Learning management systems (LMS) are playing a key role in the education systems. Education institutions are using LMS platforms to make the communication and collaboration between teacher and student easier, which inspired this study to measure the user satisfaction in using the different platforms applying usability heuristics. The survey evaluation is used to measure user satisfaction. The main objective of this study is to measure the user experience while using interactive interfaces. The selected LMS platforms for the research are canvas and google classroom. The experiment involves creating dummy course in the selected LMS platforms, fabricating the course assignments, gathering the users, and enrolling them into the platforms. The enrolled set of users will complete the assignments and take a survey on their experience with the platforms. Time taken by each user to complete assignments and survey are recorded and collected off-time comments. The responses of the survey are collected and graphically will interpret each question. Statistical attributes like populated variance and standard deviation are calculated for measuring the user experiences, and they are tabulated for the LMS platforms. User satisfaction on the canvas and google classroom was measured using usability heuristics. From the survey results, we can conclude that the canvas web application obeys all the usability heuristics, whereas the google classroom obeys only seven of the usability heuristics.

The focus of this thesis is the development and solution of problems that simultaneously involve the planning of the location of facilities and transportation decisions from such facilities to consumers. This has been termed integrated distribution planning problems with practical application in logistics and manufacturing. In this integration, different planning horizons of short, medium and long terms are involved with the possibility of reaching sub-optimal decisions being likely when the planning horizons are considered separately. Two categories of problems were considered under the integrated distribution models. The first is referred to as the Step-Fixed Charge Location and Transportation Problem (SFCLTP). The second is termed the Fixed Charge Solid Location and Transportation Problem (FCSLTP). In these models, the facility location problem is considered to be a strategic or long term decision. The short to medium-term decisions considered are the Step-Fixed Charge Transportation Problem (SFCTP) and the Fixed Charge Solid Transportation Problem (FCSTP). Both SFCTP and FCSTP are different extensions to the classical transportation problem, requiring a trade-off between fixed and variable costs along the transportation routes to minimize total transportation costs. Linearization and subsequent local improvement search techniques were developed to solve the SFCLTP. The first search technique involved the development of a hands-on solution including a numerical example. In this solution technique, linearization was employed as the primal solution, following which structured perturbation logic was developed to improve on the initial solution. The second search technique proposed also utilized the linearization principle as a base solution in addition to some heuristics to construct transportation problems. The resulting transportation problems were solved to arrive at a competitive solution as regards effectiveness (solution value) compared to those obtainable from standard solvers such as CPLEX. The FCSLTP is formulated and solved using the CPLEX commercial optimization suite. A Lagrange Relaxation Heuristic (LRH) and a Hybrid Genetic Algorithm (GA) solution of the FCSLTP are presented as alternative solutions. Comparative studies between the FCSTP and the FCSLTP formulation are also presented. The LRH is demonstrated with a numerical example and also extended to hopefully generate improved upper bounds. The CPLEX solution generated better lower bounds and upper bound when compared with the extended LRH. However, it was observed that as problem size increased, the solution time of CPLEX increased exponentially. The FCSTP was recommended as a possible starting solution for solving the FCSLTP. This is due to a lower solution time and its feasible solution generation illustrated through experimentation. The Hybrid Genetic Algorithm (HGA) developed integrates cost relaxation, greedy heuristic and a modified stepping stone method into the GA framework to further explore the solution search space. Comparative studies were also conducted to test the performance of the HGA solution with the classical Lagrange heuristics developed and CPLEX. Results obtained suggests that the performance of HGA is competitive with that obtainable from a commercial solver such as CPLEX. / Thesis (PhD)--University of Pretoria, 2019. / Industrial and Systems Engineering / PhD / Unrestricted

State exploration in directed software model checking is guided using a heuristic function to move states near errors to the front of the search queue. Distance heuristic functions rank states based on the number of transitions needed to move the current program state into an error location. Lack of calling context information causes the heuristic function to underestimate the true distance to the error; however, inlining functions at call sites in the control flow graph to capture calling context leads to exponential growth in the computation. This paper presents a new algorithm that implicitly inlines functions at call sites to compute distance data with unbounded calling context that is polynomial in the number of nodes in the control flow graph. The new algorithm propagates distance data through call sites during a depth-first traversal of the program. We show in a series of benchmark examples that the new heuristic function with unbounded distance data is more efficient than the same heuristic function that inlines functions up to a certain depth.

