Master Projects & Major Papers

Permanent URI for this collectionhttps://hdl.handle.net/10155/77

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Black male school leaders: a scoping review of K-12 leadership experiences
(2024-08-01) Cousins, Roy; Petrarca, Diana
Background: School leadership is broadly studied and critical to students’ achievement and their overall well-being. However, the race-neutral position of traditional leadership theory fails to account for the intersecting identities of school leaders and the influences on the enactment of leadership (Smith, 2021). This review critically examines school leadership based on the intersecting identities of Black Male K-12 school leaders, as existing research specific to this group is limited (Bass, 2020). Objective & Design: This review explores the scope and range of literature documenting the experiences and leadership enactment of Black male school leaders in Canada and the United States, using a scoping review methodology (Joanna Briggs Institute, 2024) and the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews (PRISMA-ScR) (Tricco et al., 2018). Utilizing the framework of Critical Race Theory (CRT), school leadership is examined with consideration specific to the experiences and professional practices of Black male K-12 school leaders influenced by their identity. Results: This scoping review resulted in 45 total sources of evidence consisting of 10 peer-reviewed journal articles and 35 dissertations, published between 2002 and 2023. Only one and a half of the 45 studies reviewed were Canadian-based, and all but one study incorporated qualitative methodologies (e.g., phenomenological approaches). Most of the studies typically centred on a small number of participants who shared their leadership experiences within the context of their intersectional identities. While researchers studied a range of questions, common themes centred on the leadership approaches that differed from European American school leaders and challenges and outcomes related to the underrepresentation of Black males in K – 12 school leadership. Findings also revealed that Black, male K - 12 school leaders experienced and enacted leadership that differed from Eurocentric norms based on their intersectionality. Several implications for research, practice, and policy are identified, including: the need for Culturally Responsive School Leadership within school leadership programs; enhanced professional development for all school staff regarding anti-Black racism and systemic barriers that marginalize some groups of students; foci on recruitment, support, and retention of Black, male teachers and administrators; and continued research related to Black, male, K - 12 school administrators and leadership within the Canadian context is needed. Conclusion: The intersectionality of Black school leadership, though underrepresented in research, is a crucial field of study. Understanding the characteristics of effective practices, particularly those that promote inclusive perspectives from marginalized and historically oppressed groups, can impact school leadership and student outcomes. This study reinforces the essential role of the identity of Black male school leaders in all aspects of leadership, particularly in reducing achievement disparities among Black students.
Polymorphic attack feature validation: bridging the gap between intrusion detection and evolving threats
(2024-08-01) Begwani, Raksha; Heydari, Shahram S.
This project focuses on enhancing the detection of polymorphic attacks, which can evade traditional Intrusion Detection Systems (IDS) by changing their form with each attack. While IDS are crucial for network security, their effectiveness diminishes against such dynamic threats. The project aims to identify key features exploited by polymorphic attacks, enabling the creation of a feature list to improve detection. Using the SlowHTTP tool for generating attack profiles and the LycoStand tool for essential feature extraction, this research seeks to develop effective mechanisms to analyze polymorphic attacks and its features, addressing the limitations of IDS in identifying these attacks.
Critical digital literacies used by adolescents and young adults with mental health diagnoses while consuming TikTok: a scoping review
(2024-07-01) Slongo, Lea; Morrison, Laura; Hughes, Janette
Teens and young adults with mental health diagnoses utilize social media for information related to their diagnoses and experiences. The design and operationalization of social media platforms can be both helpful and detrimental to this population. TikTok requires that adolescents and young adults have and utilize digital literacy skills to navigate content. This scoping review is interested in exploring how teens and young adults utilize critical digital literacy skills when navigating mental health information on TikTok. As this is an emerging technology and area of study, a large gap in the literature was identified as an opportunity for future research.
"Sounding out solutions: current status of ocean noise pollution and management approaches to conserve cetacean with the focus on Canadian habitats."
