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Exploring Innovation and Behavioral Flexibility in Captive Carnivores
No full textThe highly specialized order Carnivora, while underrepresented in the comparative cognition literature, faces diverse ecological and social constraints. Thus, species and individuals should differ in behaviors associated with cognitive abilities, facilitating the successful navigation of these challenges. Behavioral flexibility was measured in 80 individuals of 17 species through personality assessments predicting success on a multi-access puzzle box (MAB) and behaviors associated with cognition on both a MAB and an Impossible Task. At the species level, social species were significantly more persistent on the MAB, and smaller body mass and higher encephalization was associated with persistence and latency to success on the MAB. Within species, on the individual level, there were several significant differences in behaviors between the sexes, ambassador versus exhibit animals, and wild versus captive-born species in the behavioral trait assessments, and on the MAB and Impossible Task. No significant differences existed between individuals of different ages or species rated differently on the IUCN scale or in brain volume on the MAB. This dissertation contributes to the growing field of animal cognition through the first use of personality assessments predicting problem-solving success on an MAB, the largest and most inclusive sample of felids on an MAB, and the first use of an Impossible Task in non-domesticated felids
Kindergarten through Third Grade Classroom Teachers’ Use of Technology to Support All Students’ Reading Development
No full textThis study provides important insights about the dynamic landscape of technology integration in early childhood education, particularly its role in supporting reading development among students in kindergarten through third grade. The research methodology adopted for this study was a mixed-method approach, incorporating both quantitative and qualitative research methods. Quantitative data were collected through a survey distributed to K-3 teachers, while qualitative data were gathered through semi-structured interviews with selected participants. All eight participants had experience teaching struggling readers and students with Individualized Education Programs (IEPs), and the majority (75%) had experience teaching students with IEPs specifically for reading skills. The study also found that all participants used technology in their classrooms to develop students' reading skills, with varying degrees of intensity. The qualitative findings revealed six major themes, including personalization and differentiation, engagement and motivation, balance between technology and traditional methods, resources and professional development, accessibility and practicality, and support for diverse learners. The participants of this study emphasized the importance of technology in providing personalized learning experiences, promoting engagement and motivation, and enhancing reading instruction. However, they also expressed concerns about the limitations of technology, the need for balanced instruction, and the importance of professional development. The quantitative findings highlight the prevalence of technology use in reading instruction, while the qualitative findings provide insights into the complexities of technology integration. The findings also highlight the importance of professional development, resources, and accessibility in ensuring equitable opportunities for all students, which has implications for policy, practice, and future researc
Hidden Leaders: A Phenomenological Research Study that Examined the Journey of Resilient African-American Women Who Pursued and Continue to Pursue Advancement at a Predominantly White Institution
No full textABSTRACTIn this study, I focused on African-American women who have pursued and continue to pursue administrative roles at a predominately White institution in the Midwest. Using a phenomenology approach, that employed an initial questionnaire sent to various marginalized groups in the Midwest to women who fit the research criteria. After completing the questionnaire, the women who wanted to continue their participation did so by completing a pre-interview survey. Finally, 21 women were interviewed via virtual focus groups and one-on-one interviews using semi-structured questions. Through this study, the key findings revealed that these African-American women continue to experience barriers to gaining access to advancement or promotions at predominately White institutions. The findings revealed six major themes, (1) working harder and doing more work than their White counterparts, (2) a lack of acknowledgment and being overlooked, (3) a lack of transparency to be authentic self, (4) a lack of respect, (5) the need to use support systems, and (6) the need for institutions to increase support. Based on the findings from this research several recommendations for leaders of predominately White institutions on ways to support African-American women who seek career advancement are suggeste
Parents' opinions on using applied behavior analysis (ABA) to increase neurotypically developing children's level of physical activity
No full textPhysical activity is important to reduce the risk of several diseases and in some cases death. Early childhood theorists stressed the importance of physical activity, lowering its cost, and rewarding physical movements. ABA interventions were an essential part of this paper because of its importance in altering the behavior and keeping the desired change even after the intervention. In general, parents and teachers’ motivations and engagement with children were the most effective way to increase physical activity in both ABA and ECE articles. Results of this research found that general knowledge of ABA is low and there is a need to increase ABA awareness and usage in the society before directing toward specific interventions. Finally, the cost of ABA interventions without insurance coverage is astronomical, yet all children have the right to receive behavioral treatments and live in an altered environment that ensures them the best overall developmen