The quality and reliability of software systems, in terms of their functional correctness, critically relies on the effectiveness of the testing tools and techniques to detect errors in the system before deployment. A lack of testing tools for concurrent programs that systematically control thread scheduling choices has not allowed concurrent software development to keep abreast with hardware trends of multi-core and multi-processor technologies. This motivates a need for the development of systematic testing techniques that detect errors in concurrent programs. The work in this dissertation presents a potentially scalable technique that can be used to detect concurrency errors in production code. The technique is a viable solution for software engineers and testers to detect errors in multi-threaded programs before deployment. We present a guided testing technique that combines static analysis techniques, systematic verification techniques, and heuristics to efficiently detect errors in concurrent programs. An abstraction-refinement technique lies at the heart of the guided test technique. The abstraction-refinement technique uses as input potential errors in the program generated by imprecise, but scalable, static analysis tools. The abstraction further leverages static analyses to generate a set of program locations relevant in verifying the reachability of the potential error. Program execution is guided along these points by ranking both thread and data non-determinism. The set of relevant locations is refined when program execution is unable to make progress. The dissertation also discusses various heuristics for effectively guiding program execution. We implemented the guided test technique to detect errors in Java programs. Guided test successfully detects errors caused by thread schedules and data input values in Java benchmarks and the JDK concurrent libraries for which other state of the art analysis and testing tools for concurrent programs are unable to find an error.

This thesis explores the role aesthetics plays in informational websites. In commercial interfaces, aesthetics (the perceived visual appeal and appropriateness of an object) has shown to correlate positively with many aspects of usability and emotional satisfaction. This thesis examines whether aesthetics has similar positive correlations in informational websites. Heuristics or guidelines for evaluating informational websites are developed based on empirical research and practitioner expertise. Categories for heuristic evaluation include usability, credibility, visual clarity, visual richness, and emotional satisfaction. A class of graduate students browsed three academic websites, evaluated them, and critiqued the heuristics. Results indicate that aesthetics does correlate with overall impression, usability, satisfaction, and credibility. The data also suggests that there are two dimensions of aesthetics: visual richness and visual clarity. Overall impression correlated with the average of all categories. The heuristics used in this pilot study are now ready to be tested on a larger population.

Research on opportunity recognition and entrepreneurial cognition suggests that entrepreneurs are likely to use and potentially benefit from heuristics (Baron, 1998, 2004; Busenitz & Barney, 1997). Some heuristics, particularly well-refined and accurate prototypes, may be valuable to entrepreneurs in recognizing opportunities (Baron, 2004). I seek, however, to consider how other types of heuristics that lead to irrational, biased, and inaccurate judgments (e.g., the betrayal heuristic) relate to opportunity recognition (Baron, 2004; Kahneman & Lovallo, 1993). I specifically consider the underlying causal process through which the use of these types of heuristics diminishes the ability to recognize opportunities. I posit that these heuristics reduce the ability to recognize opportunities by causing entrepreneurs to consider less information regarding potential opportunities. Further, I propose two individual differences that allow certain entrepreneurs to mitigate the negative effect that these bias-causing heuristics have on entrepreneurs' ability of form the belief that they have recognized an opportunity. I test my theory with two experimental designs that use a product from a technology transfer office that has been licensed by entrepreneurs and applied to a real-world market. This allows me to isolate the underlying variables of interest and to affix my theorizing to a well-documented phenomenon (the licensing and application of tech-transfer technology/products by entrepreneurs) (Gregoire & Shepherd, 2012; Mowery, 2004; Shane, 2001). Results show that some heuristic may cause individuals to consider less information about an opportunity, which reduces their likelihood of forming an opportunity recognition belief. Post hoc analyses suggest that this indirect effect may be conditional on how reflective an individual is and that entrepreneurs may be more reflective than non-entrepreneurs. The major contribution of this dissertation is to examine the theoretical underpinnings as to why certain types of heuristics inhibit entrepreneurs from forming the belief that they have recognized an opportunity. Specifically, I suggest and show that bias-causing heuristics reduce the amount of information that entrepreneurs consider about an opportunity and, as such, inhibit opportunity recognition beliefs. Second, I provide some support for the notion that reflective individuals are more likely to form the belief that they have recognized an opportunity because they consider more information about the opportunity when they initially rely on a bias-causing heuristic. Lastly, this dissertation provides initial support for the notion that entrepreneurs may be more reflective than non-entrepreneurs. Overall, I hope to point out that although a heuristic-dependent processing style has been shown to be beneficial with regard to opportunity recognition (Baron, 2004), the failure to consider the downside of certain heuristics and benefits related to overcoming these heuristics may limit our understanding of the opportunity recognition process.

