Date/Time : 07 Aug 2026 9:30 am to 12:00 pm
Venue : S17 #04-04 (Seminar Room 3)
Speaker : Various speakers,

The workshop will focus on concrete Mathematics teaching cases, especially AI-supported and blended-learning approaches. It will be discussion-led: we will use case studies and focused group discussions to explore particular teaching topics in a practical and efficient way. We warmly welcome colleagues to participate actively in the discussions.

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Date/Time : 30 Oct 2023 14:00
Venue : S17 #04-06 (SR1)
Speaker : David Chew, NUS, Statistics & Data Science

In this talk, I will describe the effort at the Department of Statistics and Data Science to offer more courses in the Blended Learning (BL) format.

Here are some aspects of the general strategy adopted by the department:
1. Replace one of the two weekly lecture meetings by pre-recorded videos that give high level overviews of topics covered.
2. Hold in-person lectures once a week; dive deeper into the materials by going through proofs, computational applications and/or in-depth examples.
3. In all our gateway classes, senior students are hired as student helpers (SHs) to provide extra support to students. SHs are each assigned 12 ~ 15 students, to answer queries, provide consultation/coding help during (and outside of) lecture and tutorial hours.

As for the tool used for making videos, the department recommends the use of Descript, an all-in-one audio and video editing software. It allows one to make videos using AI generated voices, so that videos are made with a “neutral” voice. This make the videos more “transferrable” — they can be (easily adapted and) used by another instructor, or in other courses.

We will also discuss other tools used by some colleagues (TeX, ImageMagick, etc) and other practical considerations when offering a course in BL format: logistics, manpower etc.

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Date/Time : 18 Oct 2023 14:00
Venue : S17 #04-06 (SR1)
Speaker : Mahsa Allahbakhshi, Pontificia Universidad Católica de Chile

This presentation explores the creation of a course package, designed for newcomers to the Bachelor’s degree in Mathematics and Statistics. The primary objective is to establish an environment that enhances the ability to self-assess strengths and weaknesses in one’s study journey, a key component of self-regulated learning. As students become familiar with the required quality standards, the initiative strives to maintain their motivation and resilience amidst challenges, thereby facilitating a smooth transition into university life. Moreover, this initiative aims to enhance communication and argumentation skills, while also increasing both individual and collective awareness of the cognitive, emotional, and social challenges involved in collaborative tasks.

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