Master of Science, Miami University, 2024, Electrical and Computer Engineering

In this thesis, a thorough investigation into the optimization of user connectivity in ad hoc communication networks using robust policy creation and intelligent UAV location in stochastic environments is presented. In order to handle the dynamic and decentralized character of ad hoc networks, we identified the optimal UAV positions by applying a multi-agent deep Q-learning technique. To train stochastic environment-adaptive policies, a novel simple algorithm was devised with an emphasis on the usefulness of these policies under different scenarios. Through an empirical investigation, the study offered information on the generalizability and adaptability of learnt behaviors by examining how well policies based on one distribution of settings performed when applied to different, unseen distributions. In this thesis, we also explored the resilience of UAV networks against jamming attempts and propose a method for unaffected UAVs to self-adjust their placements. This approach ensured optimal user coverage even in adversarial situations. By demonstrating the potential of machine learning techniques to maximize network performance and enhance user connectivity in the face of environmental uncertainties and security risks, these contributions will collectively advance the field of UAV-assisted communication.

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

Recent decades saw a huge increase in the number of personal devices, wearables, edge devices, etc which led to increased data collection and increased connectivity at the edge. This collected data can be used to make insights about health, the economy, and business and help us make better decisions at the individual, organizational and global levels. With the proliferation of these devices, there are also numerous challenges associated with making use of these devices and the data to train useful models. The challenges could be due to privacy regulations or other constraints determined by the particular learning setup. These constraints make it difficult to extract the required insights from the data and the edge systems. The goal of this thesis is to understand these challenges or resource constraints and develop efficient algorithms that enable us to train models while adhering to the constraints. This thesis makes the following contributions: 1. Propose an efficient algorithm FedCMA for model heterogeneous Federated Learning under resource constraints, showed the convergence and generalization properties, and demonstrated the efficacy against state-of-the-art algorithms in the model heterogeneity setting. 2. Proposed a two-timescale aggregation algorithm that does not require the knowledge of the number of adversaries for defending against Byzantine adversaries in the distributed setup, proved the convergence of the algorithm, and demonstrated the defense against state-of-the-art attacks. 3. We highlight the challenges posed by resource constraints in the Offline Reinforcement Learning setup where the observation space during inference is different from the observation space during training. We propose a simple algorithm STPI (Simultaneous Transfer Policy Iteration) to train the agent to adapt to the changes in the observation space and demonstrated the effectiveness of the algorithm on MuJoCo environments against simple baselines.

Ph.D., Antioch University, 2017, Leadership and Change

The purpose of this theoretical case study was to explore the lived experiences of members within an inter-professional team about how they learn and adapt while dedicating their lives toward the well-being of students residing in and attending a rehabilitation home school. Although there is broad literature that addresses legacy learning theories and frameworks, as well as complex-adaptive organizations, very little shows how the application of rhizome philosophy principles address learning and adapting within an organizational context. This study is a step toward addressing that gap. Using interviews, thematic analysis, and storyline networking, the study explored in depth the lived experiences of 16 administrative, therapy, and educational staff who worked at the school. By using organizational storytelling as a means to unearth and analyze the team members' 194 stories, a rich web of connection and awareness emerged. Their stories demonstrated new ways of being, learning, and adapting both within and outside the school, and revealed alignment with rhizome philosophy principles of connection, multiplicity, heterogeneity, a signifying rupture(s), and cartography, as well as alignment with legacy and traditional learning theories and frameworks, thereby offering a new lens of learning within organizations called, Rhizomatic Learning in Organizations (RLO). This study is an opportunity to expand and enhance ways of considering learning and adapting within organizations by introducing and supporting rhizomatic behaviors and principles within collectives as they work together. This dissertation is available in open access at AURA: Antioch University Repository and Archive, http://aura.antioch.edu/ and Ohiolink ETD Center, https://etd.ohiolink.edu/

Doctor of Philosophy, The Ohio State University, 2009, ED Physical Activities and Educational Services

