1 What are the highest math classes that students took?

As seen in the graph and table in the tabs below, the most frequent highest math course taken is MTH 231 in both 2019 and 2020. This course represents 36.8% and 41.0% of the maximum math courses taken in 2019 and 2020, respectively, out of courses that range from MTH 103 to MTH 432.

1.1 Highest Math Course Graph

1.2 Highest Math Course Table

2019
2020
MTH N % N %
103 0 0.0% 1 0.2%
107 3 0.6% 4 0.8%
111 4 0.8% 2 0.4%
114 3 0.6% 1 0.2%
121 51 10.7% 47 9.6%
131 51 10.7% 42 8.6%
132 1 0.2% 0 0.0%
141 9 1.9% 10 2.0%
231 176 36.9% 201 41.0%
241 30 6.3% 20 4.1%
242 38 8.0% 25 5.1%
305 1 0.2% 0 0.0%
331 2 0.4% 0 0.0%
341 14 2.9% 21 4.3%
405 1 0.2% 1 0.2%
425 0 0.0% 1 0.2%
426 0 0.0% 1 0.2%
432 1 0.2% 1 0.2%
No Record 92 19.3% 112 22.9%
Total 477 100.0% 490 100.0%

2 When did they take their highest math?

The heatmap displays the most frequent term in which the highest math class was taken, by year. The colors/values correspond to the counts/frequencies that are greater than the median count value (excluding zeros). The median frequency across all year and MTH course combinations for 2019 was 7 and was 6 for 2020 (excluding zeros). Most students took their highest math class within 4 semesters of taking GN 311. For GN 311 2019, the semester where most students took their highest MTH course was the 2018 Spring Term. For GN 311 2020, the 2019 Spring Term contained the highest frequencies of maximum MTH course taken.

NOTE: Students that may have taken their highest math class more than once are counted only once according to the semester in which their grade points were highest (which generally corresponds to the latest enrollment), and then by latest enrollment if grade points were the same across different semesters.

2.1 Graphs of Highest Math by Year Taken

2.2 2019 Table

107 111 114 121 131 132 141 231 241 242 305 331 341 405 432 Total
2020 Spring Term 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2019 Fall Term 1 0 0 2 4 1 1 14 3 4 0 1 2 0 0 33
2019 Summer Term 2 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 3
2019 Summer Term 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 4
2019 Spring Term 0 0 0 6 7 0 0 40 2 9 1 0 1 0 1 67
2018 Fall Term 0 0 2 10 9 0 3 32 9 10 0 0 3 0 0 78
2018 Summer Term 2 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 2
2018 Summer Term 1 0 0 0 0 0 0 0 1 0 2 0 0 1 0 0 4
2018 Spring Term 0 1 0 13 10 0 1 47 7 9 0 0 1 1 0 90
2017 Fall Term 2 0 1 7 11 0 1 21 4 3 0 1 0 0 0 51
2017 Summer Term 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
2017 Spring Term 0 1 0 3 1 0 1 10 3 0 0 0 5 0 0 24
2016 Fall Term 0 0 0 7 4 0 0 5 1 0 0 0 0 0 0 17
2016 Spring Term 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2015 Fall Term 0 0 0 1 2 0 1 0 0 1 0 0 0 0 0 5
2015 Summer Term 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
2014 Fall Term 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1
2014 Spring Term 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
2013 Fall Term 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
2013 Spring Term 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2011 Summer Term 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2011 Spring Term 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
2010 Fall Term 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
Total 3 4 3 51 51 1 9 176 30 38 1 2 14 1 1 385

