Andragogy Content Knowledge Technology: A Training Model for Teaching Adults

abstract: ABSTRACT Professional Development (PD) is an important tool in the field of education. Successful PD programs are those that include adult learning methods and opportunities for experiential learning and discussion. The university where this action research was conducted does not offer formal training to adjunct instructors. The adjunct instructors are hired based primarily on their content knowledge. This research was conducted to understand, if the application of a blended training model for adjuncts influences the adjunct's perception of meeting their student's educational needs and the student's perception that their personal education needs are met. The blended learning included the delivery of a framework that incorporated Andragogy, Content Knowledge and Technology (ACKT). The purpose of the ACKT framework is to supplement adjunct's content knowledge expertise with adult learning methods and technology. The effectiveness of the framework was measured by using a quasi-experimental, pre to post intervention assessment. The treatment group and control group each contained twenty-two adjunct instructors from the university. The treatment group received training on the framework prior to commencing the class and participated in two focus groups during the semester. In addition, the treatment group was observed teaching in their classroom. The control group did not receive training, or participate in focus groups; however they were observed teaching in their classroom. The results of the action research showed significant improvement for the adjunct instructors in the treatment group. Specifically, knowledge of and application of andragogy showed a large improvement. In addition, the social influence of the adjuncts in the treatment group showed a large improvement. Less significant was the improvement in the efficacy of the students in the treatment group classes compared to those in the control group classes. However, the data suggests that the students in the treatment group better applied the content learned and they were more aware of other's educational needs than their peers in the control group. The study supports the need for adjunct instructor PD. Through a PD program adjunct instructors increase their own efficacy and this improvement translates into increased content transfer for the students in the classroom. Based on the strong evidence for adjunct instructor improvement this research will continue by expanding the blended learning model to more of the adjunct instructors at the university, and continuing to evaluate the effectiveness of the model in meeting student's educational needs.Dissertation/ThesisEd.D. Leadership and Innovation 201

Who am I?: culturally relevant pedagogy and the quest to transform teacher beliefs through professional development

The focus of this qualitative research study was to examine the impact professional development had on teachers’ perceptions and practices after participating in monthly year-long culturally relevant pedagogy and Common Core professional development. Culturally relevant pedagogy is the use of “cultural knowledge, prior experiences, frames of reference, and performance styles of ethnically diverse students to make learning encounters more relevant to and effective for them” (Gay, 2002, p. 29). The study described the experiences and practices of teachers before and after their participation in culturally relevant pedagogy professional development. Teachers were asked to describe in rich detail their interpretations, perceptions, and practices related to how they teach and build relationships with their students and parents, and their attitudes toward students of color and students experiencing generational and situational poverty. The participants were staff members at an elementary school in an urban school district in the southeastern region of the U.S. Participants were interviewed and completed a pre-and post-survey. The study describes the benefits of the culturally relevant pedagogy professional development from the perspective of the study participants. Barriers that hindered study participants from implementing culturally relevant pedagogy are highlighted as well. Based on these findings, recommendations for further study and policies to support educators as they learn and implement culturally relevant pedagogy are provided

Job satisfaction in young professional athletic trainers

Job satisfaction levels in young professional athletic trainers in multiple settings were examined quantitatively using the Job Satisfaction Survey (Spector, 1997) and qualitatively using an open-ended survey question. Job satisfaction levels were calculated to find that young professional athletic trainers are satisfied with their jobs according to the normative mean data for the scale. Post hoc analysis of the comparison of satisfaction levels by setting suggests that college/university athletic trainers have significantly lower job satisfaction levels than secondary school and clinic/outreach athletic trainers. Qualitative data suggests a high negative response rate revealing pay and operating procedures as two of the most important facets of job satisfaction. These findings suggest that a qualitative assessment of job satisfaction will provide better data for analysis regarding the job satisfaction of young professional athletic trainers

Policy Reservations: Early Childhood Workforce Registries and Alternative Pedagogy Teacher Preparation

Thesis (Ph.D.) - Indiana University, Education, 2015Due to narrowly defined quality measures, teacher preparation in Montessori, Waldorf, Reggio and LifeWays pedagogies is not recognized in many state ECE professional development systems. The problem is compounded by Quality Rating and Improvement System’s child care program ratings, which rely on teacher qualifications as a component of program ratings. Limitations, due to philosophical dissimilarities pertaining to the spirit of the child, ill-fitting measurements of quality, and policy exclusion make it difficult for alternative pedagogy communities to meet qualifications or to obtain scores that count. This is exacerbated by narrow definitions regarding national versus regional accreditation in teacher preparation programs.Using a transformative, mixed-methods approach, this study asks, “What is the role and relevance of alternative pedagogy teacher preparation to the professional development system, and where does it fit in the current policy landscape nationwide?” As a follow up question, the study seeks to answer, “What is the process for change?” Through the use of surveys, interviews, and a cultural context model, a way forward is mapped. Registry policy makers in 28 states and 46 teacher preparation directors, across three types of alternative-pedagogy teacher preparation programs, assisted in data collection, resulting in a recognition baseline. Public sources were used to triangulate a composite snapshot of this national policy situation, demonstrating appropriate policy inclusion in six out of 17 states’ career pathways and/or data collection in ECE workforce registries. Cumulative data revealed alternative pedagogy teacher recognition levels across the country and revealed how relevant policies evolved to become system inclusive. The study concludes by inviting community representatives to respond and to share their experiences and thoughts. Actionable study outcomes, community-developed recommendations, and an advocacy map were circulated in three of four alternative pedagogy communities. Using a cultural equity paradigm, the study elucidates power relationships between alternative pedagogy teacher preparation and national/state efforts towards ECE professional development and quality improvement policy systems, illuminating where federal and state policy/initiatives are shaping, responding to, and limiting the alternative-pedagogy teacher preparation pipeline in the United States. Recommended courses of action encourage policy collaboration and a cultural shift from policy power over, to power with policy

