Multilevel Analysis of Fifth Grade Teacher Qualifications and Their Students\u27 Science Achievement

The No Child Left Behind Act mandated every student be taught by a highly qualified teacher (HQT). Criteria to determine if teachers meet the HQT mandate fail to account for differences in grade levels, subject areas, and student demographics. This study posited that the relationship between measures of teacher quality and student achievement vary according to contextual factors. Fifth grade is unique in that it marks students\u27 transition from upper elementary to middle school grade levels thus, fifth grade may be classified as either an upper elementary grade or middle grade. This classification determines HQT requirements specifically, classification affects the level of content knowledge teachers must demonstrate to satisfy the HQT mandate. Middle level teachers are specialists and required to demonstrate content knowledge (CK) in the subjects they teach. However, the relationship between teachers\u27 level of content knowledge and fifth grade student science achievement is poorly understood. This study examined measures of teachers\u27 qualifications as predictors of average student achievement. In addition, examination of gender and socioeconomic status (SES) explored how teacher qualifications differentially impact various student subgroups and impact achievement gaps. A multilevel analysis examined student gender and SES as level-1 predictors of science achievement aggregated teacher characteristics at level-2 predicted changes in gender and SES achievement gaps. Findings revealed teacher qualifications that predicted fifth grade science achievement differed from qualifications that predict student achievement in other subject areas. Teachers\u27 time spent at professional development and level of job enjoyment significantly predicted changes in student science achievement. The relationship between professional develop and achievement implicated the need for fifth grade teachers to possess content knowledge. The unanticipated finding of a strong correlation between teachers\u27 job enjoyment and studen

Telling experts from spammers: expertise ranking in folksonomies

With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures

Multilevel Analysis of Fifth Grade Teacher Qualifications and Their Students\u27 Science Achievement

The No Child Left Behind Act mandated every student be taught by a highly qualified teacher (HQT). Criteria to determine if teachers meet the HQT mandate fail to account for differences in grade levels, subject areas, and student demographics. This study posited that the relationship between measures of teacher quality and student achievement vary according to contextual factors. Fifth grade is unique in that it marks students\u27 transition from upper elementary to middle school grade levels thus, fifth grade may be classified as either an upper elementary grade or middle grade. This classification determines HQT requirements specifically, classification affects the level of content knowledge teachers must demonstrate to satisfy the HQT mandate. Middle level teachers are specialists and required to demonstrate content knowledge (CK) in the subjects they teach. However, the relationship between teachers\u27 level of content knowledge and fifth grade student science achievement is poorly understood. This study examined measures of teachers\u27 qualifications as predictors of average student achievement. In addition, examination of gender and socioeconomic status (SES) explored how teacher qualifications differentially impact various student subgroups and impact achievement gaps. A multilevel analysis examined student gender and SES as level-1 predictors of science achievement aggregated teacher characteristics at level-2 predicted changes in gender and SES achievement gaps. Findings revealed teacher qualifications that predicted fifth grade science achievement differed from qualifications that predict student achievement in other subject areas. Teachers\u27 time spent at professional development and level of job enjoyment significantly predicted changes in student science achievement. The relationship between professional develop and achievement implicated the need for fifth grade teachers to possess content knowledge. The unanticipated finding of a strong correlation between teachers\u27 job enjoyment and studen

On Measuring Expertise in Collaborative Tagging Systems

Collaborative tagging systems such as Delicious.com provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of collaborative tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a collaborative tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spamming than other methods such as the HITS algorithm and simple statistical measures

Measuring expertise in online communities

Online communities have become important venues where Web users interact with each other and share their favorite items. Websites let users organize their favorite items online and benefit from one another's collections. This article discusses the notions of experts and expertise in the context of online communities

Chemical Stabilization and Electrochemical Destabilization of the Iron Keggin Ion in Water

The iron Keggin ion is identified as a structural building block in both magnetite and ferrihydrite, two important iron oxide phases in nature and in technology. Discrete molecular forms of the iron Keggin ion that can be both manipulated in water and chemically converted to the related metal oxides are important for understanding growth mechanisms, in particular, nonclassical nucleation in which cluster building units are preserved in the aggregation and condensation processes. Here we describe two iron Keggin ion structures, formulated as [Bi₆FeO₄Fe₁₂O₁₂(OH)₁₂­(CF₃COO)₁₀(H₂O)₂]³⁺ (<b>Kegg-1</b>) and [Bi₆FeO₄Fe₁₂O₁₂­(OH)₁₂(CF₃COO)₁₂]¹⁺ (<b>Kegg-2</b>). Experimental and simulated X-ray scattering studies show indefinite stability of these clusters in water from pH 1–3. The tridecameric iron Keggin-ion core is protected from hydrolysis by a synergistic effect of the capping Bi³⁺ cations and the trifluoroacetate ligands that, respectively, bond to the iron and bridge to the bismuth. By introducing electrons to the aqueous solution of clusters, we achieve complete separation of bismuth from the cluster, and the iron Keggin ion rapidly converts to magnetite and/or ferrihydrite, depending on the mechanism of reduction. In this strategy, we take advantage of the easily accessible reduction potential and crystallization energy of bismuth. Reduction was executed in bulk by chemical means, by voltammetry, and by secondary effects of transmission electron microscopy imaging of solutions. Prior, we showed a less stable analogue of the iron Keggin cluster converted to ferrihydrite simply upon dissolution. The prior and currently studied clusters with a range of reactivity provide a chemical system to study molecular cluster to metal oxide conversion processes in detail