2.3 2020 Table

103 107 111 114 121 131 141 231 241 242 341 405 425 426 432 Total
2020 Spring Term 1 1 1 0 2 7 0 21 1 4 3 0 0 1 0 42
2019 Fall Term 0 0 0 0 3 3 1 43 2 7 1 1 1 0 0 62
2019 Summer Term 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
2019 Summer Term 1 0 0 0 0 0 2 0 1 0 1 0 0 0 0 0 4
2019 Spring Term 0 0 1 0 7 9 1 45 5 3 6 0 0 0 1 78
2018 Fall Term 0 1 0 0 12 5 3 35 2 6 1 0 0 0 0 65
2018 Summer Term 2 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 2
2018 Summer Term 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2
2018 Spring Term 0 1 0 0 9 5 0 28 3 0 4 0 0 0 0 50
2017 Fall Term 0 0 0 0 10 8 4 12 2 3 3 0 0 0 0 42
2017 Summer Term 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
2017 Spring Term 0 0 0 0 2 0 0 7 2 0 1 0 0 0 0 12
2016 Fall Term 0 0 0 1 1 2 0 5 1 0 0 0 0 0 0 10
2016 Spring Term 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
2015 Fall Term 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1
2015 Summer Term 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2014 Fall Term 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1
2014 Spring Term 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2013 Fall Term 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
2013 Spring Term 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
2011 Summer Term 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
2011 Spring Term 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2010 Fall Term 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Total 1 4 2 1 47 42 10 201 20 25 21 1 1 1 1 378

3 How many students have taken ST 311? (prior to enrollment in GN 311)

193 students took ST 311 prior to taking GN 311 in the fall of 2019. 213 students took ST 311 prior to taking GN 311 in the spring of 2020. This table excludes students that took ST 311 during their enrollment in GN 311.

NOTE: Students that may have taken ST 311 more than once are counted only once according to the semester in which their grade points were highest (which generally corresponds to the latest enrollment), and then by latest enrollment if grade points were the same across different semesters.

3.1 Table

2019
2020
Term N % N %
2019 Fall Term 0 0.0% 67 31.5%
2019 Summer Term 2 2 1.0% 0 0.0%
2019 Summer Term 1 3 1.6% 3 1.4%
2019 Spring Term 50 25.9% 51 23.9%
2018 Fall Term 62 32.1% 45 21.1%
2018 Summer Term 1 3 1.6% 2 0.9%
2018 Spring Term 33 17.1% 23 10.8%
2017 Fall Term 20 10.4% 10 4.7%
2017 Summer Term 2 1 0.5% 0 0.0%
2017 Spring Term 12 6.2% 9 4.2%
2016 Fall Term 4 2.1% 2 0.9%
2016 Spring Term 0 0.0% 1 0.5%
2015 Fall Term 1 0.5% 0 0.0%
2014 Fall Term 1 0.5% 0 0.0%
2011 Fall Term 1 0.5% 0 0.0%
Total 193 100.0% 213 100.0%

4 How many people are currently taking ST 311? (while enrolled in GN 311)

36 students were enrolled in ST 311 while taking GN 311 during the fall of 2019. 28 students are currently enrolled in ST 311 and GN 311 for the spring, 2020.

NOTE: Few students may have also taken ST 311 prior to current ST 311 course enrollment with respect to their GN 311 cohort.

4.1 Table

Enrolled
Not Enrolled
N % N %
2019 36 8.2% 441 92.5%
2020 28 5.7% 462 94.3%

5 What was the average GPA for ST 311?

The average GPA for students who took ST 311 and GN 311 during Fall 2019 (indicated by the ‘Current (2019)’ row) is 3.0. The average GPA for students who did not take (or are not taking) ST 311 concurrently with GN 311, in 2019 and 2020 (middle row), is 3.3. The overall average GPA including all students (see notes below) is 3.1.

5.1 Table

Min Q1 Median Mean Q3 Max N
Current (2019) 1.0 2.7 3.0 3.0 3.3 4.0 36
Prior 1.0 3.0 3.3 3.3 4.0 4.3 404
Overall 0.0 2.7 3.3 3.1 4.0 4.3 468

6 How many students got Homework 1 question 10 correct? How many got Homework 2 question 9 correct?

The table below displays 2020 in blue. The counts and percentages of correct, incorrect, and ‘No Record’ responses are indicated in the columns below.

