Analytics of Capstone Projects: Understanding Outcomes Through People, Products, And Processes

The capstone project is a bridge from the university to industry. During the project, students not only learn the process of integrating previously learned engineering concepts into an actual product but also practice essential professional skills such as communication, teamwork, and project delivery. How can universities ensure effective capstone student learning and good outcomes? To answer this question, the capstone project is studied as a system comprising of products, people, and processes. People refer to the students and instructors involved. Students develop products by following development processes. Instructors guide students using instruction processes. In educational context there limited control over the students enrolling for the course, instructors available to guide the students, and products that client-sponsors want students to develop. Consequently, development and instructional processes emerge as the key levers through which the capstone system can be improved. Currently, at RIT like many other schools, a single standard development process is used to develop all types of products and a single instructional process is used to teach all students. Early experiments provided evidence of correlations between certain student characteristics and project outcomes, disparities in student experiences, and the potential effectiveness of process interventions in improving outcomes. The quality of outcomes improves when a capstone system can anticipate and address challenges. These findings motivated the central research question: Can poor student learning outcomes be predicted based on student and product characteristics? If so, what specific factors are associated with increased risk? Using machine learning, this research successfully predicts poor student learning outcomes in a capstone course based on product and student characteristics. This study adopts a supervised learning approach using Decision Trees and Random Forest to predict student learning outcomes, which are measured using the standardized rubrics. Input features include product and student characteristics available at the start of each project. Product characteristics include factors such as the type of product being developed and the type of client organization. People characteristics encompass data sets like students’ academic performance, demographic information, personality types, and instructor background. Out of eleven student learning outcomes, six were predicted with a recall of at least 0.75 and an F1-score of at least 0.60. Successfully predicted outcomes were applying engineering design to solve problems, experimentation, written communication, oral communication, teamwork, and independent learning. Risk factors systematically leading to bad outcomes include type of product, type of client organization, student personality, and instructor characteristics. These findings confirm that a single standard capstone process does not work equally well for everyone. In light of these findings, attention turned to identifying process customizations that could prevent poor outcomes. Using qualitative methods, the experiences of expert project guides were elicited. The findings revealed that the standard capstone process is already being implicitly customized. Additionally, certain products possess characteristics that demand greater design effort to resolve ambiguity and uncertainty. During the interviews, project guides emphasized the importance of explicitly confronting problems. Each guide helped student teams overcome emergent technical and non-technical contextual challenges in a distinctive way. This doctoral research advances knowledge at the intersection of integrated product development, capstone education, and applied machine learning. It provides new insights into how process customization can serve as a mechanism to improve student learning outcomes, and introduces methodological innovations that leverage existing student datasets to develop an early warning system for identifying poor outcomes. Through a mixed-method investigation, the study explores how customizing processes can help improve capstone project outcomes. This work lays a strong foundation for future studies and offers potential applications in both research and pedagogy. Ultimately, this work emphasizes the importance of evidence-based approaches in improving systems that involve both people and products, and underscores the importance of interdisciplinary perspectives in addressing the challenges of tomorrow

An Investigation of Active Inference for Reinforcement Learning Control

This thesis investigates the application of active inference framework on different reinforcement learning (RL) tasks. We specifically consider the following OpenAI Gym environments: CartPole, MountainCar, and LunarLander. In this thesis, our primary goal is to explore whether active inference could provide better performance and stability compared to traditional RL methods. The proposed model consisted of three main components: a variational autoencoder (VAE) model to infer hidden states, a transition model predicting latent states, and a Double deep Q-network (Double DQN) as the actor selecting optimal actions. To achieve this, extensive experiments are carried out using grid searches across several hyperparameters, including learning rate, discount factor gamma, KL-divergence weights and soft update factor tau. Models achieving stable and rapid convergence across multiple trials were selected as optimal. Custom reward shaping techniques were implemented for more challenging environments such as MountainCar and LunarLander. The experimental results demonstrated that while the active inference agent successfully achieved the desired performance thresholds in each environment, its performance was not stable, often increasing early before subsequently decreasing. This behavior suggested issues related to catastrophic forgetting where the agent might implicitly treat different state regions as separate tasks, continuously overwriting previously beneficial policy parameters. Elastic weight consolidation (EWC) was explored to solve the instability issue. However, incorporating EWC yielded limited improvement, suggesting that the instability could originate from factors beyond traditional catastrophic forgetting. These results indicate that active inference, combined with Double DQN, is capable of effectively solving standard RL tasks. However, challenges remain in terms of policy stability. Therefore, it is important to conduct further research to understand and overcome these instabilities as it potentially deliver great utilities to solving more complex tasks with active inference

