finish!

Assignment 5.Rmd

@@ -8,23 +8,20 @@ For this assignment we will be using data from the Assistments Intelligent Tutor
 
 #Install & call libraries
 ```{r}
-install.packages("party", "rpart")
-
+#install.packages("party")
 library(rpart)
 library(party)
 ```
 
 ## Part I
 ```{r}
-D1 <- 
+D1 <- read.csv("intelligent_tutor.csv", sep = ",", header = TRUE)
 ```
 
 ##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
-
 #Look at the error of this tree
 printcp(c.tree)
 
@@ -40,42 +37,55 @@ 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 <- 
+score_ctree <- ctree(factor(advice) ~ prior_prob_count + prior_percent_correct + hints, D1)
+?ctree
 ```
 
 #Plot tree
 ```{r}
-
+plot(score_ctree)
 ```
 
 Please interpret the tree, which two behaviors do you think the teacher should most closely pay attemtion to?
 
+Interpret from this plot, the students who are asking for hints for less than 12 times and have less than 63% questions answered correctly before this session are very likely to need intervention (node7). Other than that, students who do not ask for any hints, while have answered less than 85 questions correctly are also quite likely need interventions(node 3). Therefore, I think "students that ask for less than 12 hints" and "have answered less than 85 questions correctly" are the two most important behaviors that teachers should pay close attention to. 
+
+
+
 #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", header = TRUE) 
 
 #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}
+# all the students in the new dataset achieved a score of 1.0, therefore, the accuracy of our model's prediction would be:
+mean(ifelse(D2$prediction == "no action", 1, 0))
+
+
+```
+We can see that based on the model we've generated from the old data, the degree of accuracy predicting the new data is only 58%. I don't think this is a very accurate prediction.
+
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