical illustrations, users could specify the accesses to check, and be notified when problems exist

Design and Implementation of Online Learning Environments

This thesis describes a systematic approach in the design and implementation of online learning environments. This approach incorporates the principles of human learning as well as the best practices in software engineering. This thesis implements a conceptual model for the design, and it describes how software elements can be developed to comply with the model. In the context of this research two online environments are developed and analyzed. The end product of this approach is a robust and reusable software architecture, a framework for design, and an effective and engaging model suited to online learning environments

Design and Implementation of Online Learning Environments

This thesis describes a systematic approach in the design and implementation of online learning environments. This approach incorporates the principles of human learning as well as the best practices in software engineering. This thesis implements a conceptual model for the design, and it describes how software elements can be developed to comply with the model. In the context of this research two online environments are developed and analyzed. The end product of this approach is a robust and reusable software architecture, a framework for design, and an effective and engaging model suited to online learning environments

Design and Implementation of Online Learning Environments

This thesis describes a systematic approach in the design and implementation of online learning environments. This approach incorporates the principles of human learning as well as the best practices in software engineering. This thesis implements a conceptual model for the design, and it describes how software elements can be developed to comply with the model. In the context of this research two online environments are developed and analyzed. The end product of this approach is a robust and reusable software architecture, a framework for design, and an effective and engaging model suited to online learning environments

Effects of Diagrams on Strategy Choice in Probability Problem Solving

The role of diagrammatic representations and visual reasoning in mathematics problem solving has been extensively studied. Prior research on visual reasoning and problem solving has provided evidence that the format of a diagram can modulate solvers’ interpretations of the structure and concept of the represented problem information, and influence their problem solving outcomes. In this dissertation, two studies investigated how different types of diagrams influence solvers’ choice of solution strategy and their success rate in solving probability word problems. Participants’ solution strategies suggested that problem solvers tended to construct solutions that reflect the structure of a provided diagram, resulting in different representations of the mathematical structure of the problem. For the present set of problems, a binary tree or a binary table tends to steer solvers to use a sequential-sampling strategy, which defines simple or conditional probabilities for each selection stage and calculates the intersection of these probabilities as the final probability value, using the multiplication rule of probability. This strategy choice is structurally matched with the diagrammatic structure of a binary tree or a binary table, which represents unequally-likely outcomes at the event level. In contrast, an N-by-N (outcome) table steers solvers to use of an outcome-search strategy, which involves searching for the total number of target outcomes and all the possible outcomes at the equally-likely outcome level, and calculates the part-over-the-whole value as the final probability, using the classical definition of probability. This strategy is strongly cued by the N-by-N (outcome) table, because the table structure represents all equally-likely outcomes for a probability problem, and organizes the information so that the target outcomes can be seen as a subset embedded in the whole outcome space. When an N-ary (outcome) tree was provided, choices were split between the two solutions, because the N-ary tree structure not only cues searching for equally-likely outcomes but also organizes the problem information in a sequential-sampling, stage-by-stage way. Furthermore, different diagrams seem to be associated with different patterns of characteristic errors. For example, solving a combinations problem with an N-by-N table tended to elicit erroneous solutions involving miscounting those self-repeated combinations represented by the table’s diagonal cells as valid outcomes. Typical errors associated with the use of a binary tree involved incorrect value definitions of the conditional probability of the outcome of a selection. And the N-ary tree may lead to less successful coordination of all the target outcomes for the studied problems, because the target outcomes were dispersed in the outcome space depicted by the tree, thus not salient. The findings support arguments (e.g., Tversky, Morrison, & Betrancourt, 2002) that in order to promote problem solving success, a diagrammatic representation must be carefully selected or designed so that its structure and content can be well-matched to the problem structure and content. And for computational efficiency, information should be spatially organized so that it can be processed readily and accurately. In addition to the implications for effective diagram design for problem solving activities, the findings also offer important insights for probability education. It is suggested that a variety of diagram types be utilized in the educational activities for novice learners of probability, because they tend to highlight different probability concepts and structures even for the same probability topic

Using an e-learning tool to overcome difficulties in learning object-oriented programming

This study was motivated by the need to overcome the pedagogical hindrances experienced by introductory object-oriented programming students in order to address the high attrition rate evident among novice programmers in distance education. The initial phase of the research process involved exploring a variety of alternative visual programming environments for novices. Thereafter the selection process detailed several requirements that would define the ideal choice of the most appropriate tool. An educational tool Raptor was selected. Lastly, the core focus of this mixed method research was to evaluate undergraduate UNISA students’ perceptions of the Raptor e-learning tools with respect to the perceived effectiveness in enhancing novices’ learning experience, in an attempt to lower the barriers to object-oriented programming. Students’ perceptions collectively of the Raptor visual tool were positive and despite the fact that the sample size was too small to achieve statistical significance, these quantitative and qualitative results provide the practical basis for implementing Raptor in future. Thus providing learning opportunities suited to learner interests and needs, can lead to an enormous potential to stimulate individuals’ motivation and development in creating a more positive learning experience to overcome barriers in programming and enhance concept understanding to address the diverse needs of students in distance education that could lead to a reduced dropout rate.ComputingM. Sc. (Computing