Hong Kong Machine Learning Season 4 Episode 5

 26.01.2022 -  Hong Kong Machine Learning -  ~2 Minutes

When?

• Wednesday, January 26, 2022 from 7:00 PM to 9:00 PM (Hong Kong Time)

Where?

• This meetup was hosted on zoom.

The page of the event on Meetup: HKML S4E5

Programme:

Talk 1: Information geometry & adaptive assessment

[TL;DR] Why do we keep bothering students with long questionnaires while we could adaptively assess their knowledge, and track it over time? We will present why the research field of educational data mining is old, hard, beautiful and important.

In this current era where digital access to knowledge is cheap and user attention is expensive, a number of online applications have been developed for learning. These platforms collect a massive amount of data over various profiles, that can be used to improve learning experience: intelligent tutoring systems can infer what activities worked for different types of students in the past, and apply this knowledge to instruct new students. In order to learn effectively and efficiently, the experience should be adaptive: the sequence of activities should be tailored to the abilities and needs of each learner, in order to keep them stimulated and avoid boredom, confusion and dropout.

Speaker: Jill-Jênn Vie

Jill-Jênn Vie is designing machine learning models to predict student performance and optimize human learning. More broadly, he is interested in interactive systems that can help people learn a language, digital skills or culture.

Algorithmic problem solving is his passion, and he wants to teach it massively to kids. Unfortunately, this job does not exist (yet), so he (and colleagues) have to create a curriculum, educate teachers, etc. In the meantime, he has published two books on this topic.

Note that Jill-Jênn Vie presented Mangaki https://mangaki.fr, a recommender system for mangas and anime, at the Hong Kong Machine Learning Meetup Season 1 Episode 1!

Some side reading related to Jill-Jênn’s talk:

• Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
• Knowledge Tracing Machines: Families of models for predicting student performance
• Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
• A Review of Recent Advances in Adaptive Assessment

Video Recording of the HKML Meetup on YouTube

• YouTube videos:

HKML S4E5 - Information geometry & adaptive assessment