(2024-03-01) Nassim, Nakissa; Jawad, Dima
As human activities continue to rise, the adverse consequences of ocean noise pollution on cetaceans (Cetacean is the general noun used to describe all 90 species of whales, dolphins, and porpoises), including stranding, masking, alterations in foraging behaviors, and mating disruptions, have become a significant concern for marine conservationists. Despite international recognition, the complex nature of sound propagation in aquatic environments and diverse contributing sources have left various aspects of ocean noise pollution needing to be fully comprehended. This research project delves into the multifaceted impacts of underwater noise on cetaceans, underscoring the imperative to implement effective mitigation measures promptly. Key strategies, such as planning, resource management, raising stakeholders’ awareness and engagement, adopting noise reduction technologies, and integrating them with existing ones, establishing Marine Protected Areas (MPAs), and initiatives to decrease ship speed and reroute ship lanes, play a pivotal role in alleviating ocean noise pollution. The project emphasizes the importance of a holistic approach to address the issue, suggesting integrating noise reduction strategies into all conservation plans for seas and oceans. Recognizing the significance of engagement from both the public and private sectors, a fundamental aspect of this approach involves raising awareness to foster a shared sense of responsibility toward marine life. Furthermore, it advocates for intensified research endeavors to further our understanding of the specific impacts of noise pollution on distinct cetacean groups, enabling the refinement of mitigation strategies accordingly. This project underscores the urgent and collective need for immediate action to tackle ocean noise pollution. By adaptive management, amalgamating technological advancements, regulatory measures, and public awareness and engagement initiatives, we can promote more sustainable coexistence between human activities and the marine environment, ensuring the safeguarding and conservation of cetacean species in our oceans.
Enhancing nursing education: the role of emotional engagement in e-learning
(2024-06-01) Mnaymneh, Marvin; van Oostveen, Roland
This study at Ontario Tech University explores the impact of e-learning on nursing students' professional learning, focusing on digital literacy, emotional engagement, and satisfaction. Utilizing a mixed-methods approach, the research involved a Basic Demographic Survey (BDS), a Digital Competency Profiler (DCP) survey, and e-learning training sessions. Key findings indicate a positive correlation between satisfaction and perceived efficacy, with valuable and user-friendly e-learning platforms enhancing satisfaction. Emotional engagement, influenced by multimedia elements like videos and simulations, significantly impacted satisfaction. The study underscores the importance of incorporating emotional intelligence into e-learning design, recommending user-friendly, interactive e-learning environments to improve learning outcomes.
Multiscale video transformers for video class agnostic segmentation in an autonomous driving setting
(2024-08-24) Cheshmi, Leila; Siam, Mennatullah
Semantic segmentation is a key technique in the perception of autonomous driving. Traditional semantic segmentation models, however, are constrained by the need for extensive annotated datasets and struggle with unknown classes not encountered during training. On the other hand, video class-agnostic segmentation aims to segment objects without relying on their semantic category. Motion cues could be used towards that goal to account for objects outside the closed set of training classes. This project proposes an innovative approach to video class-agnostic segmentation in autonomous driving using multiscale video transformers. We enhance the Video Class Agnostic Segmentation (VCAS) dataset by integrating richer annotations and tracking data from the TAO-VOS (BDD) dataset, thereby providing a comprehensive dataset for better generalization in complex driving scenarios. Our project involves designing a novel multi-scale video transformer-based architecture that foregoes optical flow, focusing instead on learning motion implicitly to identify objects in a class-agnostic manner. This architecture utilizes the Multiscale Encoder-Decoder Video Transformer (MED-VT) framework, which processes sequential data in a multiscale approach to capture both fine and coarse-grained information. Video transformers utilize encoder and decoder components, along with attention mechanisms, to efficiently process sequential data. Our approach takes an input clip and outputs the class-agnostic segmentation of moving objects in that clip. Features extracted from the raw input clip using a convolutional backbone are treated as tokens and provided to the multiscale transformer for pixel-wise classification. Additionally, we augment the currently available video class-agnostic segmentation datasets with TAO-VOS (BDD) datasets. We also label some missing objects in TAO-VOS (BDD) datasets with a standard semantic segmentation annotation tool in a few of the sequences. The outcomes of this project include a more diverse and comprehensive dataset and a superior video class-agnostic segmentation model with improved accuracy in mean intersection over union (mIoU). Our training on datasets focused on autonomous driving scenes demonstrated a significant improvement in mIoU compared to models trained on general-purpose video object segmentation datasets.