Early Hearing Screening Policy and Deaf Children’s Language Acquisition

The federal Early Hearing Detection and Intervention Act (EHDI) guarantees medical and communication interventions for deaf children and audiological, medical, and language intervention data collection. However, the policy and its implementation have not been analyzed in regard to policy goal attainment of deaf children’s language acquisition. A qualitative case study was conducted to analyze seven federal- and state-level early hearing screening policy websites and implementation and intermediary documents to assess the federal and state policy formation and implementation of EHDI. In addition to the document assessment, data were collected from interviews to obtain the perspectives of two early childhood educational program directors regarding the goal attainment of deaf children’s language acquisition. The conceptual frameworks guiding this policy analysis study were Nakamura and Smallwood’s policy environment model and Lenneberg’s language acquisition theory, specifically critical period of language acquisition theory. Policy content analysis was based on a document review of federal and state published policy documents for frequency and emphasis of hearing, speech, and language. The results of this case study indicate a slanted bias toward hearing instead of language within the early hearing screening policy and implementation. The results of this study could lead to potential implications of positive social change by assisting program implementers in addressing the language needs of deaf children and their families, which could lead to better academic outcomes for K-12 deaf children

New teacher development in an urban district : a mixed-method study of a new teacher induction institute as professional development.

Experts attribute the teacher shortage to an increase in population while others cite the retirement of a critical mass of baby boomers who came into teaching in the mid to late twentieth century. Other experts argue that the teaching shortage is due to attrition of teachers new to the field. Job dissatisfaction, disillusionment, poor working conditions, low pay and lack of respect are cited in research as reasons for an exodus from teaching. Numerous reasons have contributed to teachers leaving the field in alarming numbers, resulting in not enough pre-service teachers in supply to fill the current demand for teaching. This study examined the perceived effectiveness of a large urban school district\u27s New Teacher Induction institute by newly hired teachers who attended in 2008-09,2009- 10, and 2010-11. Quantitative and qualitative measures were used to examine if the district\u27s week-long induction program assisted newly hired teachers by providing support. A total of 1270 teachers were invited to participate in an online survey and focus interview groups, with 245 teachers responding to the survey and five focus group participants agreeing to interview. Descriptive statistics were analyzed using constructs of student learning, student needs, critical thinking, and instructional leadership strategies. A factor analysis was performed to look at newly hired teachers\u27 expectations and attitudes about the urban school district-wide new teacher induction program; differences in the perceptions of teachers attending the program across three years; differences in perceptions between traditional certification and alternative certification prepared teachers; and differences in perceptions of inexperienced new teachers and experienced new teachers. The researcher reduced the 28 online survey item responses and qualitative data analysis from five focus group participants to three factors and emergent themes: teacher efficacy, holistic teacher, and teacher leader. An ANOVA was run using demographic data from 190 respondents, with the three new themes as an analysis framework. The results indicated no significance at the p =0.05 level

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces

Interoperability Performance Among Campus Law Enforcement Agencies

The September 11, 2001 terrorist attacks exposed considerable breakdowns in communications interoperability and information sharing among first responders. Multijurisdictional responses to the active-shooter incidents at the University of Texas in 2010; Sandy Hook Elementary of Newtown, Connecticut in 2012, and the Reynolds High School shooting of Multnomah County, Oregon in 2014 were replete with interoperability failures as well. Recent multijurisdictional response events continue to illuminate difficulties with first-responder interoperability and minimal research exists to promote understanding of the interoperability challenges of university police departments. The purpose of this study was to explore the barriers that impede communications of campus based law enforcement agencies during multiagency or multijurisdictional response. General systems theory and the unified theory of acceptance and use of technology model provided the conceptual framework for this qualitative case study. Face-to-face interviews were conducted with 10 leaders of university public safety agencies in California. Data were collected, inductively coded, and thematically analyzed. Key findings indicate that participants perceived barriers of funding, policy, inclusiveness, and training that affect communications interoperability performance. The positive social change implications from this study include recommendations of policy change for improved interoperability during multiagency or multijurisdictional response which can contribute to increased first-responder safety, more efficient multijurisdictional response, and improved safety of students and society at large