We Are Ready

圣火、五环、中国印……2008年北京奥运会离我们越来越近。奥运的气息已经渗透到每天的空气中,奥运的话题已经融入我们每天的生活中。代表"北京欢迎你"的五个福娃已经把北京热情的邀请传递到世界的各个角落。此时,脑中突然闪现一     

玫瑰是红色的

<正>玫瑰是红色的。紫罗兰是蓝色的。糖是甜的。你也一样。Notes重点讲解sugar是糖,我们平时所吃的糖果常用sweet或candy.我喜欢吃糖就是:I lke sweets.或I like candies.但是经常吃糖,可不是好习惯噢。     

We Are Ready

圣火、五环、中国印……2008年北京奥运会离我们越来越近。奥运的气息已经渗透到每天的空气中,奥运的话题已经融入我们每天的生活中。代表“北京欢迎你”的五个福娃已经把北京热情的邀请传递到世界的各个角落。此时,脑中突然闪现一个问题,我们为奥运会做好准备了吗？似乎这是一个想当然肯定的答案,但我却想这是一个需要用心去寻找、用行动去证明的答案!     

你会不会这样

说个有意思的故事给你听。有位非常走运,又非常不走运的警官。非常走运的是他做了几十年的警务工作,由小警员升上警官,一直到将近退休,居然没有遇过一次盗匪,没有开过一枪。他非常不走运的是,就在他退休的前一天,经过一家银行,正看见有人抢劫,于是掏枪吓阻,不幸对     

Interactions Are Critical

Although we agree with Theobold and Freeman (2014) that linear models are the most appropriate way in which to analyze assessment data, we show the importance of testing for interactions between covariates and factors.To the Editor:Recently, Theobald and Freeman (2014) reviewed approaches for measuring student learning gains in science, technology, engineering, and mathematics (STEM) education research. In their article, they highlighted the shortcomings of approaches such as raw change scores, normalized gain scores, normalized change scores, and effect sizes when students are not randomly assigned to classes based on the different pedagogies that are being compared. As an alternative, they propose using linear regression models in which characteristics of students, such as pretest scores, are included as independent variables in addition to treatments. Linear models that include both continuous and categorical independent variables are often termed analysis of covariance (ANCOVA) models. The approach of using ANCOVA to control for differences in students among treatments groups has been suggested previously by Weber (2009) . We largely agree with Theobald and Freeman (2014) and Weber (2009) that ANCOVA models are an appropriate method for situations in which students cannot be randomly assigned to treatments and controls. However, in describing how to implement linear regression models to examine student learning gains, Theobald and Freeman (2014) ignore a fundamental assumption of ANCOVA.ANCOVA assumes homogeneity of slopes (McDonald, 2009 ; Sokal and Rohlf, 2011 ). In other words, the slope of the relationship between the covariate (e.g., pretest score) and the dependent variable (e.g., posttest score) is the same for the treatment group and the control. This assumption is a strict assumption of ANCOVA in that violations of this assumption can result in incorrect conclusions (Engqvist, 2005 ). For example, in Figure 1, both pretest score and treatment have statistically significant main effects in a linear model with only pretest score (F(1, 97) = 25.6, p < 0.001) and treatment (F(1, 97) = 42.6, p < 0.01) as independent variables. Therefore, we would conclude that all students in the class with pedagogical innovation had significantly greater posttest scores than those students in the control class for a given pretest score. Furthermore, we would conclude that the pedagogical innovation led to the same increase in score for all students in the treatment class, independent of their pretest scores. Clearly, neither of these conclusions would be justified.Researchers must first test the assumption of the homogeneity of slopes by including an interaction term (covariate × treatment) in their linear model (McDonald, 2009 ; Weber 2009 ; Sokal and Rohlf, 2011 ). For example, if we measured student achievement in two courses with different instructional approaches in a typical pretest/posttest design, then the interaction between students’ pretest scores and the type of instruction must be considered, because the instruction may have a different effect for high- versus low-achieving students. If multiple covariates are included in the linear model (see Equation 1 in Theobald and Freeman, 2014 ), then interaction terms need to be included for each of the covariates in the model. If the interaction term is statistically significant, this suggests that the relationship between the covariate and the dependent variable is different for each treatment group (F(1, 96) = 25.1, p < 0.001; Figure 1). As a result, the effect of the treatment will depend on the value of the covariate, and universal statements about the effect of the treatment are not appropriate (Engqvist, 2005 ). If the interaction term is not statistically significant, it should be removed from the model and the analysis rerun without the interaction term. Failure to remove an interaction term that was not statistically significant also can lead to an incorrect conclusion (Engqvist, 2005 ). Whether there are statistically significant interactions between the “treatment” and the covariates in the data set used by Theobald and Freeman (2014) is unclear.Open in a separate windowFigure 1.Simulated data to demonstrate heterogeneity of slopes. Pretest values were generated from random normal distributions with mean = 59.8 (SD = 18.1) for the treatment course and mean = 59.3 (SD = 17.0) for the control course, based on values given in Theobald and Freeman (2014) . For the treatment course, posttest values were calculated using the formula posttest_i = 80 + 0.1 × pre-test_i + Ɛ_i, where Ɛ_i was selected from a random normal distribution with mean = 0 (SD = 10). For the control course, posttest values were calculated using the formula posttest_i = 42 + 0.5 × pre-test_i + Ɛ_i, where Ɛ_i was selected from a random normal distribution with mean = 0 (SD = 10). n = 50 for both courses.In addition to being a strict assumption of ANCOVA, testing for homogeneity of slopes in a linear model is important in STEM education research, as slopes are likely heterogeneous for several reasons. First, for many instruments used in STEM education research, high-achieving students score high on the pretest. As a result, their ability to improve is limited due to the ceiling effect, and differences between treatment and control groups in posttest scores are likely to be minimal (Figure 1). In contrast, low-achieving students have a greater opportunity to change their scores between their pretest and posttest. Second, pedagogical innovations are more likely to have a greater impact on the learning of lower-performing students than higher-performing students. For example, Beck and Blumer (2012) found statistically greater gains in student confidence and scientific reasoning skills for students in the lowest quartile as compared with students in the highest quartile on pretest assessments in inquiry-based laboratory courses.Theobald and Freeman (2014, p. 47) note that “regression models can also include interaction terms that test whether the intervention has a differential impact on different types of students.” Yet, we argue that these terms must be included and only should be excluded if they are not statistically significant.     

Educational Standards: Mapping Who We Are and Are to Become

Educational technology supports meaningful learning and enables the presentation of spatial and dynamic images, which portray relationships among complex concepts. The Technology-Enabled Active Learning (TEAL) Project at the Massachusetts Institute of Technology (MIT) involves media-rich software for simulation and visualization in freshman physics carried out in a specially redesigned classroom to facilitate group interaction. These technology-based learning materials are especially useful in electromagnetism to help students conceptualize phenomena and processes. This study analyzes the effects of the unique learning environment of the TEAL project on students' cognitive and affective outcomes. The assessment of the project included examining students' conceptual understanding before and after studying electromagnetism in a media-rich environment. We also investigated the effect of this environment on students' preferences regarding the various teaching methods. As part of the project, we developed pre- and posttests consisting of conceptual questions from standardized tests, as well as questions designed to assess the effect of visualizations and experiments. The research population consisted of 811 undergraduate students. It consisted of a small- and a large-scale experimental groups and a control group. TEAL students improved their conceptual understanding of the subject matter to a significantly higher extent than their control group peers. A majority of the students in the small-scale experiment noted that they would recommend the TEAL course to fellow students, indicating the benefits of interactivity, visualization, and hands-on experiments, which the technology helped enable. In the large-scale implementation students expressed both positive and negative attitudes in the course survey.     

情真意浓文自美——析朱自清散文中的情致

朱自清是我国现代文学史上颇具盛名的诗人、散文家，尤以散文的成绩卓著。他的散文在体现出巨大艺术魅力的同时，以蕴涵醇正、浓郁的感情而闪耀着不朽的艺术光辉。特别是他前期的散文，更以那叙事的真切，感情的真挚打动读者心扉，产生共鸣，体现出浓郁的情致。     

Autumn Leaves Are Falling

秋叶飘落， 红色、黄色、棕色。 秋叶飘落， 摇摇摆摆。     

何洁 美丽分贝

E：这次的专辑是隔了一年多才回归的，那之前的时闯都在做些什么？何洁：在今年之前我一直在想“我到底是个什么类型的艺人？”这一年，我找到了自己，同时，签了新东家，发行了这张“最何洁”的唱片。     

浅论翻译者即叛逆者

俗话说“翻译者即叛逆者”,作者看来不然。译文的变化是为了更好的忠实于原文黹原文的“神”传达出来。忠实并不是指形式上的对等,形式上的不对等也不意味着叛逆。本文先就忠实与叛逆说起,然后从符号理论和功能对等理论出发,结合实例分析阐述了作者的观点——翻译者并非叛逆者。     

What Are Differences 2

10.too,also,either和as well作“也”讲,too与either一般用于句末,并用逗号隔开,前者用于肯定句,后者用在否定句中。too还可以用在句中,这时两边都用逗号分开。例如:He!ll go there and I!ll go there,too.You,too,may have a try.She dare not swim.I dare not,either.also和     

Are they ready?

Patsy Allen has taught in and directed preschool, secondary, and college early childhood education programs. Currently, she is writing, consulting, and practicing her parenting skills with her three children in El Paso, Texas.