The purpose of this study was to investigate the relationships among investment in workplace learning, organizational perspective on human resource development (HRD), organizational outcomes of workplace learning, and organizational performance using the 2005 and 2007 Human Capital Corporate Panel (HCCP) surveys in Korean companies. The conceptual model proposed that investment in workplace learning was assumed to influence organizational outcomes of workplace learning, which affect in turn organizational financial performance. In addition, organizational perspective on HRD was expected to moderate between investment in workplace learning and organizational outcomes of workplace learning. The current study utilized nationally-representative datasets from the 2005 and 2007 HCCP surveys in South Korea to examine these relationships specified in the model at organizational level. In addition, the data were analyzed using structural equation modeling. The results showed a significantly positive relationship between investment in workplace learning and organizational outcomes of workplace learning. The current research also found a significantly positive relationship between organizational outcomes of workplace learning and organizational performance. In addition, the mediating effects of organizational outcomes of workplace learning were identified between investment in workplace learning and organizational performance. However, the moderating effect of organizational perspective on HRD did not exist in the relationship between investment in workplace learning and organizational outcomes of workplace learning. Moreover, this study compared two groups, manufacturing industry and non-manufacturing industry, to determine whether the conceptual model proposed in this study was the same for both the manufacturing industry and the non-manufacturing industry, using multiple-group SEM models. The results showed that there was a statistical difference in terms of the fit in the (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Educational Studies

As technologies emerge and create new job roles requiring new expertise, workplace learning and the role of instructional designer have become increasingly important. This study seeks to better understand the work of high-performing instructional designers and the ways in which they successfully create training materials to meet specific educational or performance objectives, specifically examining how instructional designers are trained for their work, how they interpret theories related to learning, to what extent those theories are consciously applied in the development of learning experiences, and how designers evaluate and engage with emerging technologies. Following an interview-based qualitative research methodology, the study combined a “think-aloud” strategy with stimulated recall, asking interviewees to share their reasoning for various design decisions while exploring an instructional artifact, something the participants had previously designed, such as a training website, an e-learning module, or an online course. In discussing their thinking and the motivations behind the various design choices, the 12 research participants—high-performing or expert-level instructional designers—illustrated how many of the “best practices” employed by instructional designers are supported by educational research, even when the designers themselves are unaware of the evidence supporting their use. These participants also shared how they approach professional development, highlighting the wide array of venues and sources for professional development, from podcasts and learner-directed study to conference presentations and practitioner-focused publications. Designers also shared how they evaluate emerging educational technologies, such as generative artificial intelligence, and how they determine whether such technologies might be useful for either the creation or delivery of educational experiences and training materials. The analysis of the discussion showed a spectrum o (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Educational Studies

As mobile learning (m-learning) becomes increasingly prevalent in education, it is recognized for its potential to enhance the overall quality of teaching and learning. Despite the many benefits, m-learning apps often experience low retention rates, which directly impede learners' benefit from using them and cause a waste of resources in app design, development, and maintenance. To investigate the critical factors influencing learners' acceptance of m-learning outside the classroom, this study introduced a novel model, the Mobile Learning Acceptance Determination (mLAD) Model, based on the Technology Acceptance Model and the updated DeLone and McLean Information System Success Model. Through the mLAD model, the study identified the critical app quality factors that influence learners' acceptance of m-learning. The moderating effects of the type of m-learning apps on learners' acceptance of m-learning were also revealed. An online questionnaire named the m-Learning Acceptance Questionnaire (mLAQ) was developed and disseminated through Amazon Mechanical Turk. A total of seven hundred forty-seven adult learners in the U.S. participated in the study. The descriptive statistical results of the examined factors revealed that m-learning apps available in the market demonstrate high mobility and content quality. Still, their interactivity and service quality could be improved. Furthermore, the results of the structural equation modeling analysis indicated that learners' two beliefs, perceived usefulness, and perceived ease of use, are the two essential determinants of learners' intention to use m-learning apps outside the classroom. Quality factors, such as content quality, interface design, mobility, and service quality, are the antecedents of learners' m-learning acceptance, given that they significantly and directly influence perceived usefulness and ease of use and indirectly impact learners' intention to use m-learning apps through learners' two beliefs. Through (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2023, Higher Education (Education)

In response to the COVID-19 pandemic, Spring 2020 saw many of the United States' estimated 19.9 million college students (National Center for Education Statistics [NCES], 2019a, Table 303.60) and 1.5 million educators (National Center for Education Statistics [NCES], 2019b, Table 315.10) experiencing remotely delivered curriculum. Higher education (HE) seeks to shape socially responsible leaders for sustainable professional practice (Chan, 2016; Moosmayer et al., 2018). However, in the case of nursing education which prepares future professionals through theoretical and practical training (Berndtsson et al., 2020), educators struggled to provide practical experiences via electronic resources (AACN, 2020a; Swift et al., 2020). This study will explore the nursing educator's decision-making process of selecting effective remote learning experiences to meet the required practical learning needs of the undergraduate nursing student.

