Information Technology Dissertations and Theses

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    Perceptions of tertiary educators and their students toward the integration of AI mentors to support learning
    (2019) Iles, Howard Robert Edward; Eastern Institute of Technology
    Artificial Intelligence (AI) is becoming more commonplace across many fields and education is no exception. AI use in general has issues with trust, privacy and real world workability, these same issues surface in the field of AI in education. Mentors also have a long history with education and the goal of individual mentors may well be achievable with the use of artificial intelligence. This study aims to explore the perceptions of tertiary educators and students toward the integration of AI Mentors to support learning. The quantitative case study used surveys to elicit teachers’ and students perceptions; the underlying paradigm that is used in this research is Bruno Latour’s Actor-network theory (ANT). What was found is that both students and staff were open and positive about technology, but what did surface was that student had a stronger sense of technical understanding and at the same time were more cynical and standoffish about the use of AI technology in education. The implication is that there is a discord between what the students and teachers views of AI technology in education are – further study should be carried out to pinpoint these issues if AI in education is to flourish.
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    Modernising traffic flow analysis: A computer vision-driven prototype for vehicle detection
    (2020) Bakker-Reynolds, Gabrielle; Eastern Institute of Technology
    RESEARCH QUESTION How can computer vision be used to support traffic flow analysis? RESEARCH SUB-QUESTIONS What is required within the planning, development and implementation phases of a prototype that performs accurate vehicle detection? How will the performance of a vehicle-detection prototype be measured? What are the barriers to the development of a vehicle-detection prototype? Computer vision holds the capabilities of performing duties by replicating tasks that the human visual system accomplishes. For this reason, computer vision is being employed as a tool to modernise and advance the management of traffic on a global scale. Traffic management remains an issue in many regions of the world, evidenced by barriers such as street obstacles, inefficient road signals, vehicles speeding, traffic congestion, and underdevelopment of freeways. Due to this, computer vision-driven management systems have been developed to combat such problems, demonstrated by their role in travel assistance and navigation, parking management and enforcement, real-time traffic control, and license plate recognition (Buch et al., 2011). Subsequently, this research explores how vehicle detection can be applied to support traffic flow analysis within Hawkes Bay, New Zealand through the use of computer vision approaches. This research assumes a design-based approach, comprised of two iteration-based approaches employed to develop a prototype for vehicle detection utilising an Nvidia Jetson Nano. The approaches are analysed according to accuracy, processing time, cost, and overall suitability. Results show that the prototype has great potential as an alternative approach to current traffic flow analysis. Finally, recommendations are offered for future research and other users working with similar devices.
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    Case study analysis of blended-delivery and cloud-based learning elements and resources for an IT degree programme
    (2021) Navaneethakrishnan, Saranya Devi; Eastern Institute of Technology
    RESEARCH QUESTION: How ready are the courses in an NZ case study IT degree program to be delivered via Moodle online and/or remotely? ABSTRACT Advances in web and cloud technologies have made it easier for higher education institutions to use interactive technologies. Indicators for engagement and achievement include the time students spend in the online learning environment and how frequently they communicate. Effective and well-structured interactive activities that fit with the educational outcomes of the courses may improve student retention. Pedagogy and curriculum design are becoming increasingly important to educators and researchers in blended and online learning, as these are expected to influence students’ engagement and performance. This research report makes a new contribution by focusing specifically on one case study undergraduate computing programme – the design of the courses, and its influence on the learning behavior of students. This research report also aims to look at online and cloud-based learning resources in the New Zealand context at a tertiary institute. The initial literature review is related to cloud-based education articles, as well as reviewing videos and tutorials that either suggests best practices or deliver key course knowledge. The research approach is a mixed-method case study, including both qualitative and quantitative approaches. The methods include content analysis of course websites and documents, as a qualitative immersion and reflection, in addition to descriptive statistics related to the same courses. The findings of this study suggest that curriculum design and online learning elements are key to predicting student success, especially in courses lined up for faster and remote delivery.
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    A pilot programme of fruit grading system using computer vision
    (2021) Wang, Liang; Eastern Institute of Technology
    RESEARCH QUESTIONS: 1. How can New Zealand’s agriculture industry use computer vision technologies to improve the accuracy of their fruit grading system? 2. What are the similarities and differences in object detection and image classification effectiveness in grading fruits? 3. What factors influence agricultural companies’ decision on embracing computer vision for fruit grading in New Zealand? ABSTRACT: Traditional fruit grading work depends on a large labour force during harvesting time. However, the grading accuracy varies, resulting in difficulties in product quality management. Due to the COVID-19 pandemic, many of New Zealand's farming industries lack seasonal workers from overseas. Hence, they are looking at taking advantage of promising computer vision technologies to avoid reliance on the labour-intensive grading method. This research project designs a prototype fruit grading system using object detection algorithms to automatically sort fruits (e.g., squash), minimising manual intervention during the production process. The proposed system consists of fruit handling and image processing modules. Amazon's machine learning platform SageMaker and Google's machine learning framework TensorFlow are the two main software components in the system. We tested the prototype in a simulated production environment. The result proved that the selected approach could suit farming industries to achieve automation transformation during post-harvesting. Other findings include that object detection has better performance than image classification on identifying defects on fruits. The high cost of setting up a new fruit grading system has hindered agriculture companies from adopting the plan. Further research within this topic could incorporate farming experts to help gain higher dataset accuracy during labelling jobs and choose a proper approach to avoid unnecessary difficulties in manipulating data between different machine learning platforms.