Formative Assessment in Math
1. Introduction When discourses of education are reduced to school level, the educational process can be divided into three steps: first is inputs such as teachers, students, management rules, curriculums, and parental anxiety, second is classroom situation where actual interaction of teaching and learning takes place, and the last is outcomes such as students’ competency, teachers’ job satisfaction, and test results. While investing more resources to inputs and evaluating outputs are objective and visible, handling or promoting intercourse between teachers and students varies in every classroom situation, which are because the inputs and outputs are quantitative and classroom situations are qualitative.
Child Parent interaction in Korea
1. Introduction The data is collected from PISA(Programme for International Student Assessment). Country: Korea Students’ age: HighSchool 1stGrade A fosterer has responded to several questionnaire. And this report analyzes data obtained from those questionnaire. Explanation of variables Early : Child regularly attended prior to <grade 1 in ISCED 1>: Early childhood educational development […] 0: No, 1: Yes Care : , participation hindered: I had no one to take care of my child/ children.
About Happiness-Priceless life
Priceless Life Intro 약 1년 전, “행복의 과학”이라는 수업을 수강하고 있는 친구로부터 “사람은 행복하기 위해 사는 게 아니고, 살기 위해 행복해지려고 하는 거래!”라는 말을 들었다. 행복이라는 것 이전에 삶(존재)이 있다는 말이다. 처음 그 말을 들었을 때는 일리 있는 말이라고만 생각했다. 그런데 몇 달 전 문득 그 말의 진짜 의미를 깨닫게 됐다. Happiness, Analogous to Battery 인간은 목적성 없이 태어났다. 부모님은 어떠한 목적을 갖고 나를 낳으셨을 수도 있지만 적어도 나는 무엇인가를 위해 태어나지 않았고 그렇게 할 수도 없다.
Constructing Hospital Score Using PCA
1. EDA reading csv file merging plots hpdata <- inner_join(table_1, table_2, by='hpid') selecting variables hpdata <- hpdata %>% select(dutyName.x, starts_with('h'), starts_with('mk'))%>% select(-hv1, -hv12, -hvidate) glimpse(hpdata) ## Rows: 313 ## Columns: 36 ## $ dutyName.x <chr> "의료법인강릉동인병원", "강릉아산병원", "강원도삼척의료원", "강원도속초의료원", "의료법인보광의... ## $ hpid <chr> "A2200005", "A2200008", "A2200007", "A2200012", "A220004... ## $ hv10 <chr> "0", "Y", "0", "0", "0", "0", "0", "Y", "Y", "Y", "0", "... ## $ hv11 <chr> "0", "Y", "Y", "0", "0", "0", "0", "Y", "Y", "Y", "0", ".
-Data-Analysis-Investigating-Relationships-Between-Variables
Selecting Model 1. Making Table ## freq S E I ## 1 14 1 1 1 ## 2 483 1 0 1 ## 3 497 0 1 1 ## 4 1008 0 0 1 ## 5 1105 1 1 0 ## 6 411111 1 0 0 ## 7 4624 0 1 0 ## 8 157342 0 0 0 2. Fitting All Possible Models fit1 <- glm(freq~S*E*I, family=poisson(link='log'), data=tab) fit2 <- glm(freq~S+E+I+S:E+S:I+E:I, family=poisson(link='log'), data=tab) fit3 <- glm(freq~S+E+I+S:E+S:I, family=poisson(link='log'), data=tab) fit4 <- glm(freq~S+E+I+S:E+E:I, family=poisson(link='log'), data=tab) fit5 <- glm(freq~S+E+I+S:I+E:I, family=poisson(link='log'), data=tab) fit6 <- glm(freq~S+E+I+S:E, family=poisson(link='log'), data=tab) fit7 <- glm(freq~S+E+I+S:I, family=poisson(link='log'), data=tab) fit8 <- glm(freq~S+E+I+E:I, family=poisson(link='log'), data=tab) fit9 <- glm(freq~S+E+I, family=poisson(link='log'), data=tab) 3.