Doctor of Philosophy (PhD), Ohio University, 2021, Instructional Technology (Education)

The current context of learning spaces is guided by several factors but most importantly, from student opinions and perspectives. This study explores and addresses constructivist pedagogical practices and students learning experiences in higher education. Precisely, the dissertation attempts to find out and to understand the importance and the roles of collaborative learning spaces and the challenges students face in those learning spaces. First, the study uses relevant theoretical frameworks of constructivism (constructivist theory of learning) and distributed cognition (collective intelligence) to highlight the importance of understanding the big picture of focusing on students developing their own knowledge and understanding of the world in an environment that encourages engagement and collaborative learning. To further understand the concept of these constructivist practices, this study directed its investigation to the analysis and the interpretation of documentation that relates directly to the study both physical and online. The documentation inquiry includes the mission statements of the space, flyers, posters, training materials and official websites affiliated to the collaborative learning space. The next inquiry was through an online open-ended questionnaire that was directed to learners in the collaborative learning space. Here, the study was interested in understanding learners' experiences in the space from their point-of-view. This was done by first, probing on the physical structure and the design of the learning space, safety, comfort and impact the space has to their learning and engagement in search for knowledge and skills. Secondly, the examination was on various ways in which students learn, interact, and actively collaborate and the challenges involved in the space. Seven themes emerged after an inductive data analysis from the documentation and online qualitative survey: Collaborative learning, flexible learning environ (open full item for complete abstract)

Master of Sciences, Case Western Reserve University, 2021, EECS - Computer and Information Sciences

A way of giving prior knowledge to a reinforcement learning agent is through a task hierarchy. When collecting data for offline learning with a task hierarchy, the structure of the hierarchy determines the distribution of data. In some cases, the hierarchy structure causes the data distribution to be skewed so that learning an effective policy from the collected data requires many samples. In this thesis, we address this problem. First, we determine the conditions when the hierarchy structure will cause some actions to be sampled with low probability, and describe when this sampling distribution will delay convergence. Second, we present three biased sampling algorithms to address the problem. These algorithms employ the novel strategy of exploring a different hierarchical MDP than the one in which the policy is to be learned. Exploring in these new MDPs improves the sampling distribution and the rate of convergence of the learned policy to optimal in the original MDP. Finally, we evaluate all of our methods and several baselines on several different reinforcement learning problems. Our experiments show that our methods outperform the baselines, often significantly, when the hierarchy has a problematic structure. Furthermore, they identify trade-offs between the proposed methods and suggest scenarios when each method should be used.

Doctor of Philosophy in Urban Education, Cleveland State University, 2020, College of Education and Human Services

A review of the literature documents two critical facts regarding the status of online education research. First, there exists minimal research on the instructional impact of online learning in K-12. Second, the focus of this limited K-12 research compares the growth outcomes of online learning to the growth outcomes of traditional face-to-face instruction. Therefore, the research found in this dissertation is unique as it is limited to examining the effect of time-in-school on high school students engaged in blended learning. the findings of this study are based on two years of data from a charter school that utilized a blended learning curriculum. The study compared the academic gains of sixteen treatment groups (students whose in-school attendance met specific percentage of time-in-school) to the academic gains of the corresponding sixteen control groups (students whose in-school attendance did not meet specific time percentages). These findings document that the academic gains of students in the study's sixteen treatment groups were statistically greater (<.001) than the academic gains of students in the sixteen control groups. While it is acknowledged these study's findings must be confirmed or refuted through additional research, this study's importance is the identification of an instructional strategy which has the potential of increasing, through personalized scheduling, the academic achievement for all students enrolled in a blended learning high school. Therefore, this study's findings should be of great interest to both blended learning practitioners and educational policy creators.

MA, Kent State University, 2017, College of Arts and Sciences / Department of Psychological Sciences

Rats have the capacity to extrapolate a known sequence of events to anticipate a novel item. We examined whether or not rats can extrapolate a serial pattern during a muscarinic cholinergic challenge. Adult male and female rats learned to nosepoke a sequential pattern of responses in a circular array of 8 receptacles attached one each to the walls of an octagonal chamber. This training pattern consisted of seven 3-element chunks of a rule-based serial pattern, namely, 123-234-345-456-567-678-781. On the day after meeting a high criterion on the training pattern, rats were given i.p. injections of 0.6 mg/kg scopolamine hydrobromide, a muscarinic cholinergic blocker, before encountering patterns consisting of the 7-chunk training pattern plus an added eighth chunk. The added chunk was either consistent with pattern structure (chunk “812”) or contained a terminal element that violated pattern structure (chunk “818”, where the violation element is underlined). Under scopolamine, and even while showing scopolamine-induced impairments of performance throughout the pattern, rats in both groups extrapolated known pattern structure in the novel added chunk, producing approximately 60% rule-consistent “2” responses on the terminal element of both types of chunks. Thus, despite scopolamine exposure, both male and female rats extrapolated well-learned pattern structure to a new chunk. Whereas earlier work showed that muscarinic cholinergic suppression had little effect on rule learning during acquisition of a pattern, the current study demonstrated that intact muscarinic cholinergic neurotransmission is not necessary for extrapolation of a well-learned rule to a novel chunk.

PHD, Kent State University, 2024, College of Education, Health and Human Services / School of Teaching, Learning and Curriculum Studies

The purpose of this self-study was to examine my enactment of 3S Understanding, a holistic, democratic and inquiry-based curriculum theory, in a university-based, English-to-speakers-of-other-languages (ESOL) writing course grounded in Subject Learning, Self Learning, and Social Learning. Through disciplined reflective inquiry and collaboration with critical friends, this study set out to unpack the complexities of course planning and teaching. Data collection and analysis involved a structured, five-stage approach to identify themes, compare relationships, and interpret findings within the 3S framework. The Subject Learning findings stressed identifying the “wiggle room” for teaching artistry to address students' learning challenges and advocated for a multimodal approach to accommodate diverse learning needs. These findings also highlighted the significance of reflective inquiry in improving pedagogical practices. The findings on Self Learning emphasized fostering self-awareness and autonomy among students through reflective practices and empowering activities. They also highlighted the value of incorporating personal stories into teaching to strengthen teacher-student connections. The Social Learning findings foregrounded the importance of a collaborative, authentic, and critical thinking-focused educational environment to deepen learning and prepare students for societal participation. This study emphasizes a holistic ESOL pedagogical shift, urging teachers to integrate comprehensive, reflective, and collaborative approaches. It recommends inquiry-based, reflective practices for teacher educators, and supportive, diverse teaching environments by administrators. Learners are encouraged towards active, self-reflective engagement, connecting learning to real-world relevance. Such an approach aims to enhance language proficiency and democratic participation, fostering a deeper understanding and engagement in ESOL education across various educational roles.

Doctor of Philosophy, The Ohio State University, 2023, Educational Studies

COVID-19 pushed online learning to a greater level of prominence in 2020 faster than anyone could have ever predicted. This sudden push highlighted the level of unpreparedness of many educators to effectively teach in online learning environments possibly due to their teacher education training. Demonstrating empirical best practices for effective online education for teachers is rarely done but necessary as we emphasize that the possibilities of online learning are infinite including creating opportunities not possible, practical, or requiring different approaches than in physical classrooms. In this dissertation, I investigated if commonly recommended active learning and design best practices combined with supports to enhance Vygotsky's popular Zone of Proximal Development (ZPD) theory can create an effective online learning environment for teacher candidates (TCs) and are ZPD supports an additive theory in the design of online learning environments. Seventy-one TCs from a large mid-western university in the United States completed online learning modules designed using a leading online course design platform (Articulate 360), with learner-centered active learning activities, authentic assessments, and supports including tutorials, animations, and case studies. TCs completed a pre-post multiple-choice quiz and evaluated the online learning experience, using two validated surveys (course design and active learning) and a ZPD survey created for this study. Findings indicate that upon completing and evaluating the modules TCs perceived the active learning components (feedback, interest, interactive engagement, and problem-solving) and the online learning environment principles implemented in the intervention as present, and they had a positive perception of the design of online learning modules. Feedback and interactive engagement positively predicted problem-solving and were significant predictors however, feedback was only significant when used as a sole predict (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Mechanical Engineering

