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This study employed (and reports in detail) systematic procedures for research synthesis and meta‐analysis to summarize findings from experimental and quasi‐experimental investigations into the effectiveness of L2 instruction published between 1980 and 1998. Comparisons of average effect sizes from 49 unique sample studies reporting sufficient data indicated that focused L2 instruction results in large target‐oriented gains, that explicit types of instruction are more effective than implicit types, and that Focus on Form and Focus on Forms interventions result in equivalent and large effects. Further findings suggest that the effectiveness of L2 instruction is durable and that the type of outcome measures used in individual studies likely affects the magnitude of observed instructional effectiveness. Generalizability of findings is limited because the L2 type‐of‐instruction domain has yet to engage in rigorous empirical operationalization and replication of its central research constructs. Changes in research practices are recommended to enhance the future accumulation of knowledge about the effectiveness of L2 instruction.

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The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases. A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further, it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.

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The glucosinolates (GSLs) is a well-defined group of plant metabolites characterized by having an S-β-d-glucopyrano unit anomerically connected to an O-sulfated (Z)-thiohydroximate function. After enzymatic hydrolysis, the sulfated aglucone can undergo rearrangement to an isothiocyanate, or form a nitrile or other products. The number of GSLs known from plants, satisfactorily characterized by modern spectroscopic methods (NMR and MS) by mid-2018, is 88. In addition, a group of partially characterized structures with highly variable evidence counts for approximately a further 49. This means that the total number of characterized GSLs from plants is somewhere between 88 and 137. The diversity of GSLs in plants is critically reviewed here, resulting in significant discrepancies with previous reviews. In general, the well-characterized GSLs show resemblance to C-skeletons of the amino acids Ala, Val, Leu, Trp, Ile, Phe/Tyr and Met, or to homologs of Ile, Phe/Tyr or Met. Insufficiently characterized, still hypothetic GSLs include straight-chain alkyl GSLs and chain-elongated GSLs derived from Leu. Additional reports (since 2011) of insufficiently characterized GSLs are reviewed. Usually the crucial missing information is correctly interpreted NMR, which is the most effective tool for GSL identification. Hence, modern use of NMR for GSL identification is also reviewed and exemplified. Apart from isolation, GSLs may be obtained by organic synthesis, allowing isotopically labeled GSLs and any kind of side chain. Enzymatic turnover of GSLs in plants depends on a considerable number of enzymes and other protein factors and furthermore depends on GSL structure. Identification of GSLs must be presented transparently and live up to standard requirements in natural product chemistry. Unfortunately, many recent reports fail in these respects, including reports based on chromatography hyphenated to MS. In particular, the possibility of isomers and isobaric structures is frequently ignored. Recent reports are re-evaluated and interpreted as evidence of the existence of "isoGSLs", i.e. non-GSL isomers of GSLs in plants. For GSL analysis, also with MS-detection, we stress the importance of using authentic standards.

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Nanomaterials are at the leading edge of the emerging field of nanotechnology. Their unique and tunable size-dependent properties (in the range 1-100 nm) make these materials indispensable in many modern technological applications. In this Review, we summarize the state-of-art in the manufacture and applications of inorganic nanoparticles made using continuous hydrothermal flow synthesis (CHFS) processes. First, we introduce ideal requirements of any flow process for nanoceramics production, outline different approaches to CHFS, and introduce the pertinent properties of supercritical water and issues around mixing in flow, to generate nanoparticles. This Review then gives comprehensive coverage of the current application space for CHFS-made nanomaterials including optical, healthcare, electronics (including sensors, information, and communication technologies), catalysis, devices (including energy harvesting/conversion/fuels), and energy storage applications. Thereafter, topics of precursor chemistry and products, as well as materials or structures, are discussed (surface-functionalized hybrids, nanocomposites, nanograined coatings and monoliths, and metal-organic frameworks). Later, this Review focuses on some of the key apparatus innovations in the field, such as in situ flow/rapid heating systems (to investigate kinetics and mechanisms), approaches to high throughput flow syntheses (for nanomaterials discovery), as well as recent developments in scale-up of hydrothermal flow processes. Finally, this Review covers environmental considerations, future directions and capabilities, along with the conclusions and outlook.

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This study investigated the effectiveness of a blended learning approach—involving the thesis, analysis, and synthesis key (TASK) procedural strategy; online Edmodo discussions; online message labels; and writing models—on student argumentative writing in a Hong Kong secondary school. It also examined whether the application of digital game mechanics increased student online contribution and writing performance. Three classes of Secondary 4 students (16- to 17-year-olds) participated in the 7-week study. The first experimental group (n = 22) utilized the blended learning + gamification approach. The second experimental group (n = 30) utilized only the blended learning approach. In the control group (n = 20), a teacher-led direct-instruction approach on the components of argumentation was employed. Data sources included students’ pre- and post-test written essays, students’ online Edmodo postings, and student and teacher interviews. We found a significant improvement in students’ writing using the blended learning approach. On-topic online contributions were significantly higher when gamification was adopted. Student and teacher opinions on the blended learning approach were also examined.

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Summary We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of specific applications. However, in any specific application the values of necessary parameters may be unknown. In this case, physical observations of the system in the specific context are used to learn about the unknown parameters. The process of fitting the model to the observed data by adjusting the parameters is known as calibration. Calibration is typically effected by ad hoc fitting, and after calibration the model is used, with the fitted input values, to predict the future behaviour of the system. We present a Bayesian calibration technique which improves on this traditional approach in two respects. First, the predictions allow for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Second, they attempt to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best-fitting parameter values. The method is illustrated by using data from a nuclear radiation release at Tomsk, and from a more complex simulated nuclear accident exercise.

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About the Alliance for Excellent EducationThe Alliance for Excellent Education is a Washington, DC-based national policy and advocacy organization that works to improve national and federal policy so that all students can achieve at high academic levels and graduate high school ready for success in college, work, and citizenship in the twenty-first century.The Alliance focuses on America's six million most-at-risk secondary school students-those in the lowest achievement quartile-who are most likely to leave school without a diploma or to graduate unprepared for a productive future.The Alliance's audience includes parents, educators, the federal, state, and local policy communities, education organizations, business leaders, the media, and a concerned public.To inform the national debate about education policies and options, the Alliance produces reports and other materials, makes presentations at meetings and conferences, briefs policymakers and the press, and provides timely information to a wide audience via its biweekly newsletter and regularly updated website, www.all4ed.org.

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