Titre :

Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

Auteur(s) :

Minn, Sein [Auteur]
Scool [Scool]
Vie, Jill-JÊnn [Auteur]
Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données [SODA]
Takeuchi, Koh [Auteur]
Kyoto University
Kashima, Hisashi [Auteur]
Kyoto University
Zhu, Feida [Auteur]
Singapore Management University [SIS]

Titre de la manifestation scientifique :

Proceedings of the AAAI Conference on Artificial Intelligence

Ville :

Vancouver

Pays :

Canada

Date de début de la manifestation scientifique :

2022-02-22

Date de publication :

2021-12-15

Mot(s)-clé(s) en anglais :

Student Model
Bayesian Knowledge Tracing
Causal Relations
Hidden Markov Model
Tree-Augmented Naive Bayes

Discipline(s) HAL :

Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting ...
Lire la suite >Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasoning are more critical in learning sciences. Since KT problem has few observable features (problem ID and student's correctness at each practice), we extract meaningful latent features from students' response data by using machine learning and data mining techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful latent features: individual skill mastery, ability profile (learning transfer across skills) and problem difficulty. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models. IKT also shows better student performance prediction than deep learning-based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL