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The purpose of this article was to systematically review the available research on learner characteristics that influence the treatment effectiveness of early literacy interventions. Meta‐analytic procedures were applied to a total of 30 studies that met the inclusionary and exclusionary criteria. Mean average effect sizes were computed for seven primary learner characteristic categories: (1) rapid naming, (2) alphabetic principle, (3) phonological awareness, (4) problem behavior, (5) memory, (6) IQ, and (7) demographic. The primary learner characteristics that influenced the treatment responsiveness of early literacy interventions were, in order of magnitude, rapid naming, problem behavior, phonological awareness, alphabetic principle, memory, IQ, and demographics. With the exception of the demographic category, the obtained effect sizes for the primary learner characteristics were moderately large. The demographic primary learner characteristic (i.e., disability, ethnicity, grade‐level status) of children was not statistically (p < 0.05) distinct from zero. The findings, limitations, and future research needs are discussed.  相似文献   
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This article explores the basis of negative sentiments toward and current critiques of high-stakes student testing from within the education profession. To promote some balance for current policy debates, evidence for 10 unintended, unrecognized, or unarticulated positive consequences is provided. The article concludes with an examination of the relationship between high-stakes testing and accountability systems.  相似文献   
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This study examined the cost and performance of six commercial document suppliers accessible via OCLC, and comapared their service to that of traditional interlibrary loan (ILL). Results indicated that, while commercial firms cannot be recommended as a wholesale replacement for ILL, they can add much-needed flexibility to a library's overall plan for document delivery.  相似文献   
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Searching online information resources using mobile devices is affected by small screens which can display only a fraction of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ) and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking that attempts to maximize information gain from the user’s choices and the top-D ranked documents. Experimental results indicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. Extended version of “Evaluating Relevance Feedback Algorithms for Searching on Small Displays, ” Vishwa Vinay, Ingemar J. Cox, Natasa Milic-Frayling, Ken Wood published in the proceedings of ECIR 2005, David E. Losada, Juan M. Fernández-Luna (Eds.), Springer 2005, ISBN 3-540-25295-9  相似文献   
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