Teaching portfolio

Teaching philosophy

My teaching philosophy is: Assessment-directed self-learning. I explain why the work is important and where it fits in the big picture. I equip them with tools and show them how to learn. I use assessment activities to guide students as to when to learn what.

For more detail on my philosophy see my Thoughts on Teaching, consultation, and research.


In the workplace my students will sit in front of a computer and search the internet for approaches to solving the problems they encounter. So in my courses the students must sit in front of a computer and search the internet for approaches to solving the problems they encounter.

This connection should not be a revolutionary idea, and yet right now it is. I am a strong driver for change towards giving students the graduate attributes they will use in the workplace. I want to equip my students to be part of the information age and the fourth industrial revolution.


Almost all my assessments are open-book and open-internet. Students do the assessments in front of a computer, and submit their work electronically via the learning management system (Blackboard).

A perk of this approach was that adjusting my course to the pandemic was straightforward. I am ready for the new normal.

In the workplace students will often be part of a team, and yet assessed individually. Thus, in my courses students can communicate on most assessments, but in the end they are mostly assessed individually.

Of course, to achieve this one needs to move away from the old-school thinking on assessment. Among others, I do the following to achieve reliable individual assessments:

  1. Raise the cognitive level: I require students to apply techniques, analyse the results, and evaluate the meaning of the results in practical terms. Marks are awarded for the thinking process and logical steps taken, not for giving the answer.
  2. I give each student slightly different data according to the patterns I wish to assess. Thus, each student must apply the methods and get a unique result (no matter how much they work together behind the scenes).

The above is facilitated by modern technology. It is now easy to create a single document that creates an individual memorandum for each student. Thus, the marking burden is not raised, only the standards are raised.

Here is an example of doing an assignment where each student gets unique data and their own memo. For some recent formal assessments in this vein try these links: Animal movement, Extreme values.

I also use other types of assessments such as a quiz from a question bank that you are welcome to try out for yourself, an essay with a rubric, short presentations, oral discussions, etc.


Invited talks

I was invited to talk at the 2019 UFS Conference on Learning and Teaching. The theme was the blended learning environment. I presented my latest assessment practices to great interest from the audience.

I also presented on these modern technologies at the national statistics conference in 2019. I was specifically invited to present at a 2020 online conference.

I also present guest lectures for students and staff of other departments of the UFS once or twice a year. I even did an invited talk for Eunice Primary School once to great applause.

Peer assessment

I often invite peers to attend my classes. They tend to make the classes more fun and engaging. Here is some feedback I’ve received:

“Sean’s knowledge and experience in the field shines through in his classes and this inspires confidence and curiosity in his students. He presents advanced concepts in simple and practical forms, but he is always ready and open to explain the finer theoretical detail. His manner invites interaction and his classes are lively and fun.”

  • Zani Ludick

“Hi Sean, I just wanted to thank you for the exciting things I learned in Bayesian Analysis during the first semester. I was particularly happy about the practical way in which you introduced us to Stan 🙂 and writing a test / presenting something every week also kept me on my toes. The course finally unlocked the fun ways of Bayesian analysis for me and I am happy that I can now apply this to my own research. Thanks again!”

  • Trudie Strauss

“From the classes that I attended, I really enjoyed how every student in the class was able to give comments, ask questions, and participate in discussions. The type of questions/problems that you present in class made such participation possible. Somehow you managed to get the students to try out examples in class as you go through your examples.”

  • Moletsane Moletsane

Student assessment

When I first started teaching in 2006 I was terrible at it. I received horrid feedback that I cannot repeat. I realised that drastic change was needed, and change I did.

I started my journey into Scholarship of Teaching and Learning with a credit bearing short learning programme in Assessment of Learning in Higher Education, for which I achieved a distinction. This helped me realise what I was doing wrong and how to improve.

