andyleung teaching dossier finalandy.leung/files/cv/... · andy leung department of statistics 2207...

Andy Leung Department of Statistics

2207 Main Mall

University of British Columbia

Vancouver, BC, Canada V6T 1Z4

[email protected] • (604) 715-1893

Teaching Dossier

Table of Contents

2 Statement of Teaching Philosophy

3 Curriculum Vitae

6 Recent Teaching Responsibilities

8 Other Contributions to Teaching and

Student Learning

11 Examples of teaching materials

16 Student Evaluations

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Statement of Teaching Philosophy

Ever since my undergraduate studies I have supported teaching in many undergraduate courses,

particularly in introductory statistics. I have gained insights into teaching by supporting the transition of the

traditional in-class lecture setting to a blended-learning environment that integrates active learning (via

assistance in in-class activities) and technology (via development in WeBWorKiR, an on-line learning and

assessment tool) into the course design for many statistics courses. I also had a great opportunity to analyze a

pre- and post-course survey which allows me to gain further insight into students’ attitudes towards learning in

Statistics. All of these experiences allow me to explore and develop a philosophy on effective teaching. Here I

describe my philosophy using the following four main principles. The first two focus on my philosophy on course

design and the last two focus on course content and teaching methods.

1. Gaining hands on experience as a team in class: It is often hard to learn new material by working on

assignments, suggested exercises and example questions on our own after class. It is natural for us to seek help

from others – either peers or superiors – when we are struggling. As such, students can greatly benefit from

corporative learning via in-class activities where, when needed, they can receive timely and adequate guidance

from an instructor and can experiment with learning in a small group. In this term, I have assisted in several in-

class activities in a statistics course. I found that students engaging in a small group discussion have shown an

almost uniform improvement in their conceptual understanding. My evidence is based on the assessments

using “clicker” questions that are asked before and after the group discussion.

2. Receiving immediate feedback on-line: In many introductory undergraduate courses, where class

sizes are in the hundreds of students, grading and returning homework in a timely manner can be extremely

difficult. Students can easily lose their interest in learning when a week passes without feedback on their

submitted work. An on-line homework system provides students an assessment tool with the valuable features

of providing immediate feedback and allowing for multiple possible attempts. I believe that these features are

essential to help students constantly confirm understanding of new knowledge and increase their engagement

in learning. In addition, as an administrator of the WeBWorKiR course page for some statistics courses, I have

found that the on-line homework system is also helpful in providing the instructors immediate feedback on their

lecture based on the students’ on-line homework performance.

3. Promoting adaptation to modern approaches: Technology is ever changing, and so is the way we

interact with data. The material in the statistics undergraduate curriculum must be updated to include more

modern approaches to existing statistical problems to keep pace with what’s happening in the outside world.

For instance, I have seen some instructors use modern resampling methods (i.e., methods that require a

computer-intensive simulation to reconstruct the distribution of the test statistics) to build an intuition for

statistical inference. The outcomes thus far are very positive in that students really appreciate the new intuition,

an intuition that is less abstract but more data driven but still logically sound.

4. Emphasizing and motivating fundamental concepts: My analysis of a pre- and post-course survey

conducted in a statistics course revealed a disturbing course effect: at the end of the course, more students

believed that plain memorization and the right use of formulas were highly associated with effective learning

and high course performance. I think that the technical skills required to solve statistical exercises may have

distracted these students from developing basic statistical thinking. Therefore, throughout the years I have been

TA-ing, I repeatedly emphasize to my students that understanding the motivation of the proposed approach to a

problem is more important than memorizing a formula.

Recent Teaching Responsibilities

Below are summaries of my recent teaching responsibilities in reverse chronological order.

Course: Statistics 302

Course Description: Introduction to probability

Position: Graduate teaching assistant

Appointment period: 2013/14 Winter T2

Summary of responsibilities:

Statistics 302 is an introductory probability course open to all but specially designed for students wishing

to continue in statistics. Compared to a traditional probability course which is more theoretically-geared,

Statistics 302 is “data-driven” with a focus on how to identify the type and distribution of a random variable that

appropriately models a given data set.

I create assignment questions and assist the instructor with in-class activities. In general, I find the past

assignment questions in this course are still quite theoretically based despite its “data-driven” syllabus.

Therefore, I have suggested that we should create and I have created questions that incorporate simulated data

or figures from actual studies. An example is included on page 11. I also attend regular meetings with the

instructor and other TAs to provide my input to assignment questions created by other TAs. The total

commitment of time is approximately 3 hours per week for now. I will also be responsible for grading midterms

and final exams.


