Analyzing FCQ Results Using Advanced Data Analytics - Using Advanced Data...

Analyzing FCQ ResultsUsing Advanced Data AnalyticsHaoyue Zhang, Lilong Wang, Aaron Nielsen1st December 2016

IntroductionWhat is an FCQ?

Goals of Project

Data Collection andCleaning

Data Collection

Data Cleaning

Exploratory Data AnalysisCourse Ratings

Instructor Ratings

Other FCQ Results

Least Squares RegressionOrdinary Least Squares Regression

Weighted Least Squares Regression

Cluster AnalysisK-Means Clustering

Determining Number of Clusters

Clustering Results

Conclusions

Preview

1 Introduction

2 Data Collection and Cleaning

3 Exploratory Data Analysis

4 Least Squares Regression

5 Cluster Analysis

6 Conclusions

Haoyue Zhang, Lilong Wang, Aaron Nielsen Analyzing FCQ Results Using Advanced Data Analytics 1st December 2016 2/91


Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

INTRODUCTION



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Introduction: What is an FCQ?

Students at the University of Colorado campuses evaluate each of their courses andinstructors every semester

This survey is called the Faculty Course Questionnaire (FCQ for short)

Evaluations can be administered in-class or online near the end of the term



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


FCQ results can impact:

Tenure eligibility for tenure-track facultyRetention of adjunct instructorsFunding for teaching assistants



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions




Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Quantitative and qualitative data is gathered from FCQs

Quantitative data: Students rate instructors and courses in eight categories (1=lowest,6=highest) and how many hours per week they spent on course-related workQualitative data: Students are allowed to comment on the most effective and least effectiveaspects of the course



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Introduction: What can we learn from individual FCQs?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions




Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions




Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Individual numerical results aren’t particularly useful

Individual comments potentially are very helpful



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Introduction: What can we learn from course-aggregatedFCQ results?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions




Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Course-aggregated data is certainly easier to interpret rather than individual results

Many questions still remain!

Do recitations typically get higher or lower scores than lectures?Are summer classes rated higher than fall/spring courses?Does the level of the course affect scores?How much do factors like availability and challenge of the course affect overall course scores?Do tenure-track professors score higher than teaching assistants?Are easy classes rated higher?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Introduction: Goals of Project

Statistics and Modeling (Lilong and Haoyue):

Collect and clean FCQ data from Math courses from the previous two yearsConduct Tukey-inspired Exploratory Data AnalysisUse ggplot2 package in R to effectively display features of the data setUse Regression Analysis to analyze the factors that contribute most to overall course scoresPerform cluster analysis to determine if certain factors are associated with higher overall coursescores



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Introduction: Goals of Project

Management (Aaron):

Organize projectSet project deadlines and benchmarksConduct weekly meetingsMentor on various statistical methodsTroubleshoot when issues arise



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Introduction: Some Disclaimers

Higher FCQ scores are not necessarily indicative of better teaching!

Names of instructors were immediately removed from data set

We intentionally did not analyze any instructor-level results



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Higher FCQ scores are not necessarily indicative of better teaching!

Names of instructors were immediately removed from data set

We intentionally did not analyze any instructor-level results



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Some studies suggest course evaluations are biased against women and minorities

Demographic information about the instructors was, in general, not included in this analysisOne exception: we did include whether each class was taught by permanent faculty, adjunctfaculty, or a teaching assistant



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

DATA COLLECTIONAND CLEANING



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Data Collection

https://fcq.colorado.edu/ucddata.htm (or google “ucd fcq”)



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Data Collection

Aaron’s FCQ results



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Data Collection

FCQ results from the University of Colorado Denver’s Mathematics Department werecollected

Data was obtained for Fall 2014 – Spring 2016

Results from 510 classes/sections were collected

This includes lectures, recitations, online classes, in-person classes, classes at Denver campus,classes at Beijing campus

Each class/section is represented with one row in our data set

Class averages are given for each question, as opposed to individual level results



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Data Cleaning

All classes with no returned forms were removed from the data set (N/A results)

Some recitations weren’t coded properly, so this had to be fixed

Some recitation results weren’t properly exported from UCD database

Results from recitations and courses in Beijing were removed after a preliminary analysis



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

EXPLORATORY DATAANALYSIS



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Exploratory Data Analysis

Exploratory data analysis (EDA) is a method for analyzing data sets to summarize theirmain features or characteristics, typically with visual methods