Examining impact and perceptions: a literature review on instructor feedback strategies and English Language Learners writing performance
(2024-03-01) Sultan, Wejdan; Eamer, Allyson
Over the years, researchers have conducted empirical studies to investigate the impact of various instructor feedback strategies to enhance English Language Learners (ELLs) writing performance. The present study synthesizes the findings of 46 articles, including both past and current publications. While greater focus was put on corrective feedback strategies, alternative feedback approaches were also considered; overall, the findings confirm that all instructor feedback positively impacts students' writing performance, albeit to different degrees. Moreover, my research indicates that ELLs believe instructor feedback contributes to their writing development. When attempting to gauge students’ specific feedback preferences as well as examine the differential effects of feedback strategies, I found that the benefits of feedback interventions were contingent on and influenced by different variables. Pedagogical implications and areas for further research are discussed.
Dance and vlogs: creating pathways to STEM identity for marginalized girls
(2024-03-01) Nathan, Amy; Ruttenberg-Rozen, Robyn
Girls tend to start losing interest and confidence in their STEM talents in their adolescence and start dissociating themselves away from STEM. Research shows that alternative learning practices have been useful and been able to increase STEM exploration, engagement and support STEM learning. In this paper, I explore the impact of STEM learning from two alternative practices: dance and video logging to determine if there is a connection to STEM identity development of marginalized girls.
Drawing back the curtain: a scoping review of backchannel communication use in adult education environments 2001 to 2023
(2024-03-01) Byers Reid, Tracy; Morrison, Laura
Social interactions during the learning process encourage engagement and enrich the learning experience. Frequently, larger classes, student dispositional barriers, and time constraints in adult educational settings impact student interactions with peers or professors. This scoping literature review explores how educators and students leverage digital communication backchannels in adult educational environments to facilitate learning, participation, and engagement. By reviewing literature from 2001 to 2023, this paper uses a longitudinal approach to provide a comprehensive understanding of the required technology affordances, implementation practices, and instructional strategies for backchannel use in adult education.
Development and validation of scaled electric combat vehicle virtual model
(2023-11-01) Vaz, Glenn Xavier; El-Sayegh, Zeinab
This research focuses on an 8x8 scaled electric combat vehicle (SECV) and aims to create a virtual model made of the same vehicle on a vehicle dynamics simulation software using parameters from the actual vehicle. In the proposed vehicle, each wheel is independently driven and steered. MATLAB and Simulink software were used to design and implement the electric powertrain while TruckSim Modelling and Simulation software was used to simulate the on-road conditions tests. The simulation data was then compared with the experimental data obtained from the physical test scenarios.
Filtering honeywords using probabilistic context free grammar
(2023-10-01) Tanniru, Alekhya; Vargas Martin, Miguel
With the growing prevalence of cyber threats, effective password policies have become crucial for safeguarding sensitive information. Traditional password-based authentication techniques are open to a number of threats. The idea of honeywords, which was developed to improve password-based security, entails using dummy passwords with real ones to build a defence mechanism based on deceit. The importance of password policies is examined in the context of honeywords in this study, emphasizing how they might improve security and reduce password-related risks. We present the idea of using the existing passwords to extract a policy and using this policy to filter good and strong passwords. Through this capstone project, we aim to contribute to the broader understanding of honeywords and their role in improving password-based authentication systems. I have conducted experiments on Chunk-GPT3 and GPT 4 models, to see which one of the models produces more honeywords which are very similar to the real passwords.
Enhancing password security: a quest for optimal honeywords
(2023-10-01) Nety, Meher Viswanath; Vargas Martin, Miguel
In this capstone report, our primary focus is on harnessing the capabilities of the GPT4 model to enhance password security through the generation of honeywords. Honeywords are decoy passwords designed to strengthen the security of sensitive systems by confusing potential attackers. The utilization of GPT4, a powerful language model developed by OpenAI, offers a n innovative approach to this challenge. By directly generating honeywords without relying on password segmentation, GPT4 introduces a unique dimension to password security. This approach is particularly valuable in thwarting targeted attacks, as honeywords generated by GPT4 are designed to deceive potential attackers effectively. In addition to the exploration of GPT4, this report also delves into the realm of Chunk-GPT3. Chunk-GPT3, as detailed in previous research, employs advanced language models to generate honeywords through the segmentation of passwords into discrete chunks. These chunks are ingeniously recombined to form decoy passwords. The re-engineered Chunk-GPT3 approach incorporates enhancements to the password segmentation process, including ”mapping digits to alphabets” and ”removal of digits” functions. These modifications aim to produce more potent and effective honeywords, ultimately elevating password security. The report includes a comprehensive comparative analysis of honeywords generated by the original Chunk-GPT3 approach and the re-engineered Chunk GPT3 approach, as well as honeywords created by GPT4. By assessing the effectiveness of these honeyword generation methods using the HWSimilarity metric, the report provides valuable insights into the strengths and weaknesses of each approach. Examining the capabilities of both GPT4 and Chunk-GPT3 in the context of honeyword generation, this report aims to provide a holistic perspective on cutting-edge strategies for safeguarding sensitive data in the ever-evolving digital landscape.