Traditional design tasks and manufacturing systems often require a multitude of manual efforts to fabricate sophisticated artifacts with desired performance characteristics. Engineers typically iterate on a first principles-based model to make design decisions and then iterate once more by manufacturing the artifacts to take manufacturability into design consideration. Such manual decision-making is inefficient because it is prone to errors, labor intensive and often fails to discover process-structure-property relationships for novel materials. As robotics is an integral part of modern manufacturing, building autonomous robots with decision-making capabilities is of crucial importance for this application domain. Unfortunately, most of the robots and manufacturing systems in the industries lack such cognitive abilities. We argue that this whole process can be made more efficient by utilizing machine learning (ML) approaches, more specifically by leveraging sequential decision-making, and thus making these robots, design processes and manufacturing systems autonomous. Such data-driven decision-making has multiple benefits over traditional approaches; 1) machine learning approaches may discover interesting correlations in the data or process-structure-property relationship, 2) ML algorithms are scalable, can work with high dimensional unstructured problems and learn in highly nonlinear systems where a model is not available or feasible, 3) thousands of man-hour and extensive manual labor can be saved by building autonomous data-driven methods. Due to the sequential nature of the problem, we consider reinforcement learning (RL), a type of machine learning algorithm that can take sequential decisions under uncertainty by interacting with the environment and observing the feedback, to build autonomous manufacturing systems (AMS). Unfortunately, traditional RL is not suitable for such hardware implementation because (a) data collection for AMS is expensive and (b) tradit (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Educational Studies

This multi-component study assesses perceived mechanisms through which undergraduate and graduate college students use social media and learning management systems (LMS) through multiple regression path modelling. The literature review outlines current work related to investigating the mechanisms of learning through LMS and social media, and proposes a new cybernetic model focusing on interplay between design constraints and user agency on online platforms. Using an existing, validated scale that measures design constraints and perceived social connection and exploration on social media, the first part of this study revalidates the existing scale with 302 college students, and adapts it to create and validate another instrument that measures user perceptions of their agency on LMS tools using Confirmatory Factor Analysis. The second part of the analysis in this study involves a platform level understanding of the use of social media and LMS in terms of social connection, exploration and design constraints, and placing these interrelationships within a framework of topology, abstraction, and scale. It also measures relationships across these platforms, through the use of legacy dialogs. The third part of the data analysis in this study focuses on the construction of multiple regression path models investigating general level mechanisms of social connection, exploration and design within and between social media and LMS. Results reveal that the ethos of community formation that drive the creation of problem-solving environments in social media settings and on LMS tools are fundamentally different; requiring educators to create activities that mirror the spontaneous agencies displayed by users on social media tools in the classroom. An interview tool is created based on results, to inquire further into students' perceived bond formation on varied informal, formal, and non-formal platforms.

EDD, Kent State University, 2023, College of Education, Health and Human Services / School of Foundations, Leadership and Administration

Due to the COVID-19 global pandemic, the higher education undergraduate student body and the structure of course offerings have drastically changed. As we continue to transition from emergency remote courses to quality online learning experiences, we must respond thoughtfully as well as empirically. The purpose of this study was to examine quality learning experiences as perceived by students in online courses and use the new knowledge generated to add to current research and enhance practice. Through qualitative data collection, I intended to answer the following research questions: (a) What do students perceive as quality learning experiences in their online courses? and (b) How can understanding the student experience and perception of quality in their online learning environments improve course development procedures in online courses? Eight participants were interviewed regarding their experiences of quality in online courses. Results of the study determined Barriers, Interaction, Structure, and Community are the themes that students identify when defining quality online learning experiences. It was also indicated that students were more satisfied with their high quality online learning experiences. Implications and recommendations for improved practice and action steps are also provided. The researcher contends that these findings demonstrate the need to systematically build quality into online courses, which will offer students better learning experiences.

Doctor of Philosophy (PhD), Ohio University, 2022, Instructional Technology (Education)

Learning spaces in recent times are seeing a shift from traditional classrooms that are instructor led to innovative spaces that are student-centered. This paradigm shift is also seeing the emergence of informal learning spaces that are unstructured and allow for student collaboration. Using the universal design for learning (UDL) as a theoretical framework, this qualitative study examined the design of a collaborative learning space and how the space support or promotes student learning. Data collection techniques used for the study were semi-structured interviews with nine participants recruited as key informants, secondary data, and site observation. To analyze the data collected, Creswell's (2014) three step approach was employed. The first step involved organizing and preparing the data and this was done by downloading the auto generated transcripts and checking it against the recorded interview videos. It also involved removing filler words from the transcripts. Generating broad themes was done as the second step in the analysis process and a total of nine broad themes were generated. For the final step, the transcribed document was imported into Nvivo and codes or sub-themes were created for each broad themes and to address the two research questions. Findings from this study revealed that design decisions made 4 were intentional in promoting collaboration among students. The findings also showed that the flexible setup and multi-purpose use of the spaces appeal to diverse users and supports the principles of UDL. In terms of learning theories, constructionism and constructivism were presented and encouraged. An area of concern identified was limited staffing in running the space and was further worsened by layoffs of full-time staff during the Covid-19 pandemic. The concluding part of the study discussed the implications of the study and direction for future research.