EDA is useful for determining what the data can tell us without using formal hypothesistesting

John Tukey championed EDA as a method for statisticians to explore data and potentiallyformulate hypotheses that can lead to new experiments



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Initial Exploratory Data Analysis Results

FCQ data comes from all types of classes including:

Academic year and summer classesOn-campus and onlineLectures and recitationsClasses in Denver and classes in Beijing



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings

n 506

Mean 4.640

Median 4.700

SD 0.711



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: In-class vs. Online

In-Class Online

n 478 28

Mean 4.659 4.311

Median 4.700 4.300

SD 0.695 0.899



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: Lecture vs. Recitation

Lecture Recitation

n 478 28

Mean 4.649 4.486

Median 4.700 4.500

SD 0.721 0.480



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: Denver campus vs. Beijing campus

Denver Beijing

n 484 22

Mean 4.614 5.218

Median 4.700 5.300

SD 0.705 0.584



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings

Since we are mostly interested in on-campus lectures at the Denver campus, we haveexcluded recitations, online classes, and Beijing classes from our analysis.

This leaves 418 classes of FCQ results to analyze from Fall 2014 to Spring 2016.



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: Fall, Spring, Summer

Fall Spring Summer

n 220 183 25

Mean 4.616 4.674 4.632

Median 4.700 4.800 4.800

SD 0.651 0.753 0.720



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: Academic Year vs. Summer

AY Summer

n 403 25

Mean 4.642 4.632

Median 4.700 4.800

SD 0.700 0.720



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: 2014, 2015, 2016

2014 2015 2016

n 100 238 90

Mean 4.675 4.574 4.783

Median 4.700 4.700 4.850

SD 0.616 0.713 0.732



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: Undergraduate vs. Graduate Courses

Undergrad Grad

n 386 42

Mean 4.59 5.10

Median 4.70 5.25

SD 0.68 0.67



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: By Level

Lower UG Upper UG Grad

n 265 121 42

Mean 4.60 4.58 5.10

Median 4.60 4.70 5.25

SD 0.62 0.81 0.67



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings: By Title

Professor Adjunct TA

n 98 247 83

Mean 4.85 4.48 4.57

Median 5.00 4.70 4.60

SD 0.73 0.70 0.63



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Course Ratings Summary

Spring classes may receive slightly higher course scores than fall and summer classes

There is not much of a difference in course scores between academic year and summerclasses

While 2014, 2015, and 2016 have slightly different average course scores, this may beinfluenced by the semesters included

Graduate classes score much higher than undergraduate classes

No apparent difference in scores between lower and upper level undergraduate courses

Tenure-track faculty score higher than teaching assistants. Teaching assistants scorehigher than adjunct faculty



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Instructor Ratings

n 428

Mean 4.957

Median 5.200

SD 0.786



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Other FCQ Results



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Correlation Pairs Plot



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

LEAST SQUARESREGRESSION



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Ordinary Least Squares Regression

Our first attempt at modeling involved using least squares regression

The goal of this model was to predict overall course scores based on the other predictorvariables

AvailabilityChallengeHow much did you learnInstructor’s respectPrior interest in subjectInstructor’s effectiveness at encouraging interestNumber of Forms ReturnedPercent of Forms Returned (new variable)



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Ordinary Least Squares Regression (OLS) is a statistical method for estimating unknownparameters in a linear regression model

The goal of OLS is to minimize the squared differences between observed values andpredicted values



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


yi = xTi β+ εi

yi is the ith average course rating

xi is a vector of ratings (availability, challenge, etc) for course i

β is the vector of unknown parameters

Goal: Minimize SSE =∑N

i=1(yi − yi)2 =∑N

i=1(yi − xTi β)

2



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


The following is the result of fitting an ordinary least squares regression model

Coefficient Estimate Std. Error p-value

(Intercept) 0.323 0.153 0.035PriorInterest 0.091 0.020 6.3 ·10−6

InstrEffective 0.362 0.032 < 10−6

Availability 0.102 0.035 0.004Challenge -0.276 0.036 < 10−6

HowMuchLearned 0.665 0.042 < 10−6

FormsReturned -0.0002 0.002 0.903

Number of forms returned wasn’t statistically significant (α= 0.05) and removed frommodel



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Percent of forms returned was added to model






PercentFormsReturned -0.0007 0.0006 0.256

Percent of forms returned wasn’t statistically significant (α= 0.05) and removed frommodel



