故事讲述方式对比:现场与录音对贫困背景学龄前儿童复述效果的影响

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本文是一篇学术论文,主要探讨了"现场讲述与录音讲述对来自低社会经济背景的学龄前儿童复述能力的影响"(Effects of Live and Recorded Storytelling on Retelling Performance of Preschool Children from Low Socioeconomic Backgrounds)。作者大卫·E·坎贝尔和托尼娅·A·坎贝尔,来自休斯顿大学的研究者,设计了一项实验来测试假设,即现场阅读能够提高儿童的记忆和理解力。 研究方法是将34名来自低收入家庭的学龄前儿童随机分配到两个条件组:现场阅读组和录音阅读组。在实验中,孩子们分别听取老师或录音讲述的故事,然后用自己的语言重新讲述。研究发现,现场阅读组的孩子在复述过程中使用的词汇量显著更多,而且主题表达也更准确,这表明现场互动对儿童的理解和记忆有显著优势,其效果优于单纯的录音听讲(p<.01)。 这篇论文的结论对于教育实践具有重要意义,它强调了人际交流在语言学习过程中的不可替代性。尽管小学教师面临丰富的教学材料选择,包括如佩伯利语言发展套装(American Guidance Service, 1968)这样的教育资源,其中包含要求孩子听录音并重复故事的课程。然而,该研究的结果提醒教育者,在利用这些多媒体工具的同时,不应忽视现场讲述对孩子口头表达能力和思维发展的重要性。 进一步的研究可以探索如何在不同教育环境下有效地结合现场讲述和录音技术,以最大化地提升所有儿童的学习效果,特别是那些来自社会经济劣势背景的孩子。这可能涉及到创新的教学策略,比如混合式学习模式,既能提供录音作为辅助,又能确保儿童有机会参与到实时、互动的故事体验中。这项研究为早期教育领域的实践者提供了宝贵的指导,以优化教学方法,促进儿童全面的语言和认知发展。

GOAL Perform a Poisson regression to predict the number of people in a househouse based on the age of the head of the household. DATA The Philippine Statistics Authority (PSA) spearheads the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three years, is aimed at providing data on family income and expenditure, including levels of consumption by item of expenditure. The data, from the 2015 FIES, is a subset of 1500 of the 40,000 observations (Philippine Statistics Authority 2015). The data set focuses on five regions: Central Luzon, Metro Manila, Ilocos, Davao, and Visayas. The data is in the file fHH1.csv. Each row is a household, and the follow variables are recorded: • location: where the house is located (Central Luzon, Davao Region, Ilocos Region, Metro Manila, or Visayas) • age: the age of the head of household • total: the number of people in the household other than the head • numLT5: the number in the household under 5 years of age • roof: the type of roof in the household (either Predominantly Light/Salvaged Material, or Predominantly Strong Material. STEPS 1. Read in the dataset. 2. Produce a bar-chart of total 3. Produce a scatter-plot of total against age - add a smoothing line. 4. Fit the Poisson regression total ∼ age 5. Interpret the coefficient of age. 6. Obtain the Pearson residuals. Plot these against age. Is the model adequate? 7. Fit the Poisson regression total ∼ age + age2 8. Repeat the residual plots for the new model. 9. Compare the models using a likelihood ratio test, and AIC. 10. Calculate the predicted values for model M2. What is the age of the head of the household associated with the largest fitted value 使用R语言

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Consider the following information about the pharmacies, patients and drugs: ● (1) Patients are identified by an SSN, and their names, addresses, and ages must be recorded. ● (2) Doctors are identified by an SSN. For each doctor, the name, specialty, and years of experience must be recorded. ● (3) Each pharmaceutical company (制药公司) is identified by name and has a phone number. ● (4) For each drug, the trade name and formula(成份)must be recorded. Each drug is produced by a given pharmaceutical company, and the trade name identifies a drug uniquely from among the products of that company. ● (5) Each pharmacy(药房) has a name, address, and phone number. Each pharmacy is identified by ID. ●(6) Every patient has a primary doctor. Every doctor has at least one patient. ● (7) Each pharmacy sells several drugs and has a price for each. A drug could be sold at several pharmacies, and the price could vary from one pharmacy to another. ● (8) Doctors prescribe drugs for patients. A doctor could prescribe one or more drugs for several patients, and a patient could obtain prescriptions from several doctors. Each prescription has a date and a quantity associated with it. ●(9) Pharmaceutical companies have long term contracts with pharmacies. A pharmaceutical company can contract with several pharmacies, and a pharmacy can contract with several pharmaceutical companies. For each contract, you have to store a start date, an end date. 用ER图进行描述用python代码表示

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