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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
Page 2 of 16
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.
Page 5 of 16
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: Statistics 200
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
Summary of responsibilities:
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: Statistics 404
Course Description: Design & analysis of experiment
Position: Graduate teaching assistant
Appointment period: 2011/12 Winter T1
Summary of responsibilities:
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.
Course: Statistics 200
Appointment period: 2011/12 Winter T2
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.
Course: Statistics 404
Appointment period: 2011/12 Winter T1
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!!