Model-based reinforcement learning for truck trailer robotics vehicles trajectory planning and control
No full textAdvanced Driver Assistance Systems (ADAS) has become increasingly important in the automotive industry, because of customers’ need for higher safety and efficiency for transportation. Truck-trailer systems, which occupies a large market share in both commercial and recreational sections, have been facing special challenges for ADAS due to the complex vehicle system dynamics and special skills required for maneuvering, especially in reverse. In the current market products, traditional control methods consisted of path planning and classical control methods have been widely used, however, they often struggle to adapt to the changing scenarios in real-world applications. On the other hand, with the development of Machine Learning, one of its research field, reinforcement learning, shows great potential in robotics and control area. The reinforcement learning algorithm is a semi supervised machine learning approach, and it can provide a flexible solution by enabling systems to learn and improve through experience, potentially conquer the limitations of tradional approaches. This dissertation explores the development of control systems for Truck-trailer Robotics Vehicle (TTRV) system, especially during reverse driving and parking in low-speed, closed, and and unstructured environments. The research addressed challenges caused by the complex dynamics of TTRV systems, which is known for its nonlinearity, nonholonomic constraints, and under-actuation behaviours. And this research applied several modified reinforcement learning approaches to trailer control, as well as tradional control methods, to show the improved ADAS capabilities of reinforcement learning approaches, and effectiveness of satisfying the growing need for intelligent transportation solutions.This dissertation begins with a review of classical control methods which has been used for truck trailer system, including pole placement, Lyapunov controller, Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and other nonlinear control methods. Multiple path planning approaches such as Dubins paths, Reeds-Shepp paths, and the A* algorithm are also applied and tested to the articulated vehicle. While these methods have shown capability in certain scenarios, changing environments, online calibration, and complex dynamics can make tradional algorithm struggle in real application situations. Moreover, these algorithms require pre-design and fine-tuning from engineers, which limits their application and self-evolution in real-world situations. All these problems slow down the popularity for advanced TTRV control systems in nowadays fast growing market. To overcome these limitations mentioned above, an end-to-end Deep Reinforcement Learning (DRL) network solution for TTRV reverse autonomous control is proposed in this dissertation. The study compares several DRL algorithms with different computational complexities and performances, including both on-policy and off-policy approaches, all of them showed the effective of DRL in solving the trailer control problem. Moreover, a hybrid model-based reinforcement learning approach is developed in this research, which integrates classical path planning techniques into the DRL algorithm to improve sample efficiency and performance. The proposed algorithm is tested in several osbtacle avoidance simulations, demonstrating its ability to handle target chasing, obstacle avoidance, and passenger comfort simultaneously. Also, an Immitation Inspired Probabilistic Ensembles with Trajectory Sampling (IIPETS) algorithm is developed on the base of Probabilistic Ensembles with Trajectory Sampling (PETS) algorithm, this model based approach significantly improved sample efficiency, and showed outstanding robustness and adaptability for controlling the truck trailer system. The results of this study contribute to the development of intelligent ADAS technologies for truck trailer systems, which can potentially satisfy the growing demand for efficient and safe transportation market needs. The developed Reinforcement Learning (RL) based controllers demonstrated the adaptation capability to changing scenarios and overcoming the limitations of classical control methods, paving the way for a more robust and flexible TTRV intelligent control systems
Exploring mediation in literacy development: peer, teacher, and robot interactions in early learning contexts
No full textThis dissertation presents three studies focused on mediated literacy interventions to support emergent bilingual and young learners' reading comprehension and vocabulary development. Chapter Two examines teacher-student interactions during read-aloud sessions, analyzing how specifice mediations enhance bilingual students' engagement and comprehension, as submitted to the Journal of Literacy Research. Chapter Three, published in The Journal of Literacy and Technology, explores how children learn to use digital features in app books during buddy reading sessions, emphasizing peer mediation's role in developing digital literacy skills. Chapter Four investigates robot-mediated read-alouds, focusing on the feasibility of a robot providing instructional support to young children, which highlights both opportunities and challenges in robotic engagement with literacy tasks. Collectively, these studies underscore the significance of diverse mediated learning approaches - teacher-guided, peer-supported, and robot-facilitated - in enhancing literacy outcomes, offering insights for educators and researchers in developing innovative, inclusive literacy intervention