P EMBELAJARAN M EMPRODUKSI T EKS E KSPLANASI YANG B ERORIENTASI PADA K ARAKTERISTIK TEKS MENGGUNAKAN MODEL PEMBELAJARAN ASSURANCE, RELEVANCE, INTEREST, ASSESMENT, SATISFACTION ( ARIAS ) DI KELAS XI SMA PGRI I B ANDUNG PADA TAHUN AJARAN 2015 / 2016

Menulis merupakan sa tu kegiatan yang digunakan seseorang untuk berko - munikasi secara tidak langsung. Salah satu metode pembelajaran yang dapat meningkatkan kemampuan peserta didik dalam menulis teks eksplanasi , yaitu dengan model pembelajaran Assurance, Relevance, Interest, Assesment, Satis - faction , (ARIAS) . Rumusan masalah yang penulis ajukan: (1) Mampukah penulis meren - canakan, melaksanakan, dan menilai pembelajaran memproduksi teks eksplanasi dengan menggunakan model ARIAS ? ; (2) Mampukah siswa kelas XI SMA PGRI I Bandung memproduksi teks eksplanasi sesuai dengan struktur, ciri - ciri keba - hasaan, dan kaidah penulisan? ; (3 ) Efektifkah model pembelajaran ARIAS digunakan dalam menulis teks eksplanasi sesuai dengan struktur, ciri - ciri kebaha - saan, dan kaidah penulisan? Hipotesis yang penulis rumuskan yaitu: (1) p enulis mampu merencanakan, melaksanakan, dan menilai pembelajara n memproduksi teks eksplanasi yang ber - orientasi pada karakteristik teks menggunakan model ARIAS , di kelas XI PGRI I Bandung pada tahun ajaran 2015/2016 ; (2) s iswa kelas XI PGRI I Bandung pada tahun ajaran 2015/2016 mampu memproduksi teks eksplanasi sesuai dengan struktur, ciri - ciri kebahasaan, da n kaidah penulisan dengan tepat ; (3) m odel pembelajaran ARIAS , efektif digunakan dalam pembelajaran memproduksi teks eksplanasi sesuai dengan struktur, ciri - ciri kebahasaan, dan kaidah penulisan di kelas XI P GRI I Bandung dibandingkan metode konvensional. Metode penelitian yang digunakan oleh penulis dalam penelitian ini adalah metode penelitian one group pretest - posttest design . Adapun hasil penelitiannya sebagai berikut. 1. Penulis mampu merencanakan, melaksan akan, dan menilai pembelajaran mem - produksi teks eksplanasi dengan menggunakan model ARIAS di kelas XI IIS 2 SMA PGRI 1 Bandung. Hal ini terbukti dengan hasil penilaian perencanaan dan pelaksanakan pembelajaran sebesar 3,77 . Kemampuan penulis termasuk ke dalam kategori sangat baik (A). 2. Peserta didik kelas XI IIS 2 SMA PGRI 1 B andung mampu memproduksi teks eksplanasi sesuai dengan struktur, ciri - ciri kebahasaan, dan kaidah penulisan . Hal ini terbukti dari nilai rata - rata pretes yaitu 44,3 , sedangkan nilai r ata - rata postes 74,1 . Jadi selisih nilai rata - rat a pretes dan postes yaitu 29,8 . 3. M odel pembelajaran (ARIAS) efektif digunakan dalam menulis teks eksplanasi sesuai dengan struktur, ciri - ciri kebahasaan, dan kaidah penulisan siswa kelas XI IIS 2 SMA PGRI 1 Bandung. Hal ini terbukti dari hasil perhitungan taraf signifikan perbedaan dua mean antara pretes dengan nilai postes , diketahui t hitung 11,6 > t tabel 2,0 5 . Berdasark an pemaparan di atas, dapat ditarik kesimpulan bahwa model ARIAS efektif digunakan untuk pembelajaran memproduksi teks eksplanasi. Kata kunci: Pembelajaran, me mproduksi , teks eksplanasi , ARIA

The importance of multimodel projections to assess uncertainty in projections from simulation models

Simulation models are increasingly used to gain insights regarding the long-term effect of both direct and indirect anthropogenic impacts on natural resources and to devise and evaluate policies that aim to minimize these effects. If the uncertainty from simulation model projections is not adequately quantified and reported, modeling results might be misleading, with potentially serious implications. A method is described, based on a nested simulation design associated with multimodel projections, that allows the partitioning of the overall uncertainty in model projections into a number of different sources of uncertainty: model stochasticity, starting conditions, parameter uncertainty, and uncertainty that originates from the use of key model assumptions. These sources of uncertainty are likely to be present in most simulation models. Using the forest dynamics model SYMFOR as a case study, it is shown that the uncertainty originated from the use of alternate modeling assumptions, a source of uncertainty seldom reported, can be the greatest source of uncertainty, accounting for 66-97% of the overall variance of the mean after 100 years of stand dynamics simulation. This implicitly reveals the great importance of these multimodel projections even when multiple models from independent research groups are not available. Finally, it is suggested that a weighted multimodel average (in which the weights are estimated from the data) might be substantially more precise than a simple multimodel average (equivalent to equal weights for all models) as models that strongly conflict with the data are given greatly reduced or even zero weights. The method of partitioning modeling uncertainty is likely to be useful for other simulation models, allowing for a better estimate of the uncertainty of model projections and allowing researchers to identify which data need to be collected to reduce this uncertainty.</p

MetaFIND: A feature analysis tool for metabolomics data

<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p

Research trends in mathematics teacher education

Research on the preparation and continued development of mathematics teachers is becoming an increasingly important subset of mathematics education research. Such research explores the attributes, knowledge, skills and beliefs of mathematics teachers as well as methods for assessing and developing these critical aspects of teachers and influences on teaching.Research Trends in Mathematics Teacher Education focuses on three major themes in current mathematics teacher education research: mathematical knowledge for teaching, teacher beliefs and identities, and tools and techniques to support teacher learning. Through careful reports of individual research studies and cross-study syntheses of the state of research in these areas, the book provides insights into teachers' learning processes and how these processes can be harnessed to develop effective teachers. Chapters investigate bedrock skills needed for working with primary and secondary learners (writing relevant problems, planning lessons, being attentive to student learning) and illustrate how knowledge can be accessed, assessed, and nurtured over the course of a teaching career. Commentaries provide context for current research while identifying areas deserving future study. Included among the topics: Teachers' curricular knowledgeTeachers' personal and classroom mathematicsTeachers' learning journeys toward reasoning and sense-makingTeachers' transitions in noticingTeachers' uses of a learning trajectory as a tool for mathematics lesson planningA unique and timely set of perspectives on the professional development of mathematics teachers at all stages of their careers, Research Trends in Mathematics Teacher Education brings clarity and practical advice to researchers as well as practitioners in this increasingly critical are

Appendix A. Parameters used for different model variants of SYMFOR.

Parameters used for different model variants of SYMFOR