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Ordinary least squares regression model:








Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

OLS Model Diagnostics

No apparent non-linearity



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Potentially heavy tails on the errors



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Entry #250 has high leverage and a large residual

Should we remove it?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Let’s examine entry #250 a little more closely

It’s an online summer class which had only two people filled out the FCQ

Should we remove it now?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Instead of removing entry #250, we decided that we should use weighted least squares toaccount for the different number of forms returned



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


yi = xTi β+ εi

yi is the ith average course rating

xi is a vector of ratings (availability, challenge, etc) for course i

β is the vector of unknown parameters

Goal: Minimize SSE =∑N

i=1 wi(yi − yi)2 =∑N

i=1 wi(yi − xTi β)

2

Weights, wi , are assigned to be proportional to the number of forms returned



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Weighted least squares regression model:


(Intercept) 0.323 0.126 0.011PriorInterest 0.056 0.018 0.002InstrEffective 0.361 0.027 < 10−6

Availability 0.139 0.029 2.9 ·10−6

Challenge -0.253 0.032 < 10−6


y = 0.323+ 0.056 ·PriorInterest+ 0.361 · InstrEffective+ 0.139 · Availability− . . .0.253 ·Challenge+ 0.631 ·HowMuchLearned

R2 = 0.84



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

WLS Model Diagnostics

No apparent non-linearity



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Potentially heavy tails on the errors



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


Entry #250 no longer has high leverage (since we decreased its weight)



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Least Squares Regression Summary

Weighted Least Squares Regression is preferred to Ordinary Least Squares Regressionsince we have given aggregated data

The largest coefficients in the WLS Regression model corresponded to the variablesHowMuchLearned (β5 = 0.631) and InstrEffective (β2 = 0.361)

While challenge was positively correlated with overall course scores, after accounting forthe other variables challenge had a negative coefficient in our model (β4 =−0.253)



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

CLUSTER ANALYSIS



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Cluster Analysis

Using weighted least squares regression, we were able to explore how overall coursescores were affected by other factors (challenge, instructor availability, etc)

Next, we will use cluster analysis in attempt to determine if certain types of classestypically get higher FCQ scores.

Cluster Analysis (or clustering) is often in used in statistical data analysis where objectsare to be placed into subgroups with similar characteristics



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Cluster Analysis: Iris Example

R.A. Fisher famously published astudy on Irises in 1936

Petal length and petal width from 150Irises are plotted

Cluster analysis can help usinvestigate underlying characteristicsin each cluster

How many clusters are there? 2? 3?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Cluster Analysis: Iris Example

It turns out that there are threedifferent species of Irises in the dataset, so it would make sense if thereare three clusters



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Cluster Analysis: FCQ Scores

In the plot to the left, overall coursescores and overall instructor scoresare given in a scatter plot

What is the “optimal” number ofclusters in this data set?

Are there common characteristics(besides course and instructorscores) in the clusters?



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

K-Means Clustering

k-means clustering is one popular method used to separate data points into k subgroups,that is, determine the partition S= {S1, S2, . . . , Sk}k-means clustering determines the partition by minimizing within-cluster sum of squares(WSS)



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

K-Means Clustering

Let (x1, x2, . . . , xn) be a set of n observations, where each xk is a d-dimensional vector ofreal numbers

argminS

∑ki=1

∑

x∈Si||x−µi ||2, where µi is the i th cluster mean

In other words, we want to partition our observations in k subsets in a manner that willminimize the squared distances from each observation to its respective cluster mean



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


The k-means clustering algorithm requires that we know the number of clusters

One method for determining the “optimal” number of clusters is called the Elbow Method

Within-cluster sum of squares (WSS) is calculated for each value of k over a specifiedrange

The Elbow Method is a heuristic method that selects the “optimal” number of clusters k bychoosing the value of k such that WSS doesn’t decrease much if the number of clusters isincreased

Look for an “elbow” in a plot of WSS versus k



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions


In the plot to the left, the elbowmethod suggests k = 3, as it that isapproximately where the “elbowoccurs”



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Instructor’s Respect



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Instructor’s Respect



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Prior Interest



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Prior Interest



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Challenge



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Challenge



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: How Much Learned



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: How Much Learned



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Instructor Encourages Interest



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Instructor Encourages Interest



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Availability



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Availability



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Results: Average Number of Hours for Course



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Summary

It was determined that k=3 was the optimal number of clusters by elbow method

These clusters were named “low”, “mid” , and “high”