Course Description: Elementary statistics for applications

Position: Undergraduate and graduate teaching assistant

Appointment period: 2013/14 Winter T1, 2011/12 Winter T2, 2010/11 Summer T1,

2010/11 Winter T1, 2010/11 Winter T2


Statistics 200 is an introductory statistics course intended for science students. The course consists of

lectures and weekly computer labs. For most students, Statistics 200 is their first course in learning how to

analyze data, and hence many find the course not only conceptually difficult but also technically difficult. An

additional challenge for students is that they must learn to use statistical software to analyze data.

I have been TA-ing this course since the summer of 2010 when I was an undergraduate. At that time, my

responsibilities were reviewing assignment questions, writing up solution keys, and marking the assignments. In

Term 2 of 2011/12 I joined the Statistics 200 team as a graduate TA and had the opportunity to take on more

responsibilities, ranging from conducting labs to creating assignment questions. My total commitment for this

course was 6 hours per week.

When I was conducting Statistics 200 labs, I was surprised to find out that many students have minimal

experience with Excel. Although the lab handouts provided sufficient instructions on how to use Excel for the lab

activities, students had no overview of Excel. Therefore I gave small demos and provided some general tips on

using Excel in the briefing at the start of the lab. Students found these small three-minute presentations very

useful not only for completing the lab activities but also outside of the lab since Excel is a widely used

application in most working environments.

In Term 1 of 2013/14 I returned to the Statistics 200 team as an hourly graduate TA where my

responsibilities were only marking assignments and exams and holding office hours during exam period. I also

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attended regular TA meetings to discuss on general issues with the assignment and common misconception that

students had. Since I did not conduct any labs, I did not have any student evaluations.


Course Description: Design & analysis of experiment

Position: Graduate teaching assistant



Statistics 404 is an upper level undergraduate course intended for students majoring in statistics, and is

also taken by some Statistics Masters students. The course focuses on the theory and application of analysis of

variance for standard experimental designs. In general, students are expected to understand some fundamental

concepts, e.g., statistical inference, and have some experience in analyzing data with R – a statistical

programming language.

This was my first semester as a graduate teaching assistant and I particularly enjoyed the tasks of

creating lab material and interacting with the students. As the only TA of the course, I was responsible for many

aspects of the course such as creating lab material, conducting labs, holding office hours and responding to

students’ e-mail, not to mention marking assignments and exams. I also had a great deal of freedom in the

content of my labs, so I created a substantial number of examples along with R code for conducting the

corresponding analyses. Students found these extremely useful for working on the assignments. In addition, I

arranged weekly meetings with the instructor to understand the students’ current difficulties and then I

addressed them in my labs. On page 12 I have included a couple of slides that I created to reinforce a concept

that some students had difficulties with. My total time of commitment was 12 hours per week.

At the time, I developed a close relationship with a few students. Since then, I have helped some of

these students on data analysis using R when they were in their co-op placement. Some of the 404 students also

asked me for advice on pursuing postgraduate studies.

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Other Contributions to Teaching and

Student Learning

In addition to my general teaching responsibilities, I provide my other involvement in and contributions

to teaching and student learning.

Contributions to graduate student teaching and learning

I believe it is an invaluable experience to be a graduate student. We have almost unlimited

opportunities and support to acquire a wide range of skills necessary for our future professional and academic

careers. As such, I have been involved in various graduate student activities to help incoming and continuing

students access these opportunities and support. These activities include the TA training program that I have

been co-leading with another two TA trainers and various workshops for enhancing communication and

computer programming skills that were held via the Graduate Student Seminar.

• Statistics Department TA training program (09/2012 to present)

Every year, the Statistics Department offers a mandatory comprehensive TA training program for all new

TAs. The program is generally led by two to three TA trainers (experienced TAs) with the support and guidance

of a faculty member. The program includes an initial training session that provides an overview of general TA

duties along with specific training on assignment marking and lab presentation. Throughout the academic year,

TA trainers keep track of the new TAs’ teaching performance and provide feedback and support. We have

conducted an informal midterm student evaluation of the TAs, a midterm lab visitation and an end of term

formal student evaluation of the TAs. Since 2013, we have also incorporated a cultural diversity workshop to

help new TAs develop skills to create a learning environment respectful of the diversity of the UBC classroom.

In September of 2012 I was given the opportunity to assist the lead TA trainer with the TA midterm

evaluations. I conducted several TA lab visitations for a number of TAs and provided feedback and advice to

them on their teaching performance. I also had follow-up meetings with certain TAs that showed a less

satisfactory performance, to help improve their performance. All had shown significant improvement by the end

of the semester, as evidenced by an end of term TA evaluation by students.

