The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in Educational Administration Quarterly (EAQ) from 1965 to 2014. Given the computationally intensive data analysis required by probabilistic topic models, relying on high-performance computing, we used a 10-fold crossvalidation to estimate the model in which we categorized each article in each year into one of 19 latent topics and illustrated the rise and fall of topics over the EAQ's 50-year history. Findings: Our model identified a total of 19 topics from the 1965 to 2014 EAQ corpus. Among them, five topics-inequity and social justice, female leadership, school leadership preparation and development, trust, and teaching and instructional leadership-gained research attention over the 50-year time period, whereas the research interest appears to have declined

ABSTRACT:123

Purpose: Given the essential role of theories in research, this study aims to identify the theories and concepts undergirding the educational leadership research, illuminate the interconnections among them, and examine the evolution of the theoretical groundings of the field from 2005 to 2014. Methods: This study constructed a concept co-occurrence network, in which the nodes represent all framing concepts that theoretically framed the 1,328 articles published in four leading educational leadership research journals (EAQ, JEA, EMAL, and JSL) over the last decade, and the ties link the concepts that co-occur in an article. The reference frequency and centrality measures were used to identify the influential concepts. Next, the k-core analysis was performed to visualize the interconnections among the concepts. Moreover, a series of network cohesion measures were used to detect the changes in conceptual cohesion over the last decade. Findings: While 295 framing concepts guided educational leadership empirical studies, a small number of concepts exerted disproportionately large influence on the research. Further, these influential concepts closely interplay with one another, and the strongest interconnection was seen between the concepts of leadership approaches and organizational perspectives. Lastly, the increasingly pluralistic theoretical foundation did not yield the growing conceptual cohesion in educational leadership. Implications: This study for the first time elucidates the structure and evolution of the theoretical groundings of educational leadership research, laying the foundation for further theory development and inviting researchers to bring conceptual cohesion to our field through integrating concepts, allowing random ideas to mutate, and developing new theories.

The purpose of this study is to examine the public opinion on the Common Core State Standards (CCSS) on Twitter. Using Twitter API, we collected the tweets containing the hashtags #CommonCore and #CCSS for 12 months from 2014 to 2015. A Common Core corpus was created by compiling all the collected 660,051 tweets. The results of sentiment analysis suggest Twitter users expressed overwhelmingly negative sentiment towards the CCSS in all 50 states.Five topic clusters were detected by cluster analysis of the hashtag co-occurrence network. We also found that most of the opinion leaders were those who expressed negative sentiment towards the CCSS on Twitter. This study for the first time demonstrates how text mining techniques can be applied to education policy research, laying the foundation for real-time analytics of public opinion on education policies, thereby informing policymaking and implementation. , and #gagop) co-occurred in a tweet "Jeremy Spencer talking about the coming storm Nathan Deal will face with #CommonCore #gagov #StopCommonCore #gagop http://t.co/oyM0WIBBsw", then there were six co-occurrence ties connecting the four hashtags in the hashtag co-occurrence network: (1) CommonCore-gagov, (2) CommonCore-StopCommonCore, (3) CommonCore-gagop, (4) gagov-StopCommonCore; (5) gagovgagop; and (6) StopCommonCore-gagop. We wrote R code to repeat this procedure for all 660,051 tweets in the Common Core corpus to build the hashtag co-occurrence network. We then ran the faction algorithm-one of the network clustering algorithms-to partition the network (de Amorim, Barthélemy, & Ribeiro, 1992;Glover, 1989Glover, , 1990, thereby detecting the clusters of hashtags in the network. According to network science (Borgatti et al., 2013), the cooccurrence relationships between hashtags in the same cluster are closer than the ones in different clusters. Thus, the clusters of hashtags manifest the frequently co-occurred topics and their interconnections in the Common Core discourse on Twitter. Further, to ensure the robustness of network partitions, following the recommendations for cluster analysis, we ran the faction algorithm multiple times with different initial partitions by using different random number seeds (Borgatti et al., 2013). If the same subgroups always emerged, then the network partition is considered robust. Therefore, in this study we examined the subgroups that are consistently detected by using the faction algorithm. Communication Network AnalysisTo identify the opinion leaders in the Common Core discourse on Twitter, five centralities-Indegree, Outdegree, In-Bonacich Power, Out-Bonacich Power, and betweenness degree-were calculated as the indicators of each Twitter user's influence in the Twitter communication network. Opinion leaders, according to Rogers (2003), are those who occupy the PUBLIC SENTIMENT AND OPINION ON THE COMMON CORE 20 central structural locations in the communication network. In this study, the opinion leaders were those who have high centrality, calculated by performing social netwo...