6.1 Table

2019
2020
Correct
Incorrect
No Record
Correct
Incorrect
No Record
N % N % N % N % N % N %
Q9 343 71.9% 114 23.9% 20 4.2% 378 77.1% 95 19.4% 17 3.5%
Q10 321 67.3% 139 29.1% 17 3.6% 320 65.3% 154 31.4% 16 3.3%
Both 246 51.6% 214 44.9% 17 3.6% 268 54.7% 206 42.0% 16 3.3%

7 Does it make a difference whether someone has taken ST 311 or not as to whether they got question 10 or 9 correct?

7.1 Table

Below is a frequency table for questions 9, 10 and both that tabulates correct, incorrect and no record responses by whether or not ST 311 was taken. The first two rows are for 2019 and the second two rows are for 2020. These questions are prior to intervention in the spring 2020 semester so we would roughly expect to see the same trends across 2019 and 2020 assuming similar populations of students.

Q9
Q10
Both
Correct Incorrect No Record Correct Incorrect No Record Correct Incorrect No Record
2019 Not Taken 175 60 13 169 70 9 125 114 9
Took 311 168 54 7 152 69 8 121 100 8
2020 Not Taken 202 39 10 168 73 10 150 91 10
Took 311 176 56 7 152 81 6 118 115 6

7.2 Significant Differences

Using a binomial glm model logodds(BothCorrect) ~ HighestMTH + tookST311 + ST311current did not yield any significant factors.

Marginal binomial models for the log odds of answering each separate question correctly, using whether or not a student has taken ST 311 as the factor of interest indicated no significant association of taking ST 311 and grades for 2019 on correct answers for each question and both combined. The same results were found for 2020 except for question 10 where the model indicates a significant association between taking ST 311 and correct answers at the .032 significane level using a Wald Test. However, the chi-square test does not indicate significance and accounting for the multiple tests, the association is not likely significant.

‘No Record’ responses were excluded from these models.

Below is the summary output for the binomial model coefficients for question 9 in 2019 along with the chi-square association test. The last two rows are the same models for question 9 in 2020. All other tests of interest are similar in that there is no evidence of statistically significant association between taking ST 311 on answer status.

##                      Estimate Std. Error    z value     Pr(>|z|)
## (Intercept)       -1.07044141  0.1496025 -7.1552365 8.352878e-13
## tookST311Took 311 -0.06453852  0.2164527 -0.2981645 7.655776e-01
## Number of cases in table: 477 
## Number of factors: 2 
## Test for independence of all factors:
##  Chisq = 1.5042, df = 2, p-value = 0.4714
##                     Estimate Std. Error   z value     Pr(>|z|)
## (Intercept)       -1.6447061  0.1749043 -9.403462 5.279685e-21
## tookST311Took 311  0.4995737  0.2326595  2.147231 3.177488e-02
## Number of cases in table: 490 
## Number of factors: 2 
## Test for independence of all factors:
##  Chisq = 5.069, df = 2, p-value = 0.0793

8 Analysis in regard to HW 6 questions

Intervention occurred between homework 2 and homework 6. We want to look at any possible impact the intervention may have had. There are a number of questions from homework 6 to consider here. There are binary indicators of correct/incorrect for each and one summary average variable that aggregates across the questions.

For all of the questions, ignoring missing values (students that didn’t complete the question and students that didn’t have a cohort) the first quartile of binary scores is 1. This indicates that 75% of people got an individual question correct. The sample proportion of correct responses for each question is given below as well as the summary stats on the aggregate variable.