In the following academic year, I was appointed as a co-TA trainer (i.e., co-leader) and my

responsibilities have greatly expanded. My responsibilities consist of preparing and planning all of the training,

ranging from the initial training session to the end of term student evaluation of the TAs. In addition, I have

participated in the cultural diversity workshop that is recently incorporated into our training program and I have

taken minutes of the workshops for the facilitators.

• Graduate Student Seminar (09/2012 to present)

These are weekly seminars run by graduate students and aimed at a graduate student audience, and in

general only students attend them. The seminar was started with an objective of having students present the

breadth of their research in an informal environment.

I felt that the seminar should have a broader focus to not only provide students with the opportunity to

share their research interests, but also to help students improve their skills required for their research and work.

So, since 2012, I have volunteered to take on the role of an organizer. I started reshaping the seminar by inviting

speakers to cover a wider range of topics, such as specific computational skills (e.g., how to use a server, how to

do parallel computing, how to link Fortran/C++ to R) and effective communication (e.g., how to write in a clear

and concise manner). I have also given some talks about R instruction and these are briefly described under

Instruction in R on page 10. Moreover, with the help of the department head, I have arranged a couple of

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workshops led by two facilitators from the Center for Teaching and Learning and Technology at UBC. These

workshops focused on how to give effective presentations and how to critique peers’ presentations and how to

take criticism constructively. Our graduate students in general greatly appreciate this workshop, and, in return,

we have provided the facilitators some feedback on possible improvements.

Other contributions to undergraduate teaching and learning

My contribution to undergraduate teaching and learning regularly goes beyond the scope of the regular

TA duties. I have highlighted two contributions below.

• Campus-wise introductory statistics discussion group (02/2012 to present)

This discussion group fosters cooperation and builds communication among the instructors from

Statistics and other UBC departments who teach introductory statistics. The group has discussed a wide range of

topics related to effectiveness in teaching. Currently, the group focuses on the flipped classroom course design.

Flipped classrooms provide a more active learning experience in the classroom by having students work in class

with more personalized guidance from and interaction with the instructor instead of listening to lectures. The

learning is now done at home by, for instance, watching video lectures coupled with some on-line assessment

tools. My role as a web archive manager is to attend bi-monthly meetings and to insure that all discussion and

all available resources are logged on the department website. My teaching philosophy is greatly inspired by my

attendance at this discussion group.

The following is the link to the department website that hosts the intro stats meeting resources.

Intro stats web archive: https://slate.stat.ubc.ca/slate/Slate/Misc/IntroStats/

• NSERC-USRA project on investigating student learning and attitudes on Statistics courses at UBC

(05/2011 to 08/2011)

This project investigated student learning and attitudes in Statistics courses at UBC. My role was to

conduct different analyses of attitudes towards learning before and after the course. Data collected from the

pre- and post-course surveys conducted in Statistics 200 in 2008 were analyzed.

My data analysis revealed an unfavourable course effect: at the end of the course, more students

believed that plain memorization and the right use of formulas were highly associated with effective learning

and high course performance. Dr. Dunham (the project leader) presented some of the results at a Science

Supper Series talk in March 2013. The work is still in progress (see also in CV) and we aim to publish the results

in the Statistical Education Research Journal or in a similar journal.

Another part of this project was to explore various readily available statistical software packages for use

in the Statistics 200 computer lab. I suggested Rcmdr (a simplified GUI for R, a popular and free statistics

package) and rewrote the existing lab activities to include instructions on how to use Rcmdr to perform specific

statistical analyses. My prepared materials were used in the current Statistics 200 labs. I include one example on

page 15.

Contributions to educational technology and training

I believe that education technology is becoming a fundamental tool to achieve pedagogical aims. For

instance, on-line homework is a powerful tool to enhance student learning. I have participated in the

WeBWorKiR project which is a component of the flipped classroom course design for providing on-line learning

and assessment.

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• Involvement in WeBWorKiR – an on-line homework system– project (09/2013 to present)

WeBWorK (WW) is an on-line homework system developed by academics at the University of Rochester

and is widely used by various departments in UBC. With immediate feedback on the correctness of their answers,

students can be encouraged to make multiple attempts until they succeed.

In the fall of 2013 I was given the opportunity of developing WW questions to be used in Statistics 305

(Introduction to Statistical Inference). I work closely with the project leader to create and test our WW

questions. I am also responsible for administrating the WW course page for Statistics 200 and 305. This includes,

for example, responding to all e-mail from students regarding the WW homework and amending the existing

questions based on the students’ and other TAs’ feedback.