FCQ scores with a low instructor respect was primarily associated with “low”

FCQ scores of prior interest are similar for “high”, “mid” and “low”

FCQ scores with a low challenge was primarily associated with “low”



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Clustering Summary

Courses where students reported learning a lot were most associated with “high”

High scores with instructor’s encouragement are most associated with “high”

High scores with availability are most associated with “high”

Most people choose to study 7-9 hours no matter which cluster they belong to, however,there are lots of people in “low” that spend more time on learning than the other twoclusters



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Conclusions

During the Exploratory Data Analysis phase, the following were noted:

Overall course scores are, on average, about 0.3 points lower than overall instructor scoresRecitations, online classes, and Beijing classes appeared to be distributed differently thanstandard, on-campus lecturesThere doesn’t appear to be much of a seasonal effect on overall scoresTenure-track professors typically score higher on overall course scores than teaching assistantsand adjunct facultyGraduate courses tend to receive higher scores than undergraduate courses



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Conclusions

Least squares regression was utilized in attempt to model overall course scores as afunction of other FCQ questions (availability, challenge, etc)

Weighted least squares regression is preferred to ordinary least squares regression for this dataset since we are dealing with aggregated dataThe largest coefficients in the weighted least squared regression model corresponded to howmuch the student reported learning and how effective the instructor was at encouraging interestWhile challenge was positively correlated with overall course score, after accounting for otherfactors it had a negative regression coefficient



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Conclusions

Clustering was used to group course and instructor scores into three subgroups. Thesesubgroups were studied for common characteristics

High instructor and course scores are associated with high scores on instructor respect, howmuch was learned, and instructor encouraging interestLow instructor and course scores are associated with low scores in instructor respect, challenge,and how much learnedAmount of time spent on course work was similarly distributed for all three clusters



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Conclusions

If an instructor is aiming for high scores on FCQ results, the following are suggested:

Be available to help students as much as possibleAlways be respectful of studentsTeach students as much as possible while making it not too challenging

If all else fails, teach a graduate course!



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Conclusions

If an instructor is aiming for high scores on FCQ results, the following are suggested:

Be available to help students as much as possibleAlways be respectful of studentsTeach students as much as possible while making it not too challenging

If all else fails, teach a graduate course!



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Possible Future Work

It could be helpful to have the number of each grade given in a class (how many A’s doeseach teacher give?)

Investigate potential bias against women and minorities

Investigate if other departments follow a similar pattern



Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Works Cited

Textbooks:An Introduction to Statistical Learning (James, Whitten, Hastie, Tibshirani)Elements of Statistical Learning (Hastie, Tibshirani, Friedman)Probability and Statistics for Engineering and the Sciences (Devore)Applied Regression Models (Kutner, Nachtsheim, Neter, Li)

Websites:http://onlinecourses.science.psu.edu/stat501/node/352http://plot.ly/ggplot2/facet/http://www.r-bloggers.com/how-to-make-a-histogram-with-ggplot2/http://en.wikipedia.org/wiki/Elbow_method_(clustering)http://www.r-bloggers.com/bot-botany-k-means-and-ggplot2/http://www.sthda.com/english/wiki/ggplot2-histogram-plot-quick-start-guide-r-software-and-data-visualizationhttp://www.sthda.com/english/wiki/cluster-analysis-in-r-unsupervised-machine-learninghttp://www.statmethods.net/stats/rdiagnostics.html


http://onlinecourses.science.psu.edu/stat501/node/352

http://plot.ly/ggplot2/facet/

http://www.r-bloggers.com/how-to-make-a-histogram-with-ggplot2/

http://en.wikipedia.org/wiki/Elbow_method_(clustering)

http://www.r-bloggers.com/bot-botany-k-means-and-ggplot2/

http://www.sthda.com/english/wiki/ggplot2-histogram-plot-quick-start-guide-r-software-and-data-visualization

http://www.sthda.com/english/wiki/ggplot2-histogram-plot-quick-start-guide-r-software-and-data-visualization

http://www.sthda.com/english/wiki/cluster-analysis-in-r-unsupervised-machine-learning

http://www.sthda.com/english/wiki/cluster-analysis-in-r-unsupervised-machine-learning

http://www.statmethods.net/stats/rdiagnostics.html


Goals of Project


Data Collection

Data Cleaning


Instructor Ratings

Other FCQ Results





Clustering Results

Conclusions

Questions?


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