Predictive Policing - Leveraging CCTV Data and AI for Crime Hotspots

This thesis explores fine-grained sentiment analysis in the context of police social media posts, leveraging hybrid approaches to manage the rapid flow of big data and mitigate its negative impact on youth. As social media becomes a primary medium for public interaction, understanding sentiment within police-related posts is crucial for law enforcement agencies to gauge public opinion and address community concerns effectively. The rapid dissemination of information and the potential for negative sentiments to spread swiftly pose significant challenges, particularly for young audiences. The research begins with a comprehensive review of existing sentiment analysis techniques and their applicability to large-scale social media data. The study then develops and evaluates hybrid models that combine lexicon-based methods with machine learning approaches to achieve a more nuanced understanding of sentiment in police-related posts. The proposed models are tested on a dataset of social media posts, demonstrating their ability to accurately classify sentiments while handling the complexities of big data. Results indicate that the hybrid approach not only improves sentiment classification accuracy but also effectively processes large volumes of data in real-time. The study further explores how these insights can be used to counteract the negative influence of social media on youth, proposing strategies for early detection and intervention. The findings of this research contribute to the field of sentiment analysis by offering a robust solution to the challenges posed by big data in social media. Additionally, the thesis provides practical recommendations for law enforcement agencies on how to utilize sentiment analysis to foster positive community relations and safeguard youth from harmful online content

Utilizing Natural Language Processing to Optimize Business Processes

This Capstone project explores the potential of Natural Language Processing (NLP) techniques in optimizing decision-making and organizational workflows across various domains. The Capstone project uses three case studies to demonstrate how sentiment analysis, frequency analysis, and topic modeling can analyze unstructured textual data to provide insightful findings. The first case study evaluates employee satisfaction using sentiment analysis, uncovering trends across departments and roles to guide targeted organizational interventions. The second case study focuses on student feedback at a higher education institution, using sentiment and frequency analyses to identify key areas for improvement in academic programs and services. The third case study leverages advanced topic modeling techniques to analyze thematic trends in artificial intelligence (AI) research over a decade, providing strategic insights into emerging innovations and priorities. The findings highlight the efficiency and scalability of NLP techniques, with automated processes completing tasks in seconds or hours that would otherwise take weeks or months manually. The research emphasizes the importance of selecting models and techniques, including embedding models, clustering methods, and preprocessing approaches that are specifically tailored to the task and organizational needs to ensure detailed, relevant, and easily interpretable outcomes. While limitations remain, including the need for end-to-end pipelines to make these techniques accessible to non-technical users, this Capstone demonstrates the transformative role of NLP in enabling organizations to harness the power of unstructured data for strategic planning, resource allocation, and innovation

Fostering Human Rights Awareness in Kosovo: A Case Study on the Sustainability of NGO Trainings

The sustainability of human rights trainings in Kosovo remains a critical issue, particularly as the country seeks to develop a rights-oriented society aligned with Euro-Atlantic values. This case study evaluates the sustainability efforts of two trainings conducted by a non-governmental organization, focusing on their alignment with objectives, participant feedback, and systemic factors such as institutional support and follow-up mechanisms. It also explores broader challenges like participant disengagement and the limited integration of human rights education into the national curriculum. A mixed-methods approach was used, combining survey data from participants, interviews with NGO representatives, and analysis of internal documents. To ensure consistency, two trainings from the same 2023 project were selected. Despite efforts to center participant perspectives, the study faced methodological challenges, including low response rates, unavailability of certain stakeholders, and limited geographic and demographic diversity – factors that influenced the depth and generalizability of findings. Key results show the trainings were more effective at refreshing knowledge and influencing behavior than delivering new content. Training A demonstrated moderate sustainability efforts, reinforcing professional skills but exhibiting limited broader impact. Training B demonstrated moderate to high sustainability efforts by targeting educators as multipliers; however, barriers such as rigid curricula, limited resources, and insufficient follow-up mechanisms constrained its reach. The study concludes that sustainability hinges on systemic reforms, follow-up mechanisms, participant engagement, and stronger collaboration between NGOs, donors, and educational institutions. Future efforts should prioritize continuous monitoring, integrate sustainability into donor criteria, and expand access to educational resources. Addressing curriculum rigidity and building institutional partnerships will be key to ensuring these trainings foster lasting societal change