Opting out of state standardized tests has recently become a movement-a series of grassroots, organized efforts to refuse to take high-stakes state standardized tests. In particular, the opt-out rates in the state of New York reached 20% in 2015 and21% in 2016. This study aims to illustrate the social networks and examine the paradoxes that have propelled the opt-out movement in New York-the movement's epicenter with the highest opt-out rate in the United States. Drawing on the conceptual frameworks of social movement theory, social network theory, and policy paradox, this study compiled the opt-out corpus by using the data from 221 press-coverage and 30 archival documents. Social network analysis was performed by examining the relational data that suggest coalition ties between movement actors. Further, to explicate how the movement actors forged coalition ties, all data in the corpus were then coded by Stone's framework of policy paradox regarding how the movement goals were articulated, how the movement was framed, and what policy solutions were mobilized. In addition to identifying the movement actors and two competing coalitions, it is found that to forge coalition ties, the movement actors in the opposing coalitions articulated contested goals of standardized testing, framed the movement via symbols, numbers, and Education Policy Analysis Archives Vol. 25 No. 34 2 interests, as well as mobilized policy solutions via inducements, rights, and power. The findings have important and timely implications for policymakers and movement actors as they seek and advance on common ground to make substantive changes in education policy. Keywords: Common Core State Standards; education policy; network analysis; opt-out movement; policy paradox; social movement; social networks; standardized testing Las redes sociales y las paradojas del movimiento opt-out entre la implementación de los estándares estatales Common Core: El caso de Nueva York Resumen: La exclusión de las pruebas estandarizadas estatales se ha convertido recientemente en un movimiento organizado para rehusarse a tomar pruebas estatales de alto riesgo. El índice de opt-out en el estado de Nueva York llego a 20% en 2015 y a 21% en 2016. Este estudio ilustra redes sociales y examina las paradojas que han promovido el movimiento opt-out en Nueva York, el epicentro con el índice más alto de opt-out en los Estados Unidos. Este estudio examina cómo los actores del movimiento crearon lazos de la coalición, cómo se articularon los objetivos del movimiento, cómo se enmarcó el movimiento y cuales soluciones de polítiza se utilizaron en la teoría de los movimientos sociales, la teoría de las redes sociales y la paradoja de las políticas fueron movilizados. De acuerdo con este estudio, para poder crear conexiones de coalición, los actores del movimiento en coaliciones opuestas articularon objetivos disputados de pruebas estandarizadas, enmarcaron el movimiento a través de símbolos, números e intereses, así como soluciones de políticas movilizadas a través de ...

No abstract

Abstract:Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data-characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining the assumptions, key concepts, merits, and caveats of three commonly used analytical approaches to mining massive amounts of text data: topic models, network text analysis, and sentiment analysis. Next, to ensure the veracity of using big data in education policy research, I debunk three methodological misconceptions. This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data in education policy. Keywords: big data; education policy; network text analysis; sentiment analysis; text mining; topic models Investigación de política educativa en la era de los grandes datos: Fronteras metodológicas, equívocos, y desafíos Resumen: A pesar de la abundancia de datos y del aumento de la disponibilidad de datos traídos por los avances tecnológicos, hubo estudios de políticas educativas muy limitadas que usaron datos importantes, caracterizados por gran volumen, gran variedad y alta velocidad. En base al reciente progreso del uso de grandes datos en investigaciones de políticas públicas y de ciencia social computacional, este trabajo pretende demostrar el potencial de datos importantes y la gran cantidad de datos que pueden ser utilizados en la investigación de políticas educativas. En primer lugar, introduzca datos importantes que son potencialmente relevantes para la investigación de políticas educativas. Puedo, entonces, presentar fronteras metodológicas, examinando los supuestos conceptos clave, méritos y salvedades de tres enfoques analíticos comúnmente usados en la minería de grandes cantidades de datos de texto: modelos de tópicos, análisis de conexiones textuales y análisis de sentimientos. A continuación, para garantizar la veracidad del uso de grandes datos en la investigación sobre políticas educativas, desenmascaramos tres equívocos metodológicos. Este artículo concluye con una discusión sobre el desarrollo de la capacidad de investigación interdisciplinaria abordando las preocupaciones de privacidad y los enigmas éticos a medida que exploramos una agenda de investigación de uso de datos importantes en la política educativa. Palabras-clave: datos grandes; política educativa; análisis de conexiones textuales; análisis de sentimientos; minería de textos; modelos de temas Pesquisa de política educacional na era dos grandes dados: Fronteiras metodológicas, equívocos, e desafios ...

CREPT and p15RS are two recently identified homologous proteins that regulate cell proliferation in an opposite way and are closely related to human cancer development. Both CREPT and p15RS consist of an N-terminal RPR domain and a C-terminal domain with high sequence homology. The transcription enhancement by CREPT is attributed to its interaction with RNA polymerase II (Pol II). Here we provide biochemical and structural evidence to support and extend this molecular mechanism. Through fluorescence polarization analysis, we show that the RPR domains of CREPT and p15RS (CREPT-RPR and p15RS-RPR) bind to different Pol II C-terminal domain (CTD) phosphoisoforms with similar affinity and specificity. We also determined the crystal structure of p15RS-RPR. Sequence and structural comparisons with RPR domain of Rtt103, a homolog of CREPT and p15RS in yeast, reveal structural basis for the similar binding profile of CREPT-RPR and p15RS-RPR with Pol II CTD. We also determined the crystal structure of the C-terminal domain of CREPT (CREPT-CTD), which is a long rod-like dimer and each monomer adopts a coiled-coil structure. We propose that dimerization through the C-terminal domain enhances the binding strength between CREPT or p15RS with Pol II by increasing binding avidity. Our results collectively reveal the respective roles of N-terminal RPR domain and C-terminal domain of CREPT and p15RS in recognizing RNA Pol II.