finish!

Assignment 5.Rmd

@@ -16,14 +16,17 @@ library(party)
 
 ## Part I
 ```{r}
-D1 <- 
+D1 <- read.csv("intelligent_tutor.csv")
 ```
 
 ##Classification Tree
 First we will build a classification tree to predict which students ask a teacher for help, which start a new session, or which give up, based on whether or not the student completed a session (D1$complete) and whether or not they asked for hints (D1$hint.y). 
 ```{r}
 
-c.tree <- rpart(action ~ hint.y + complete, method="class", data=D1) #Notice the standard R notion for a formula X ~ Y
+c.tree <- rpart(action ~ hint.y + complete, method="class", data=D1)
+
+
+#Notice the standard R notion for a formula X ~ Y
 
 #Look at the error of this tree
 printcp(c.tree)
@@ -40,41 +43,55 @@ We want to see if we can build a decision tree to help teachers decide which stu
 
 #Visualize our outcome variable "score"
 ```{r}
-
+hist(D1$score)
 ```
 
 #Create a categorical outcome variable based on student score to advise the teacher using an "ifelse" statement
 ```{r}
-D1$advice <- 
+D1$advice <- ifelse(D1$score <=0.4,"intervene", ifelse(D1$score>0.4 & D1$score<=0.8,"monitor", "no action"))
+
 ```
 
 #Build a decision tree that predicts "advice" based on how many problems students have answered before, the percentage of those problems they got correct and how many hints they required
 ```{r}
-score_ctree <- 
+#regress advice on the other three variables
+score_ctree <- ctree(factor(advice) ~ prior_prob_count + prior_percent_correct + hints, D1)
 ```
 
 #Plot tree
 ```{r}
-
+plot(score_ctree)
 ```
 
 Please interpret the tree, which two behaviors do you think the teacher should most closely pay attemtion to?
-
+1.For students who ask for over 12 times of hints and still ask for 40% of intervene and 60% of monitoring, teachers should pay more attention to them.
+2. For 40% students who ask no hints and grades lower than 85 and ask for no actions is also problematic, since they do not understand and they don't ask for hints or any other intervence. 
 #Test Tree
 Upload the data "intelligent_tutor_new.csv". This is a data set of a differnt sample of students doing the same problems in the same system. We can use the tree we built for the previous data set to try to predict the "advice" we should give the teacher about these new students. 
 
 ```{r}
 #Upload new data
 
-D2 <- 
+D2 <- read.csv("intelligent_tutor_new.csv")
 
 #Generate predicted advice using the predict() command for new students based on tree generated from old students
 
-D2$prediction <- 
+D2$prediction <- predict(score_ctree,D2)
 
 ``` 
 ## Part III
 Compare the predicted advice with the actual advice that these students recieved. What is the difference between the observed and predicted results?
+```{r}
+T1 <-table(D2$prediction)
+
+#monitor
+T1[2]/sum(T1)
+
+#no action
+T1[3]/sum(T1)
+
+since everybody gets 1, and that means everybody should get no action. But our model says some students need monitor, so that means our model is not perfect!
+```
 
 ### To Submit Your Assignment
 

assignment5.Rproj

@@ -0,0 +1,13 @@
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