---
title: "Towards a New Generation of Cognitive Diagnosis"
authors:
  - "Qi Liu"
doi: "10.24963/ijcai.2021/703"
url: "https://ijcai.org/proceedings/2021/703"
pdf: "https://ijcai.org/proceedings/2021/0703.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4961-4964"
track: "Early Career"
keywords:
  - "Data Mining: Big Data, Large-Scale Systems"
  - "Humans and AI: Computer-Aided Education"
  - "Machine Learning Applications: Humanities"
  - "Multidisciplinary Topics and Applications: Social Sciences"
---

# Towards a New Generation of Cognitive Diagnosis

Qi Liu

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4961-4964. DOI: [10.24963/ijcai.2021/703](https://doi.org/10.24963/ijcai.2021/703)

## Abstract

Cognitive diagnosis is a type of assessment for automatically measuring individuals' proficiency profiles from their observed behaviors, e.g. quantifying the mastery level of examinees on specific knowledge concepts/skills. As one of the fundamental research tasks in domains like intelligent education, a number of Cognitive Diagnosis Models (CDMs) have been developed in the past decades. Though these solutions are usually well designed based on psychometric theories, they still suffer from the limited ability of the handcrafted diagnosis functions, especially when dealing with heterogeneous data. In this paper, I will share my personal understanding of cognitive diagnosis and review our recent developments of CDMs mostly from a machine learning perspective. Meanwhile, I will show the wide applications of cognitive diagnosis.