This thesis explores the critical role of usability in software development and uses usability heuristics as a cost-effective and efficient method for evaluating various software functions and interfaces. With the proliferation of software development in the modern digital age, developing user-friendly interfaces that meet the needs and preferences of users has become a complex process. Usability heuristics, a set of guidelines based on principles of human-computer interaction, provide a starting point for designers to create intuitive, efficient, and easy-to-use interfaces that provide a seamless user experience. The study uses Jakob Nieson's ten usability heuristics to evaluate the usability of Stack Overflow posts, a popular Q\&A website for developers. Through the analysis of 894 posts related to usability, the study identifies common usability problems faced by users and developers, providing valuable insights into the effectiveness of usability guidelines in software development practice. The research findings emphasize the need for ongoing evaluation and improvement of software interfaces to ensure a seamless user experience. The thesis concludes by highlighting the potential of usability heuristics in guiding the design of user-friendly software interfaces and improving the overall user experience in software development.

The topic of exposure to childhood adversity has comprehensively been explored, and the effects of childhood trauma can undoubtedly impact a person on a lifelong continuum, still, investigation is unceasingly conducted. Much research has yet to be done when considering early adversity's outcomes on various stages throughout life and just how reoccurring consequences from early trauma may impair regulatory abilities and executive functioning beyond childhood. Research objective is to discover how early adversity influences later executive functions in conjunction to heuristics and the effects of hypervigilance in a college student populace. This study aims to examine the several variable differences from internal beliefs to external actions when considering the discrepancies between those with adversity to those without, and what factors may play a key role in harboring resilience through a quantitative experimental study. The importance of creating awareness for those impacted by early adversity is vital for building advocacy within society. The proposed study can potentially provide knowledge towards issues related to the influence of early adversity in Adulthood. Parallel to analyzing how early adversity impacts later life, examining student populations can provide findings that help depict in what ways the related factors influence trajectory both in academia and personal development and what role education may play.

Graph search is used in many areas of computer science. It is well-known that the scalability of graph-search algorithms such as A* is limited by their memory requirements. In this dissertation, I describe three complementary strategies for reducing the memory requirements of graph-search algorithms, especially for multiple sequence alignment (a central problem in computational molecular biology). These search strategies dramatically increase the range and difficulty of multiple sequence alignment problems that can be solved. The first strategy uses a divide-and-conquer method of solution reconstruction, and one of my contributions is to show that when divide-and-conquer solution reconstruction is used, a layer-by-layer strategy for multiple sequence alignment is more memory-efficient than a bestirst strategy. The second strategy is a new approach to duplicate detection in external-memory graph search that involves partitioning the search graph based on an abstraction of the state space. For graphs with sufficient local structure, it allows graph-search algorithms to use external memory, such as disk storage, almost as efficiently as internal memory. The third strategy is a technique for reducing the memory requirements of sub-alignment search heuristics that are stored in lookup tables. It uses the start and goal states of a problem instance to restrict the region of the state space for which a table-based heuristic is needed, making it possible to store more accurate heuristic estimates in the same amount of memory. These three strategies dramatically improve the scalability of graph search not only for multiple sequence alignment, but for many other graph-search problems, and generalizations of these search strategies for other graph-search problems are discussed throughout the dissertation.

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