Chemical Stabilization and Electrochemical Destabilization of the Iron Keggin Ion in Water

The iron Keggin ion is identified as a structural building block in both magnetite and ferrihydrite, two important iron oxide phases in nature and in technology. Discrete molecular forms of the iron Keggin ion that can be both manipulated in water and chemically converted to the related metal oxides are important for understanding growth mechanisms, in particular, nonclassical nucleation in which cluster building units are preserved in the aggregation and condensation processes. Here we describe two iron Keggin ion structures, formulated as [Bi₆FeO₄Fe₁₂O₁₂(OH)₁₂­(CF₃COO)₁₀(H₂O)₂]³⁺ (<b>Kegg-1</b>) and [Bi₆FeO₄Fe₁₂O₁₂­(OH)₁₂(CF₃COO)₁₂]¹⁺ (<b>Kegg-2</b>). Experimental and simulated X-ray scattering studies show indefinite stability of these clusters in water from pH 1–3. The tridecameric iron Keggin-ion core is protected from hydrolysis by a synergistic effect of the capping Bi³⁺ cations and the trifluoroacetate ligands that, respectively, bond to the iron and bridge to the bismuth. By introducing electrons to the aqueous solution of clusters, we achieve complete separation of bismuth from the cluster, and the iron Keggin ion rapidly converts to magnetite and/or ferrihydrite, depending on the mechanism of reduction. In this strategy, we take advantage of the easily accessible reduction potential and crystallization energy of bismuth. Reduction was executed in bulk by chemical means, by voltammetry, and by secondary effects of transmission electron microscopy imaging of solutions. Prior, we showed a less stable analogue of the iron Keggin cluster converted to ferrihydrite simply upon dissolution. The prior and currently studied clusters with a range of reactivity provide a chemical system to study molecular cluster to metal oxide conversion processes in detail

Chemical Stabilization and Electrochemical Destabilization of the Iron Keggin Ion in Water

The iron Keggin ion is identified as a structural building block in both magnetite and ferrihydrite, two important iron oxide phases in nature and in technology. Discrete molecular forms of the iron Keggin ion that can be both manipulated in water and chemically converted to the related metal oxides are important for understanding growth mechanisms, in particular, nonclassical nucleation in which cluster building units are preserved in the aggregation and condensation processes. Here we describe two iron Keggin ion structures, formulated as [Bi₆FeO₄Fe₁₂O₁₂(OH)₁₂­(CF₃COO)₁₀(H₂O)₂]³⁺ (<b>Kegg-1</b>) and [Bi₆FeO₄Fe₁₂O₁₂­(OH)₁₂(CF₃COO)₁₂]¹⁺ (<b>Kegg-2</b>). Experimental and simulated X-ray scattering studies show indefinite stability of these clusters in water from pH 1–3. The tridecameric iron Keggin-ion core is protected from hydrolysis by a synergistic effect of the capping Bi³⁺ cations and the trifluoroacetate ligands that, respectively, bond to the iron and bridge to the bismuth. By introducing electrons to the aqueous solution of clusters, we achieve complete separation of bismuth from the cluster, and the iron Keggin ion rapidly converts to magnetite and/or ferrihydrite, depending on the mechanism of reduction. In this strategy, we take advantage of the easily accessible reduction potential and crystallization energy of bismuth. Reduction was executed in bulk by chemical means, by voltammetry, and by secondary effects of transmission electron microscopy imaging of solutions. Prior, we showed a less stable analogue of the iron Keggin cluster converted to ferrihydrite simply upon dissolution. The prior and currently studied clusters with a range of reactivity provide a chemical system to study molecular cluster to metal oxide conversion processes in detail

Specification of CNS glia from neural stem cells in the embryonic neuroepithelium

All the neurons and glial cells of the central nervous system are generated from the neuroepithelial cells in the walls of the embryonic neural tube, the ‘embryonic neural stem cells’. The stem cells seem to be equivalent to the so-called ‘radial glial cells’, which for many years had been regarded as a specialized type of glial cell. These radial cells generate different classes of neurons in a position-dependent manner. They then switch to producing glial cells (oligodendrocytes and astrocytes). It is not known what drives the neuron–glial switch, although downregulation of pro-neural basic helix–loop–helix transcription factors is one important step. This drives the stem cells from a neurogenic towards a gliogenic mode. The stem cells then choose between developing as oligodendrocytes or astrocytes, of which there might be intrinsically different subclasses. This review focuses on the different extracellular signals and intracellular responses that influence glial generation and the choice between oligodendrocyte and astrocyte fates