Intelligent tutoring system

An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher (e.g., classroom lectures), or no teacher at all (e.g., online homework). ITSs are often designed with the go

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (28)

Related publications

Related people (2)

Related people

Related units (4)

Related units

Related concepts (2)

Related concepts

Related courses (2)

Related courses

Related lectures (5)

Related lectures

CoPainter: Interactive AI drawing experience

Pawel Golinski

Google AI Experiments [3] is a set of simple experiments that showcase innova- tive uses of AI in UX. As part of this collection, Google shared Quick Draw, where user draws simple sketches and intelligent agent tries to guess what it depicts, and Sketch- RNN interactive demo, an experience where user starts a sketch and recurrent neural network model completes it [4]. These experiences provide fun and engaging drawing activities which have potential to be used in more educational context. This report describes CoPainter - an interactive AI drawing experience which aims to provide an educational activity based on learning by teaching paradigm - the application asks the user to draw a painting of some object in cooperation with an intel- ligent agent (which can either be a robot or an abstract entity). The activity is based on Sketch-RNN model mentioned above. Main contributions of this project are porting the Sketch-RNN web app experience to Qt, which enables integration with CoWriter code- base and makes it possible to collect more data on user activities. An additional draw in turns game mode is also introduced. The activity may possibly be extended to enable it to be used in diagnosing and treating dysgraphia, which is briefly mentioned in this re- port and is aim of further research on this project.

2019

Beyond Knowledge Tracing. Modeling Skill Topologies with Bayesian Networks

Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing (BKT). BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies. To ensure model interpretability, we constrain the parameter space. We evaluate the performance of our models on five large-scale data sets of different learning domains such as mathematics, spelling learning and physics, and demonstrate that our approach outperforms BKT in prediction accuracy on unseen data across all learning domains.

Springer2014

Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clustering pipeline that can be applied to learning data, aiming at improving cluster stability over multiple training sessions in the presence of noise. Our model selection is designed such that relevant cluster evolution effects can be captured. The pipeline can be used as a black box for any intelligent tutoring system (ITS). We show that our method outperforms previous work regarding clustering performance and stability on synthetic data. Using log data from two ITS, we demonstrate that the proposed pipeline is able to detect interesting student behavior and properties of learning environments.

EDM2016

CS-411: Digital education

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies on this topic: do student actually learn due to technologies?

CS-421: Machine learning for behavioral data

Computer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will cover the core methods of user modeling and personalization, with a focus on educational data.

Educational psychology

Educational psychology is the branch of psychology concerned with the scientific study of human learning. The study of learning processes, from both cognitive and behavioral perspectives, allows resea

Educational technology

Educational technology (commonly abbreviated as edutech, or edtech) is the combined use of computer hardware, software, and educational theory and practice to facilitate learning. When referred to wit