Doctor of Education, Miami University, 2022, Educational Leadership

The purpose of this study was to build upon existing research that explored teachers' professional learning expectations and how teachers can utilize social media platforms or social learning environments to aid their professional learning. This information may be used to support thinking differently about time and space for both student and adult learning. Understanding to what extent and why teachers engaged in professional learning experiences in a social media environment can inform future learning options in utilizing these asynchronous platforms. Data generated may aid in the design of engaging professional learning experiences, through social media, that give teachers a venue for rapid, focused, personalized, and asynchronous learning. This qualitative study was limited to a non-random sample of interview participants, which ensured participants had a guaranteed proficiency in using social media environments for professional learning experiences. A survey was conducted to identify individuals who actively engaged in using social media platforms for professional learning, and six qualifying educators were invited to expand upon their experiences through their participation in semi-structured interviews. The open-ended questions inspired a dialogue about their lived experiences, resources located on social media platforms, and interests regarding professional learning during the 2020 pandemic time frame. Responses to the interview questions were coded to examine how and to what extent the teacher participated in a social media platform as a venue for professional learning during the pandemic. A theoretical, thematic analysis was used to identify how teachers participated in a social media environment for professional learning. The responses were coded based on CHAT's Four C's of Participation Taxonomy: Contemplator, Curator, Crowdsourcer, or Contributor (Trust, 2017). Additionally, the responses were coded to identify the type of informal learning experience th (open full item for complete abstract)

Master of Sciences, Case Western Reserve University, 2022, EECS - Computer and Information Sciences

This thesis studies Object-Oriented Markov Decision Processes (OO-MDPs), which extend MDPs with prior knowledge about the shared dynamics of similar objects in the environment. Existing work presents model-based algorithms that leverage the properties of OO-MDPs and adhere to the Knows What It Knows (KWIK) framework. In practice, models may not be easy to estimate and the KWIK framework may still lead to slow performance in a reinforcement learning context. In this thesis, I first introduce a new model-free learning algorithm for OO-MDPs based on Q-Learning. Though my approach is not KWIK, I show empirically that it exhibits significantly faster convergence than the KWIK and flat baselines. Next, I extend hierarchical reinforcement learning (HRL) to use OO-MDPs in the same manner. HRL uses a task hierarchy as prior information to reduce the overall problem into a set of smaller tasks. I show that HRL and OO-MDPs have a natural synergy, and I propose a novel model-free OO-HRL algorithm. I show empirically that this algorithm has better sample complexity than either HRL or OO-MDP algorithms alone.

Doctor of Philosophy, The Ohio State University, 2021, Computer Science and Engineering

Ranking is a fundamental problem that has been extensively studied in the machine learning community due to its broad applicability to different application areas such as recommendation, web searching, social choices, and crowd sourcing. The problems of ranking focus on finding the full ranking or partial rankings (e.g., top-$k$ selection) of a set of items from noisy observations. The items may refer to choices, movies, pages, and advertisements and the observations may refer to the queries about the users' preferences on these items. In many systems, the observations can be done in an active (or adaptive) manner, i.e., for each observation, the learner can adaptively choose items to observe according to past observations. An interesting problem is to understand the number of observations needed (aka sample complexity) for finding the ranking under the active setting. In this dissertation, we present new algorithms and derive new sample complexity upper and lower bounds that improve the state-of-the-art for three significant and fundamental problems in this topic. This dissertation explores the following problems: I. Exploring top-fraction arms in stochastic bandits. The stochastic multi-armed bandit (MAB) model is a classical online learning model for studying sequential decision making under uncertainty. In an MAB model, there are multiple arms (each refers to an action or an item), and each sample (aka pull) of an arm returns an independent reward according to this arm's unknown latent distribution. The first problem is to find $k$ distinct arms that are approximately within the top-$\rho$ fraction with respect to the mean rewards of the arms with error tolerance $\epsilon$ and confidence $1-\delta$. We develop algorithms and analyze the sample complexity lower and upper bounds for four variations (the number of arms is infinite or finite; the threshold of the top-$\rho$ fraction is known or unknown). The upper bounds and lower bounds of all four variations (open full item for complete abstract)