Recently, the Statistics Department has initiated a project to customize WW by integrating it with R – a

free statistical programming language. As R can randomly generate data, create graphics, and perform most

statistical analyses, an integration of WW and R (WeBWorKiR or WWiR) allows more types of statistical

questions to be asked. Currently the project focuses on promoting WWiR in statistical education and developing

WWiR questions that will be made available globally via an on-line library.

In 2014 I am appointed to co-instruct with the project leader, Dr. Dunham, a two-day training workshop

at UBC on how to use and how to develop questions in WW. The workshop is intended for both instructors and

current teaching assistants within and outside of the Statistics Department. There are 10 attendees registered,

with 5 from outside the Statistics Department. My responsibilities as a co-instructor consist of preparing,

planning, and leading the training on the second day of the workshop about development of WW questions. As

we would like the attendees to come out of the workshop feeling comfortable in using WW and developing WW

questions on their own, I design and incorporate a sequence of challenging but not intimidating activities for

them to work on in groups. I include some activities handouts on page 13-14.

The same workshop will be given in the annual meeting of the Statistical Society of Canada (SSC 2014) in

May 2014 and I will share the teaching with Dr. Dunham. The following is the link to the description of the

workshop in 2014 SSC in Toronto:

SSC workshop: http://www.ssc.ca/meetings/2014/workshops/ed.

• Instruction in R (09/2011 to present)

In addition to my involvement in WeBWorKiR, I am also engaged in teaching researchers and students

the computer-programming skills necessary for their research.

As noted under Graduate Student Seminar, I presented on several topics about R such as parallel

computing using Rsnow and seamless linking between C++ and R using Rcpp. These skills are useful for statistics

graduate students in performing modern statistical analyses for large data sets and any analysis that requires

heavy computational power.

Recently, I have volunteered to facilitate an R workshop led by instructors from Software Carpentry

Organization. The workshop is intended for postgraduate students and other scientists who are familiar with

basic programming concepts but need help in practical tools like R that are widely used for data manipulation

and analysis.

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Examples of teaching materials

Example 1: Statistics 302 – an assignment question

My motivation for creating the following assignment question is two-fold. First I think it is important to build the

connection between the probability concepts learned and the reality. In this question, I used a real life study but

with a simulated data set as the background. Second I have noticed that students generally have difficulties

computing a statistic using a variable that has to be derived from the existing data. Therefore, I created the

following question to guide students to create a new variable, namely, the average daily heath care cost, and

then I ask them to find the expected value of this new variable.

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Example 2: Statistics 404 – a snapshot of some lab materials

In general I found students are too used to the setting of a single hypothesis test. This leads to their difficulties

adapting to multiple hypothesis testing and understanding the importance of correcting the significance level to

avoid an inflation of false positive results. Multiple testing is very common in genomics and other biology-

related fields and it is not unusual for the number of tests to be larger than 10. This motivates me to create the

following slides to reinforce this statistical idea.

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Example 3: WeBWorK workshop – activities handouts

As WeBWorK work is described in detail in Involvement in WebWorKiR – an on-line homework system – project

(see pg 10), I will skip the description here. I created the following activities for attendees to experience

WeBWorK as both a student user and a developer.

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Example 4: Statistics 200 – lab activities pre-reading instruction on how to use Rcmdr

Excel had been long used in Statistics 200 lab since it is easy to learn and is common in many workplaces, but it

supports limited features for statistical analysis. In contrast to Excel, R is a powerful scripting language for

performing statistical analysis. Rcmdr provides a nice graphical interface that uses R as the engine. With Rcmdr,

students can perform statistical analyses easily by using menus and buttons instead of command lines. I provide

the following lab material that I rewrote to include instruction on how Rcmdr can be used to perform simple

exploratory data analysis such as creating a histogram and boxplot.

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Student Evaluations

Below are summaries of my end of term student evaluations for the two courses where I had student contact,

that is, when I conducted labs.



No. of responses: 19

The TA was: Mean score (/5)

1. Well prepared 4.95

2. Helpful 4.89

3. Considerate of students 5.00

4. Easily understood 4.89

5. An effective instructor 4.95

All comments from the students:

• Andy is a really nice guy and very helpful. Always clarified info if anything was unclear during the lab.

Good job!!

• An incredibly helpful and considerate TA.

• In total, he was a very effective TA. I learnt a lot from him.



No. of responses: 19

The TA was: Mean score (/5)

1. Well prepared 4.89

2. Helpful 4.84

3. Considerate of students 4.84

4. Easily understood 4.84

5. An effective instructor 4.95

All comments from the students:

• R-code / Powerpoint helps with assignments. Helpful replies e-mails quickly. A bit soft-spoken.

• Very good TA. Clear in examples and helpful in R codes.

• Nice TA. Very helpful!!

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