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The electronic structure of eight zinc-centered porphyrin macrocyclic molecules are investigated using density functional theory for ground-state properties, time-dependent density functional theory (TDDFT) for excited states, and Franck-Condon (FC) analysis for further characterization of the UV-vis spectrum. Symmetry breaking was utilized to find the lowest energy of the excited states for many states in the spectra. To confirm the theoretical modeling, the spectroscopic result from zinc phthalocyanine (ZnPc) is used to compare to the TDDFT and FC result. After confirmation of the modeling, five more planar molecules are investigated: zinc tetrabenzoporphyrin (ZnTBP), zinc tetrabenzomonoazaporphyrin (ZnTBMAP), zinc tetrabenzocisdiazaporphyrin (ZnTBcisDAP), zinc tetrabenzotransdiazaporphyrin (ZnTBtransDAP), and zinc tetrabenzotriazaporphyrin (ZnTBTrAP). The two latter molecules are then compared to their phenylated sister molecules: zinc monophenyltetrabenzotriazaporphyrin (ZnMPTBTrAP) and zinc diphenyltetrabenzotransdiazaporphyrin (ZnDPTBtransDAP). The spectroscopic results from the synthesis of ZnMPTBTrAP and ZnDPTBtransDAP are then compared to their theoretical models and non-phenylated pairs. While the Franck-Condon results were not as illuminating for every B-band, the Q-band results were successful in all eight molecules, with a considerable amount of spectral analysis in the range of interest between 300 and 750 nm. The π-π^* transitions are evident in the results for all of the Q bands, while satellite vibrations are also visible in the spectra. In particular, this investigation finds that, while ZnPc has a D_4h symmetry at ground state, a C_4v symmetry is predicted in the excited-state Q band region. The theoretical results for ZnPc found an excitation energy at the Q-band 0-0 transition of 1.88 eV in vacuum, which is in remarkable agreement with published gas-phase spectroscopy, as well as our own results of ZnPc in solution with Tetrahydrofuran that are provided in this paper.

This paper presents a Bayesian framework for evaluative classification. Current education policy debates center on arguments about whether and how to use student test score data in school and personnel evaluation. Proponents of such use argue that refusing to use data violates both the public’s need to hold schools accountable when they use taxpayer dollars and students’ right to educational opportunities. Opponents of formulaic use of test-score data argue that most standardized test data is susceptible to fatal technical flaws, is a partial picture of student achievement, and leads to behavior that corrupts the measures.

A Bayesian perspective on summative ordinal classification is a possible framework for combining quantitative outcome data for students with the qualitative types of evaluation that critics of high-stakes testing advocate. This paper describes the key characteristics of a Bayesian perspective on classification, describes a method to translate a naïve Bayesian classifier into a point-based system for evaluation, and draws conclusions from the comparison on the construction of algorithmic (including point-based) systems that could capture the political and practical benefits of a Bayesian approach. The most important practical conclusion is that point-based systems with fixed components and weights cannot capture the dynamic and political benefits of a reciprocal relationship between professional judgment and quantitative student outcome data.

This is a brief text intended for use in undergraduate school-and-society classes. Your class may also be titled “Social foundations of education.” “Social foundations of education” is an interdisciplinary field that includes both humanities and social-science perspectives on schooling. It thus includes study of the philosophy and history of education as well as sociological, economic, anthropological, and political perspectives on schooling.

The core of most social foundations classes lies in the relationship between formal schooling and broader society. This emphasis means that while some parts of psychology may be related to the core issues of social foundations classes—primarily social psychology—the questions that are asked within a social-foundations class are different from the questions raised in child development, educational psychology, and most teaching-methods classes. For example, after finishing the first chapter of this text, you should be able to answer the question, “Why does the federal government pay public schools to feed poor students at breakfast and lunch?” Though there is some psychology research tying nutrition to behavior and learning, the policy is based on much broader expectations of schools. In this case, “Children learn better if they are well-fed” both is based on research and also is an incomplete answer.

The current debate over graduate rate calculations and results has glossed over the relationship between student migration and the accuracy of various graduation rates proposed over the past five years. Three general grade-based graduation rates have been proposed recently, and each has a parallel version that includes an adjustment for migration, whether international, internal to the U.S., or between different school sectors. All of the adjustment factors have a similar form, allowing simulation of estimates from real data, assuming different unmeasured net migration rates. In addition, a new age-based graduation rate, based on mathematical demography, allows the simulation of estimates on a parallel basis using data from Virginia's public schools.

Both the direct analysis and simulation demonstrate that graduation rates can only be useful with accurate information about student migration. A discussion of Florida's experiences with longitudinal cohort graduation rates highlights some of the difficulties with the current status of the oldest state databases and the need for both technical confidence and definitional clarity. Meeting the No Child Left Behind mandates for school-level graduation rates requires confirmation of transfers and an audit of any state system for accuracy, and basing graduation rates on age would be a significant improvement over rates calculated using grade-based data.

The spread of academic testing for accountability purposes in multiple countries has obscured at least two historical purposes of academic testing: community ritual and management of the social structure. Testing for accountability is very different from the purpose of academic challenges one can identify in community “examinations” in 19th century North America, or exams’ controlling access to the civil service in Imperial China. Rather than testing for ritual or access to mobility, the modern uses of testing are much closer to the state-building project of a tax census, such as the Domesday Book of medieval Britain after the Norman Invasion, the social engineering projects described in James Scott's Seeing like a State (1998), or the “mapping the world” project that David Nye described in America as Second Creation (2004). This paper will explore both the instrumental and cultural differences among testing as ritual, testing as mobility control, and testing as state-building.

One way to view ‘equitable pedagogy’ is through an opportunity to learn (OTL) lens, meaning that regardless of race, class, or culture, a student has access to rigorous and meaningful content, as well as appropriate resources and instruction necessary to learn and demonstrate understanding of that content. Assessment holds a unique position in the classroom in that it can both uncover whether inequitable conditions exist (i.e., performance gaps, denied OTL) and provide an OTL by mediating communication between teacher and students regarding learning progress and what is important to learn. Nevertheless, individuals entering teacher education programs often hold deficit views toward marginalized students, such as Language Minorities (LMs), believe that assessment strictly serves to evaluate learning, and do not do consider how language and culture influence student thinking–views supplanting assessment’s role at supporting an equitable pedagogy for LMs. Through surveys, interviews, program artifacts, and classroom observation, I report on a case study of one pre-service physics teacher, Dean, to depict how his expertise at assessing science did evolve throughout his yearlong teacher education program in terms of (a) becoming more knowledgeable of the role of language and (b) developing a belief in incorporating ‘discourse’ while assessing science. Within the case study, I analyze one particular episode from Dean’s teaching practicum to highlight remaining challenges for pre-service teachers to integrate science and language in classroom assessment—namely, interpreting students’ use of language along with their understanding of core science ideas. The findings underscore the need for connecting language and equity issues to content-area assessment in teacher preparation.

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on–off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

Successful identification of directed dynamical influence in complex systems is relevant to significant problems of current interest. Traditional methods based on Granger causality and transfer entropy have issues such as difficulty with nonlinearity and large data requirement. Recently a framework based on nonlinear dynamical analysis was proposed to overcome these difficulties. We find, surprisingly, that noise can counterintuitively enhance the detectability of directed dynamical influence. In fact, intentionally injecting a proper amount of asymmetric noise into the available time series has the unexpected benefit of dramatically increasing confidence in ascertaining the directed dynamical influence in the underlying system. This result is established based on both real data and model time series from nonlinear ecosystems. We develop a physical understanding of the beneficial role of noise in enhancing detection of directed dynamical influence.