means <- full_data %>% group_by(Cohort) %>% select(Cohort, ends_with("bin")) %>% 
  summarize_if(.predicate = function(x) is.numeric(x), .funs = funs(mean="mean"), na.rm = TRUE) 
kable(means[1:2,], col.names = c("Cohort", paste("Q", c(3:10, 15:19)), "Aggregate"), digits = 3) %>% 
  kable_styling(bootstrap_options = "striped",full_width = T)
Cohort Q 3 Q 4 Q 5 Q 6 Q 7 Q 8 Q 9 Q 10 Q 15 Q 16 Q 17 Q 18 Q 19 Aggregate
Fall2019 0.978 0.978 0.876 0.868 0.868 0.863 0.857 0.854 0.881 0.951 0.911 0.867 0.880 0.900
Spring2020 0.969 0.966 0.921 0.906 0.901 0.864 0.841 0.846 0.890 0.959 0.931 0.927 0.921 0.921

Looking closely at the questions themselves, I’m going to remove question 8, 9, and 10 (Keeping all decimal digits during your calculations and rounding your answer to 3 decimal digits, what is the calculated chi-square value? What is the number for the degrees of freedom? What is the critical value? Round to two decimal places.) as these weren’t things focused on in the intervention.

full_data <- select(full_data, -HW6_Q_8_score_bin, -HW6_Q_9_score_bin, - HW6_Q_10_score_bin)

Conducting a quick comparison of these sample proportions by question using a logistic regression model to get a p-value (assumptions likely not met but still somewhat useful).

full_data <- full_data[!is.na(full_data$Cohort),]

#create indicator of math above 100 level or not
full_data$MTHLevel <- ifelse(full_data$MTHcourse > 200, "200's", "100's")

sum_glm <- function(data, response){ 
  fit <- glm(data[[response]] ~ Cohort, data = data)
  means <- aggregate(data[[response]] ~ Cohort, data = data, FUN = mean)
  resp <- gregexpr(response, pattern = "(Q_\\d+)")
  ret <- c(substring(response, resp[[1]], resp[[1]]+attr(resp[[1]],'match.length')-1), round(c(fit$coefficients, means[,2], means[2,2]-means[1,2], summary(fit)$coefficients[2,4]), 4))
  names(ret) <- c("Response", "Intercept", "Beta1", "2019 mean", "2020 mean", "Difference", "p-value")
  ret
}

bin_names <- c("HW6_Q_3_score_bin", "HW6_Q_4_score_bin", "HW6_Q_5_score_bin",
               "HW6_Q_6_score_bin", "HW6_Q_7_score_bin",  "HW6_Q_15_score_bin",
               "HW6_Q_16_score_bin", "HW6_Q_17_score_bin", "HW6_Q_18_score_bin",
           "HW6_Q_19_score_bin")
lapply(X = bin_names, FUN = sum_glm, data = full_data)
## [[1]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_3"   "0.9785"  "-0.0097"   "0.9785"   "0.9688"  "-0.0097"   "0.4166" 
## 
## [[2]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_4"   "0.9784"  "-0.0125"   "0.9784"   "0.9659"  "-0.0125"   "0.3053" 
## 
## [[3]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_5"    "0.876"   "0.0453"    "0.876"   "0.9213"   "0.0453"   "0.0461" 
## 
## [[4]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_6"   "0.8679"   "0.0385"   "0.8679"   "0.9064"   "0.0385"   "0.1058" 
## 
## [[5]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_7"   "0.8679"    "0.033"   "0.8679"   "0.9009"    "0.033"   "0.1704" 
## 
## [[6]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_15"   "0.8808"   "0.0091"   "0.8808"   "0.8899"   "0.0091"   "0.7037" 
## 
## [[7]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_16"   "0.9514"   "0.0081"   "0.9514"   "0.9594"   "0.0081"   "0.6027" 
## 
## [[8]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_17"   "0.9111"   "0.0198"   "0.9111"   "0.9308"   "0.0198"   "0.3278" 
## 
## [[9]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_18"   "0.8672"   "0.0599"   "0.8672"   "0.9271"   "0.0599"   "0.0088" 
## 
## [[10]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_19"   "0.8804"   "0.0408"   "0.8804"   "0.9213"   "0.0408"   "0.0696"

Three of these are significant or marginally signficant and in the direction we’d hope for. That’s a good sign.

We’ll also consider just the students that missed at least one question from the earlier homework assignment. Perhaps they are the ones that will show improvement.