Guarding the gate: using honeywords to enhance authentication security
(2023-10-01) Koppada, Gowtham; Vargas Martin, Miguel
A honeyword (false password) can be defined as a duplicate password (rearranged) resembling the same characteristics of the original password. It is very challenging for any cyberpunk to distinguish between a real password and honeyword (containing PI). Using HGT’s (honeyword generation technique), these honeywords are generated in lump sum and the hashed honeywords are placed in an organization database with triggers to identify breach before it’s too late. In accordance with the previous research, the concept of HGT’s might fail if the generated honeywords does not contain the personal information of the user, making it easy for the attacker to perform targeted attack. It is a good practice to include the chucks containing PI or part of the original password of that particular user in generated honeywords to make it look natural. In order to generate such honeywords with chunks, the concept of prompt engineering in LLM (Large Learning Models) is used. In this report, we tried to improve the existing prompt, making it easy for the LLM to get deep understanding and to produce better throughput. In addition to that, we compared the base GPT Learning model (existing) with the newly upgraded GPT models like GPT-3.5-turbo and GPT-4. Considering the ‘strength of password‘ as a base factor, we came up with results and statements stating which model outperformed the others.
Using the evidence-development-validation-consensus (EDVC) approach to develop an online training program for healthcare professionals and laypeople to provide outside-hospital cardiac arrest care in rural and remote places
(2023-09-01) Gino, Bruno; Dubrowski, Adam
Introduction: The COVID-19 pandemic exacerbated challenges in delivering cardiac arrest (CA) courses in remote and rural (R&R) areas and affected training for laypeople (LP) and healthcare professionals (HCPs). Due to the combined issues, medical education suffered, including the suspension of basic life support and defibrillation (BLSD) training. Materials and Methods: In this study, researchers developed an online training program via evidence-development-validation-consensus (EDVC) approach using a learning management system (LMS) model. Results: A comprehensive online training program should encompass cognitive, affective, and psychomotor learning domains, addressing various skills and knowledge aspects in BLSD training. Conclusion: The study presents the EDVC approach used to develop an online training program, enabling effective out-of-hospital CA care courses in R&R places. The program incorporates expert feedback and improves knowledge and techniques in automated external defibrillator (AED) delivered by drones use.
Co-designing instruction in virtual learning environments using AI
(2023-12-01) Ganesh, Aishwarya; Hunter, William
The literature was explored to determine how artificial intelligence (AI) systems and algorithms are currently being used in the co-design of learning within virtual learning environments. Through the analysis of literature, the study aims to retrieve multiple methods of AI assistance to ease or uplift the educator’s role in online learning design. The study determined a variety of themes that determine methods of AI use in online instruction, such as prediction, providing feedback, adaptive learning, and providing visualization of student data on learning management systems (LMS). The study also determined the importance of a repository of various student data input in AI algorithms, and the collaboration of educators and experts in the process of using AI systems. The key implications suggest the importance of bridging feedback immediacy and formative approaches to improving student performance in online environments. Furthermore, the study also determines the changing roles of stakeholders in the education process. Finally, it also suggests the potential to create a multifaceted AI system and an effective LMS that supports such features.
Instructional design and development tools for online adult education: a literature review
(2023-12-01) Elbaghdadi, Ziad; Hunter, William
This paper examines the intersection of online learning, adult learning theories, and instructional methods in adult education. Through a comprehensive literature review, it analyzes prominent adult learning theories and models, emphasizing their implications for designing effective learning experiences. The study also explores adult learners' unique characteristics and needs, addressing key features such as self-directedness, experience, intrinsic motivation, and external factors influencing learning. Additionally, the paper discusses instructional design methods for online adult learners and identifies key principles, including the importance of feedback, engagement through collaborative activities, and personalized learning experiences. The analysis covers experience-based learning, scaffolding, blended learning models, and the role of technology, with a focus on learning management systems. The study also explores the significance of online communities of practice for collaborative adult learning, emphasizing factors influencing engagement and success. Overall, the findings provide valuable information for educators, instructional designers, and researchers seeking insights into online education for adult learners.
