Teaching

Most of what makes someone good at research is invisible. The reasoning behind a choice of method, the moment you notice your data isn’t going to support the claim you wanted — students may not see any of it unless you make a point of showing them. My teaching is largely an attempt to work in the open — real decisions, real data, including the ones I get wrong. That’s also why I assess the way I do. You can’t coach reasoning you can’t see, so I ask for the process as well as the product.

Courses at Monash

2024–2026

Research Methods in Data Science (ADS4001) — Lecturer Honours undergraduate, in-person. Three offerings of 3–12 students

2024–2025

IT Professional Practice (FIT1049) — Chief Examiner & Lecturer (2024), Co-Lecturer (2025) Undergraduate, in-person. 689 students in 2024; 828 in 2025

2025

Statistical Data Modelling (FIT5197, ITO5197) — Chief Examiner & Lecturer Master’s. Australian campus, in-person (253 students); Chief Examiner, Malaysia campus (76); Chief Examiner, Monash Online (33)

2024

Modelling for Data Analysis (FIT2086) — Co-Lecturer Undergraduate, in-person (344 students)

2022–2024

Educational Theories and Processes in Learning Analytics (ITO5005) — Unit designer; Lecturer (2022), Chief Examiner (2023, 2024) Master’s, fully online and asynchronous. Designed from scratch

2023

Introduction to Data Science (FIT5145) — Co-Lecturer (Semester 2), Lecturer (Summer B) Master’s, in-person. 229 and 94 students

2023

Data Challenges 4 (ADS2002) — Lecturer Undergraduate, in-person (37 students)

2021–2022

Data Challenges 1 (ADS1001) — Lecturer Undergraduate. Hybrid in 2021 (48 students), in-person in 2022 (55)

At Monash a unit is a single course. The Chief Examiner is responsible for its content, assessment and delivery, roughly a US course director; Lecturers deliver the lectures and workshops. Student numbers are enrolments per offering.

Courses at UW–Madison

2019

Research Experience in Educational Psychology — Guest Lecturer

2018–2019

Current Topics in the Learning Sciences — Guest Lecturer

2016–2018

Human Abilities and Learning — Instructor (2018), Teaching Assistant (2016, 2017)

Supervision

Doctoral students — main supervisor

Exp. 2026

Zheng Fang Computational collaboration analytics · Faculty of Information Technology, Monash University

Exp. 2027

Yixin Cheng Identifying key skills for writing with generative artificial intelligence · Faculty of Information Technology, Monash University

Doctoral students — associate supervisor

Current

Luna Lu, Keyang Qian, Steven Walker Monash University

Graduated

Riordan Alfredo, Jae Han, Lixiang Yan Monash University

Master’s and undergraduate students

2025

Robert Milligan, Master’s thesis Extending a model for simulating collaborative discourse

Current

Kaii Leong, Sandira Polketiyage Undergraduate research projects

Monash doctoral candidates are research-only, with no required coursework, and are supervised by a committee of a main supervisor, who directs the project — the equivalent of a US advisor — and associate supervisors.

Methods workshops

I run methods workshops for doctoral and early-career researchers, most regularly Advanced ENA and rENA, which has been on the programme at every Quantitative Ethnography conference since the first one in 2019. Others include:

• A workshop on writing in quantitative ethnography — ICQE26, Hiroshima (with D. W. Shaffer and A. R. Ruis)
• Integrating Quantitative Ethnography Methods to Support Learning Analytics in the Age of Artificial Intelligence — LAK24, Kyoto
• Techniques for Investigating Collaboration with Multimodal Approaches — ISLS Annual Meeting 2023, Montreal
• Creating, refining, and validating automated discourse codes: An introduction to nCoder and rho — CSCL 2019, Lyon; earlier versions at LSGS and ICLS

I have also taught a Quantitative Ethnography Masterclass Series — six invited lectures across National Yang Ming Chiao Tung University and National Central University (Taiwan), Seoul National University (South Korea), and Osaka and Senshu Universities (Japan).