#filter by missing an earlier question or having no record
missed_data <- filter(full_data, bothQscorrect != 1)
lapply(X = bin_names, FUN = sum_glm, data = missed_data)
## [[1]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_3"   "0.9665"  "-0.0242"   "0.9665"   "0.9423"  "-0.0242"   "0.2873" 
## 
## [[2]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_4"   "0.9719"   "-0.036"   "0.9719"   "0.9359"   "-0.036"   "0.1135" 
## 
## [[3]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_5"   "0.8212"   "0.0359"   "0.8212"   "0.8571"   "0.0359"   "0.3834" 
## 
## [[4]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_6"   "0.8212"    "7e-04"   "0.8212"   "0.8219"    "7e-04"   "0.9872" 
## 
## [[5]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##      "Q_7"   "0.8156"    "7e-04"   "0.8156"   "0.8163"    "7e-04"   "0.9874" 
## 
## [[6]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_15"   "0.8475"   "0.0192"   "0.8475"   "0.8667"   "0.0192"    "0.623" 
## 
## [[7]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_16"   "0.9438"  "-0.0034"   "0.9438"   "0.9404"  "-0.0034"   "0.8949" 
## 
## [[8]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_17"   "0.8939"   "-0.005"   "0.8939"   "0.8889"   "-0.005"   "0.8851" 
## 
## [[9]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_18"   "0.8079"   "0.0847"   "0.8079"   "0.8926"   "0.0847"   "0.0346" 
## 
## [[10]]
##   Response  Intercept      Beta1  2019 mean  2020 mean Difference    p-value 
##     "Q_19"   "0.8239"   "0.0755"   "0.8239"   "0.8993"   "0.0755"    "0.052"

Looking like just the last two questions showed improvement (none went the wrong way) when averaged across things.

9 HW 6 Q’s broken down - all students

To further investigate the effect of the intervention we’ll look at a break down comparing cohorts for correct/incorrect question status by things like:

  • highest math course taken (>200 or <200) (MTHLevel)
  • ST 311 status (not taken or taken) (tookST311)
  • class level (senior, etc.) (NC_LVL_BOT_DESCR)
  • race (STDNT_RACE_IPEDS)
  • sex (STUDENT_GENDER_IPEDS)
  • transfer status (ORIG_ENROLL_STATUS)
  • rural status(RuralStatus)
  • first gen status (FirstGenStatus)

First just the means across some combinations.

9.1 By Cohort, Math Level, Transfer, and First Gen

out2 <- lapply(X = bin_names, FUN = q_means, data = full_data, preds = preds[c(1,2,7,9)])

out2e <- lapply(FUN = nice_it, X= out2)