Engineering Tunable Collagen Fiber Alignment Gradients to Quantify Contact Guidance Effects on Cell Migration

Directional migration of cells is one of the most important physiological processes, necessary for tissue and organ development. It also plays a crucial role in pathological conditions such as cancer metastasis. This directed migration is influenced by various factors present in the native environment, which guide cells to migrate steadily in a specific direction. Due to its importance, over the years, a lot of emphasis has been placed on understanding and exploring the biochemical and biophysical factors that direct cell migration. In addition to the commonly studied biochemical and biophysical cues, contact guidance from collagen fibers, in the native matrix, has also been shown to influence cell motility. However, current studies treat the arrangement of collagen fibers in a binary manner, either as unaligned or aligned fibers. In the native matrix, however, these fibers gradually transition from an aligned to an unaligned state, creating a gradient in fiber alignment. Since it is difficult to engineer these fiber alignment gradients at in vivo length scales, their role in guiding cell migration remains unexplored. Understanding how cells sense and respond to these cues is crucial for developing physiologically relevant in vitro models and advancing tissue engineering applications. To address this shortcoming, I first developed a microfluidic device that could replicate collagen fiber alignment gradients at physiologically relevant length scales. Using this microfluidic platform, I investigated the effects of these engineered gradients on both single cells and cell clusters. Both single cells and cell clusters exhibited a preferential migration towards increased fiber alignment. Cancer cells demonstrated a 2.6-fold directional bias, while endothelial cells displayed 2 times higher directional persistence on the fiber alignment gradient. Finally, I employed time-lapse imaging to understand the mechanism behind this preferential migration. Cells seeded on a gradient initially extended protrusions in multiple directions but preferentially stabilized those pointing toward higher alignment, resulting in cell polarization towards regions of increasing alignment. This polarization may be responsible for driving the observed directional migration bias. This work establishes a model system that allows investigation of fiber alignment gradients as a potent guidance cue for directed cell migration. Furthermore, this microfluidic device is a versatile platform that can be easily modified to incorporate additional native gradients. This can facilitate studies on how cells interpret simultaneous multiple directional cues

Amorphous Oxide Semiconductor Materials and Devices for Monolithic and Heterogeneous Integration