The effect on the student assessment of my teaching was a clear rise, and I’ve maintained high standards ever since:

Evaluation Graph

Latest module evaluation:

”… as this module comes to a close, I just want to say it’s rocked my world. The fact that you got us doing Stan and always pushing me to my limits has really has been inspiring. I feel like I have learned a lot and I can’t wait to take these practices into the real world. …” - Kheagan Eckley (Honours class 2020)

Other accolaids

I am the official faculty leader in the use of Blackboard tools. I make use of almost all the available tools across the courses I teach.


Applied Bayesian Analysis (2015 to 2020 onwards)

To create this course I read a handful of textbooks and then set about creating my own set of Bayes slides. Every year I update the slides, and yet there is still room for improvement. I hope to turn them into an open educational resource next year. These slides include exercises for students to work through, and many practical examples.

The goal of this course is to turn students into statisticians, equipping them with many tools for solving real problems, with Bayesian analysis being one of the tools added to their toolbox.


The course begins with an overview of simulation and R programming, although these are only indirectly assessed. The core of the course can be described as:

Students will be able to

  1. explain standard Bayesian concepts and apply them to problems
  2. derive, and simulate samples from, prior, posterior and predictive densities, for both simple and complex Bayesian hierarchical models
  3. calculate probabilities, parameter estimates and credibility intervals, for both simple and complex Bayesian hierarchical models
  4. test their results for internal consistency and perform appropriate inference based on those results

In spite of the standards of assessment being extremely high, and the course demands quite intense, the pass rate remains in the high 90s. To be more explicit, no student has ever completed the course and failed it. The only students that do not pass the course are those very few that give up along the way because their life does not give them the time they would have liked to devote to the course.

The latest moderator report, praising my efforts and adaptability, is available here. It is a balanced report in my opinion that does also show my shortcomings.

Short Research Essay (2010 to 2020 onwards)

I act as coordinator for honours research projects in my department, running the continuous assessment process. My focus is on maintaining fairness, avoiding plagiarism, implementing moderation, and ensuring skill acquisition. I also introduce the students to some research principles and tools.

Time Series (2007 to 2018)

Over the years I taught this third year course I gradually made a great deal of improvements, moving ever closer to my goal: That a student passing a Time Series course should be able to work with a time series (data that is measured regularly over time).

To this end I created a lot of my own resources, including a summary of the key principles: Univariate time series analysis for dummies

More interesting are the interactive learning tools like: Interactive page for visualising ARIMA time series and Interactive page for practicing time series graph interpretation.

I tried to make the examples interesting too, like: Prediction via simulated paths - FTSE example

Lastly, this course is where I pioneered my best assessment practices. I implemented electronic journalling, group work, and even implemented peer assessment on a major test, with excellent results.

For an external moderator’s perspective, see this external moderation report.

Other courses

Other university courses taught over the years include:

  • Introduction to Probability
  • Expected Values
  • Financial Management
  • Actuarial Modelling
  • Hypothesis testing
  • Time Series
  • Generalised Linear Models
  • Multivariate Analysis
  • Categorical Data Analysis
  • Basic Research Skills
  • VBA Programming

I have also substituted for lecturers in various other courses over the years.

Research supervision

Doing a full research masters is a new thing in my department and there is little incentive for a student to continue past honours degrees in my field as almost all competent students are offered good work by their fourth year.

Still, I am doing my part:

  • I have a recent graduate for whom I was sole supervisor.
  • I am currently PhD promotor for one student, and co-promotor for another.
  • I am primary supervisor for two masters students, and co-supervisor for another.
  • I supervise at least two honours students per year through their research projects, often much more.

In summary, I am partially responsible for at least seven postgraduate students’ research progress at any time.


I hope the above has given you a fair overview of my teaching. If you require any more information on the above please contact myself or my colleagues.

UFS staff will find me on Teams up to 60 hours a week, just search me out there.

Sean van der Merwe
Coordinator of UFS Statistical Consultation Unit