for(x in 1:length(out2e)){
  print(kable(out2e[[x]], digits = 3, col.names = c("MathLevel", "Transfer", "First Gen", "Avg2019", "Avg2020", "n2019", "n2020", "AvgDiff"), caption = paste0("Question ", qs[x], " Summary")) %>% 
          kable_styling(bootstrap_options = "striped",full_width = T) )
}
Question 3 Summary
MathLevel Transfer First Gen Avg2019 Avg2020 n2019 n2020 AvgDiff
100’s New Student First Gen 0.909 1.000 11 10 0.091
200’s New Student First Gen 1.000 0.976 33 42 -0.024
100’s New Transfer Student First Gen 1.000 1.000 9 5 0.000
200’s New Transfer Student First Gen 1.000 0.818 8 11 -0.182
100’s New Student Not First 0.975 0.984 80 63 0.009
200’s New Student Not First 0.979 0.978 190 180 -0.001
100’s New Transfer Student Not First 1.000 1.000 11 11 0.000
200’s New Transfer Student Not First 0.957 1.000 23 17 0.043
100’s New Student Unknown 1.000 0.667 3 3 -0.333
200’s New Student Unknown 1.000 1.000 2 3 0.000
100’s New Transfer Student Unknown NA 1.000 NA 3 NA
200’s New Transfer Student Unknown 1.000 0.500 2 4 -0.500
Question 4 Summary
MathLevel Transfer First Gen Avg2019 Avg2020 n2019 n2020 AvgDiff
100’s New Student First Gen 0.909 1.000 11 10 0.091
200’s New Student First Gen 1.000 1.000 33 42 0.000
100’s New Transfer Student First Gen 1.000 0.600 9 5 -0.400
200’s New Transfer Student First Gen 1.000 0.818 8 11 -0.182
100’s New Student Not First 0.962 0.984 79 63 0.022
200’s New Student Not First 0.984 0.983 190 179 -0.001
100’s New Transfer Student Not First 1.000 1.000 11 11 0.000
200’s New Transfer Student Not First 0.957 0.941 23 17 -0.015
100’s New Student Unknown 1.000 0.667 3 3 -0.333
200’s New Student Unknown 1.000 1.000 2 3 0.000
100’s New Transfer Student Unknown NA 1.000 NA 3 NA
200’s New Transfer Student Unknown 1.000 0.500 2 4 -0.500
Question 5 Summary
MathLevel Transfer First Gen Avg2019 Avg2020 n2019 n2020 AvgDiff
100’s New Student First Gen 0.636 0.667 11 9 0.030
200’s New Student First Gen 0.970 0.927 33 41 -0.043
100’s New Transfer Student First Gen 0.778 1.000 9 5 0.222
200’s New Transfer Student First Gen 0.750 0.818 8 11 0.068
100’s New Student Not First 0.838 0.935 80 62 0.098
200’s New Student Not First 0.905 0.926 190 176 0.021
100’s New Transfer Student Not First 0.909 1.000 11 10 0.091
200’s New Transfer Student Not First 0.818 1.000 22 17 0.182
100’s New Student Unknown 0.667 1.000 3 2 0.333
200’s New Student Unknown 1.000 1.000 2 3 0.000
100’s New Transfer Student Unknown NA 0.667 NA 3 NA
200’s New Transfer Student Unknown 1.000 0.667 2 3 -0.333
Question 6 Summary
MathLevel Transfer First Gen Avg2019 Avg2020 n2019 n2020 AvgDiff
100’s New Student First Gen 0.636 0.750 11 8 0.114
200’s New Student First Gen 0.970 0.878 33 41 -0.092
100’s New Transfer Student First Gen 0.778 0.800 9 5 0.022
200’s New Transfer Student First Gen 0.750 0.909 8 11 0.159
100’s New Student Not First 0.838 0.903 80 62 0.066
200’s New Student Not First 0.889 0.920 190 176 0.031
100’s New Transfer Student Not First 0.909 1.000 11 10 0.091
200’s New Transfer Student Not First 0.818 0.941 22 17 0.123
100’s New Student Unknown 0.667 1.000 3 2 0.333
200’s New Student Unknown 1.000 1.000 2 3 0.000
100’s New Transfer Student Unknown NA 0.667 NA 3 NA
200’s New Transfer Student Unknown 1.000 0.667 2 3 -0.333
Question 7 Summary
MathLevel Transfer First Gen Avg2019 Avg2020 n2019 n2020 AvgDiff
100’s New Student First Gen 0.636 0.556 11 9 -0.081
200’s New Student First Gen 0.970 0.878 33 41 -0.092
100’s New Transfer Student First Gen 0.778 0.800 9 5 0.022
200’s New Transfer Student First Gen 0.750 0.818 8 11 0.068
100’s New Student Not First 0.838 0.935 80 62 0.098
200’s New Student Not First 0.895 0.915 190 176 0.020
100’s New Transfer Student Not First 0.909 1.000 11 10 0.091
200’s New Transfer Student Not First 0.818 0.941 22 17 0.123
100’s New Student Unknown 0.667 1.000 3 2 0.333
200’s New Student Unknown 1.000 1.000 2 3 0.000
100’s New Transfer Student Unknown NA 0.667