Ontario Ministry of Education’s Policy/Program Memorandum 140: a review and critique of current resource allocation and practices for supporting autistic students in elementary classrooms
(2023-12-01) Conway, Kathleen M.; Power, Rob
A review of recent literature on Applied Behaviour Analysis (ABA) and alternative evidence-based therapies for autistic students, as well as data extracted from Ontario school boards’ recent financial reports. Information from this review is used to conduct a critique of Ontario's Policy/Program Memorandum 140 (PPM 140) by scrutinizing current funding models to determine whether they allow the policy to be implemented in a way that facilitates execution of Applied Behaviour Analysis (ABA) methods that align with best practice recommendations. The critique determines that most of Ontario’s school boards are currently underspending from their Behaviour Expertise Amount (BEA) allocations, which are intended to fund ABA training for educators, ASSD programs for students, and school board hiring of behaviour experts. Data reviewed as part of this critique revealed that half of Ontario’s school boards employ two or fewer full-time behaviour experts. Based on best-practice guidelines, this behavioural expert-to-student ratio makes the mandated requirement of integrating ABA methods into the IEPs of students unsustainable and likely means that autistic students are not receiving the required support. Underspending in ABA training for educators indicates that limited teacher-focused educational opportunities are available, creating a divide between policy and practice. The current approach to PPM 140 in Ontario schools renders genuine and authentic application of ABA principles difficult, if not impossible, to implement and indicates a need for improvements moving forward. This critique and review of the literature reveals that PPM 140 could be more efficient if new oversight and monitoring protocols could be developed, including standards and quality indicators used to establish training requirements for education staff, behaviour expert hiring practices that align with board enrollment numbers, and accessible data collection protocol to monitor implementation and to measure student outcomes.
The challenges and benefits of assistive technology and educational programs for educators, caregivers, and youth with multiple exceptionalities/special needs
(2023-08-23) Ivan, Paula M.; Hunter, William J.
Assistive technology (AT) is a process that provides opportunities for youth with multiple exceptionalities/special needs to learn, grow, and discover meaningful avenues in order to navigate through an evolving digital world. A growing body of research literature suggests that when assistive technology is introduced into the educational curriculum by teachers, included in the educational system by stakeholders, and made accessible in the learning community; it has the potential to enhance digital literacy, language, and numeracy skills for youth with multiple exceptionalities/special needs. Assistive technology also has the potential to augment cognitive development, language development, social development, and physical development, while improving the overall well-being of youth with multiple exceptionalities/special needs. This systematic literature review is qualitative by nature and seeks to explore the broad question, “what are the challenges and benefits of assistive technology and educational programs for educators and families of youth with multiple exceptionalities/special needs in the educational system?”
Substance use disorder education for emergency registered nurses
(2023-08-14) Shillington, Kelly; Lemonde, Manon
Patients with substance use disorders (SUD) constitute up to 1 in 11 emergency department (ED) visits in North America and this number increasing throughout the COVID-19 pandemic (Morin et al., 2017). The ED presents an excellent opportunity to intervene and improve care for patients with SUDs, however there is currently no required or expected level of competency in managing SUDs for registered nurses (RNs) working in an emergency setting. To inform an educational intervention for improving nursing care for patients with SUDs, this project aimed to leverage nursing leadership opportunities to understand the current competency and confidence amongst ED RNs and identify gaps to be ameliorated through continuing education and policy implementation.
Enhancing password security: advancements in password segmentation technique for high-quality honeywords
(2023-07-01) Sannihith Lingutla, Satya; Vargas Martin, Miguel
Passwords play a major role in the field of network security and play as a first line of defense against attackers who gain unauthorized access to the profiles. However, passwords are vulnerable to various types of attacks making it essential to ensure that they are strong, unique, and confidential. One of the major techniques that evolved over time to enhance password security is the use of honeywords that are decoy passwords designed to alert the administrator when a data breach has happened. The main goal of this project is to addresses one of the limitations of a honeyword generation technique, called Chunk-GPT3, by performing better password segmentation through a re-engineered chunking algorithm that maps digits into characters, and which would seem to lead to better honeywords. We justify our re-engineering method and generate honeywords that we compare to those generated by Chunk-GPT3. Nonetheless, after evaluating honeywords using the HWSimilarity metric, the results suggest that improved chunking does not necessarily lead to better honeywords in all cases.

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