The expanding consumer electronics industry has spurred significant advancements in display technology. Thin-Film Transistors (TFTs) serve as active matrix switching components in LCD and OLED panels. Amorphous metal-oxide semiconductors (AOS) support large-area deposition at low temperatures and boasts an electron mobility many times greater than that of amorphous silicon. This work presents a comprehensive study on AOS materials and devices for their introduction to display, monolithic integration, and heterogeneous integration applications. Key studies have focused on advancing the state of (Indium Gallium Zinc Oxide) IGZO TFTs by addressing challenges in device uniformity, reliability, and modeling. Device uniformity was improved by modifying process parameters to allow for higher degree of film uniformity during deposition. Devices fabricated with this modified process demonstrate exceptional resistance to the application of traditional bias stress. Application of intensive bias and illumination-bias stress treatments led to distinctive transfer characteristics, differing in shift magnitude, distortion, and hysteresis behavior. Silvaco TCAD and a previously defined mobility model were used to simulate this behavior and explore the defect states created during intensive bias stress. Utilizing ion implantation for self-aligned source/drain regions present a path towards sub-micron device scaling. Past reports have demonstrated boron implanted self-aligned TFTs with excellent on-state and off-state performance. However, when subjected to thermal stresses above 175ºC the device transfer characteristics gradually shift over time. From this work it is hypothesized that this instability is related to the implanted boron dose, with higher doses presenting more shifting. Interpretation of electrical results suggests that boron can exist in two states: an active form that bonds with interstitial oxygen, increasing oxygen vacancies and enhancing conductivity, and an inactive form as an isolated interstitial atom. At low boron doses, the active state is dominant, improving conductivity, whereas at higher doses, the inactive state prevails, leading to reduced current and, in extreme cases, charge injection degradation. A thermally-activated diffusive mobility and percolation theory are two contending processes that have been proposed to govern electron transport in IGZO. This work builds upon previous investigations on the temperature dependence of channel mobility in IGZO TFTs, where transport behavior from 170 K to room temperature (RT) is clearly described by a thermally-activated diffusive mobility. The isolation of thermally dependent mechanisms via TCAD enabled the separation of the intrinsic and extrinsic components of the observed field-effect mobility. The methods used resulted in a quantitative assessment of the thermally-activated diffusive mobility and the free/total charge ratio. These advancements allowed for the development of a platform to realize the heterogeneous integration of µLEDs on an IGZO TFT backplane. µLEDs were successfully transferred onto an IGZO TFT backplane using a micro transfer printing technique employing a PDMS stamp. Metal deposition techniques were investigated for post passivation anneal interconnects between printed µLEDs and the completed IGZO backplane. Emission of single pixel and RGB pixel circuits was confirmed and a new RGB pixel was designed to minimize pixel cross-talk and sub-pixel leakage. While IGZO is the only AOS technology that has matured enough for commercialization, the electron channel mobility (≈10 cm2/Vs) presents a limitation in its application in back-end-of-line (BEOL) monolithic and heterogeneous integration. As such, alternate candidate AOS materials exist that exhibit channel mobilities 2-3x higher than that of IGZO. Studies in alternate AOS materials such as Indium Tungsten Oxide (IWO), Indium Tin Gallium Oxide (ITGO), and Indium Gallium Zinc Tin Oxide (IGZTO) have been conducted. The investigation on IWO TFTs revealed an unusual metastable device behavior dependent upon annealing temperature. This is believed to be the first report of such behavior, as published works adhere to either a low-temperature or high-temperature regime. The investigations into ITGO and IGZTO serve as preliminary studies; device characteristics support the claims of high channel mobility; however, the influence of defect states clearly indicates the need for further process development. The advancements realized in IGZO TFTs in this work will serve as a foundation for these alternative AOS materials

Heritage in Search of a Home: Archiving the Learning Designs and Artistry of Instructional Television of the 1970s and 1980s

This chapter tells intertwined stories of the rescue and preservation of two archival collections of instructional (classroom) television programs and attendant materials from the ITV new wave of the 1970s and 1980s. A.I.T., the Agency for Instructional Television/Technology created innovative programming combining instructional design and media artistry, all focused on learners. The ability, afforded by these collections, to address key lacunae in the history could have significant implications for the design of learning media in its current forms of interactive media and video games for learning as well as the development of the next evolution of learning media and technology

Prediction of Vessel Arrival Time to Optimize Berth Allocation in Ports Using Machine Learning Methods

80% of the world trade is carried out through the sea, which shows the importance of maintaining transportation efficiency in the maritime industry. The vessel arrival time and berth allocation pose a significant challenge in the marine field\u27s day-to-day operation, especially when a high number of vessels are waiting at the anchorage to deliver the goods on time. This increases the pressure on the responsible stakeholders and good owners as some cargo must be delivered urgently. Artificial intelligence and machine learning play a vital role in improving the operation of different fields; utilizing such technologies in the maritime industry will facilitate operations and increase efficiency. In this work, the vessel arrival prediction was tackled through implementing different machine-learning models. The historical data was collected from The Norwegian Base Station and Satellites between August 1 and September 24, 2024. Different preprocessing techniques were utilized to clean the dataset and prepare it for modeling. The three models built are Gradient Boosting Regression, K-Nearest Neighbors (KNN) Regression, and Random Forest Regression. Random Forest Regression showed better results than the other two models with R2 value equal to 0.704 and MAPE of 0.0285%