epfl-ml4ed Goto Github PK
Name: ML4ED
Type: Organization
Bio: EPFL's Machine Learning for Education (ML4ED) Laboratory led by Professor Tanja Käser.
Location: Switzerland
Name: ML4ED
Type: Organization
Bio: EPFL's Machine Learning for Education (ML4ED) Laboratory led by Professor Tanja Käser.
Location: Switzerland
Code for "Generalisable Methods for Early Prediction in Interactive Simulations for Education" published at EDM 2022.
Repository for the LAK 2023 paper: "Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling" by Jade Mai Cock, Muhammad Bilal, Richard Lee Davis, Mirko Marras and Tanja Käser
Bias analysis of german language models (BERT, T5, GPT2), with a case study on educational peer review data.
Comparing 5 different XAI techniques (LIME, PermSHAP, KernelSHAP, DiCE, CEM) through quantitative metrics. Published at EDM 2022.
Code for the paper "Evolutionary Clustering of Apprentices' Behavior in Online Learning Journals for Vocational Education" published at SFUVET 2022.
Code for the paper "Identifying and Comparing Multi-Dimensional Student Profiles across Flipped Classrooms" published at AIED 2022.
Success prediction in flipped classrooms via clickstreams. Published at EDM 2021.
Teaching and measuring inquiry skills in interactive simulations, published at AIED 2024.
Explainable clustering method used to extract interpretable student profiles. Featured at AIED 2024.
InterpretCC, an inherently interpretable neural network architecture
Student Modeling (Bayesian Knowledge Tracing) lecture for the JDPLS program.
Models to warm-start student pass/fail performance prediction trained using meta-transfer learning on 26 courses, 100k students, and millions of interactions. Published at L@S 2022.
Lab Materials for the EPFL Course on Machine Learning for Behavioral Data CS-421, Spring 2021.
Lab Materials for the EPFL Course on Machine Learning for Behavioral Data CS-421, Spring 2022.
Lab Materials for the EPFL Course on Machine Learning for Behavioral Data CS-421, Spring 2023.
Interpretability on raw time series with graph neural nets and concept activation vectors. Featured at AAAI 2023.
Prototype of the SubProject 03 for the SCESC Innosuisse Project
Validating post-hoc explainers with learning science experts (LAK 2023).
Downstream bias analysis (WEAT, SEAT, GenBit) for real-time educational writing support using LLMs. Published at EMNLP Findings 2023.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.