Machine learning (ML) techniques have been successfully applied to a wide array of applications. This dissertation aims to take application data handling into account when developing ML-based solutions for real-world problems through a holistic framework. To demonstrate the generality of our framework, we consider two real-world applications: spam…

Mathematical reasoning, a fundamental aspect of human cognition, poses significant challenges for artificial intelligence (AI) systems. Despite recent advancements in natural language processing (NLP) and large language models (LLMs), AI's ability to replicate human-like reasoning, generalization, and efficiency remains an ongoing research…

The COVID-19 pandemic led to an increase in Online learning. With this increase comes several challenges for instructors and students such as connectivity issues and student engagement. Organizations are spending millions of dollars developing emerging technologies such as emotion AI and other AI applications without taking cognizance of users'…

The world has experienced rapid changes, leading to pressing issues such as environmental degradation, social inequality, and resource depletion. As a transdisciplinary field, evaluation has emerged as a crucial tool in addressing these challenges and promoting systemic change. However, concerns have been raised regarding the field's capacity to…

Research in artificial intelligence has made great strides in developing 'cognitive tutors' that teach a range of technical skills. These automated tutors allow students to practice, observe their mistakes, and provide personalized instructional feedback. Evidence shows that these methods can increase learning above and beyond traditional…

Artificial intelligence (AI) aims to build intelligent systems that can interact with and assist humans. During the interaction, a system learns the requirements from the human user and adapts to the needs to complete tasks. A popular type of interactive system is retrieval-based, where the system uses a retrieval function to retrieve relevant…

This research study employed quantitative and qualitative designs to explore the impact of immersive technology on the Computational Thinking (CT) capabilities of students enrolled in an engineering program at a Historically Black College or University (HBCU). The quantitative study in this research employs a survey design approach to explore the…

Since most worldly phenomena can be expressed via language, language is a crucial medium for transferring information and integrating multiple information sources. For example, humans can describe what they see, hear and feel, and also explain how they move with words. Conversely, humans can imagine scenes, sounds, and feelings, and move their…

Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming…

Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art…

New Jersey consistently ranks as one of the top-performing states in the country when it comes to education. Most recently, it was ranked third in the nation. A look at the state's standards in any subject shows the state works diligently to vertically align standards while also challenging students in order to better prepare them for the future…

Many problems related to synthesis with intelligent tutoring may be phrased as program synthesis problems using AI-style search and formal reasoning techniques. The _x000C_first two results in this dissertation focus on problem synthesis as an aspect of intelligent tutoring systems applied to STEM-based education frameworks, specifically high…

Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

Constraint satisfaction problems are a powerful way to abstract and represent academic and real-world problems from both artificial intelligence and operations research. A constraint satisfaction problem is typically addressed by a sequential constraint solver running on a single processor. Rather than construct a new, parallel solver, this work…

The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…