Towards a New Generation of Intelligent Tutoring Systems

In this paper, a new approach of intelligent tutoring systems based on adaptive workflows and serious games is proposed. The objective is to use workflows for learning and evaluation process in the activity-based learning context. We aim to implement a system that allow the coexistence of an intelligent tutor and a human tutor who could control and follow-up the execution of the learning processes and intervene in blocking situations. Serious games will be the pillar of the evalu-ation process. The purpose is to provide new summative evaluation methods that increase learner’s motivation and encourage them to learn.

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Intelligent Production of Serious Games in Intelligent Tutoring Systems Based on Adaptive Workflows

Advances in Intelligent Systems and Computing - Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) ◽

10.1007/978-3-030-36653-7_16 ◽

2020 ◽

pp. 157-167

Author(s):

Mohammed Beyyoudh ◽

Mohammed Khalidi Idrissi ◽

Samir Bennani

Keyword(s):

Intelligent Tutoring Systems ◽

Serious Games ◽

Intelligent Tutoring ◽

Tutoring Systems ◽

Adaptive Workflows

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A tutorial on machine learning for  interactive pedagogical systems

International Journal of Serious Games ◽

10.17083/ijsg.v5i3.256 ◽

2018 ◽

Vol 5 (3) ◽

pp. 79-112

Author(s):

Francisco S Melo ◽

Samuel Mascarenhas ◽

Ana Paiva

Keyword(s):

Machine Learning ◽

Intelligent Tutoring Systems ◽

Serious Games ◽

Intelligent Tutoring ◽

Short Introduction ◽

Tutoring Systems ◽

Key Concepts

This paper provides a short introduction to the field of machine learning for interactive pedagogical systems. Departing from different examples encountered in interactive pedagogical systems—such as intelligent tutoring systems or serious games—we go over several representative families of methods in machine learning, introducing key concepts in this field. We discuss common challenges in machine learning and how current methods address such challenges. Conversely, by anchoring our presentation on actual interactive pedagogical systems, highlight how machine learning can benefit the development of such systems.

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DESIGN OF THE PEDAGOGICAL MODULE FOR AN EMOTIONALLY INTELLIGENT TUTORING SYSTEM / PEDAGOGINIO MODULIO EMOCIJAS SUPRANTANČIAI MOKYMO SISTEMAI KŪRIMAS

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2014.18 ◽

2014 ◽

Vol 6 (2) ◽

pp. 138-146 ◽

Cited By ~ 3

Author(s):

Sintija Petrovica

Keyword(s):

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Point Of View ◽

Tutoring System ◽

Tutoring Systems ◽

Emotional Component ◽

Human Teachers ◽

New Generation ◽

Emotionally Intelligent

Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s knowledge state butalso to his/her emotional state has been disregarded. The paper presents study on how to use the determined student’s emotionalstate further in order to change behaviour of the intelligent tutoring system looking from the pedagogical point of view and toimplement this as a part of the pedagogical module. The architecture of the planned tutoring system that adapts the tutoring bothto student’s emotions and knowledge is also described in the paper. Straipsnyje nagrinėjami klausimai, susiję su informacijos apienustatytą studento emocinę būklę taikymu sumaniosios mokymosistemos elgsenai keisti, taip pat emocinės būklės poveikis mokymoprocesui pedagoginiu požiūriu. Siūlomas pedagoginiamsaspektams įgyvendinti specializuotas informacinės sistemosmodulis. Parodoma pedagoginio modulio vieta sumaniosiosmokymo sistemos, pritaikančios mokymo procesą konkretausstudento žinių ir emociniam lygmenims, architektūroje.

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NLP Techniques in Intelligent Tutoring Systems

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch183 ◽

2011 ◽

pp. 1253-1258

Author(s):

Chutima Boonthum-Denecke ◽

Irwin B. Levinstein ◽

Danielle S. McNamara ◽

Joseph P. Magliano ◽

Keith K. Millis

Keyword(s):

Natural Language ◽

Language Processing ◽

Intelligent Tutoring Systems ◽

Semantic Analysis ◽

Assessment Tool ◽

Intelligent Tutoring ◽

Assessment Tools ◽

Tutoring Systems ◽

Active Reading ◽

Intelligent Tutor

Many Intelligent Tutoring Systems (ITSs) aim to help students become better readers. The computational challenges involved are (1) to assess the students’ natural language inputs and (2) to provide appropriate feedback and guide students through the ITS curriculum. To overcome both challenges, the following non-structural Natural Language Processing (NLP) techniques have been explored and the first two are already in use: word-matching (WM), latent semantic analysis (LSA, Landauer, Foltz, & Laham, 1998), and topic models (TM, Steyvers & Griffiths, 2007). This article describes these NLP techniques, the iSTART (Strategy Trainer for Active Reading and Thinking, McNamara, Levinstein, & Boonthum, 2004) intelligent tutor and the related Reading Strategies Assessment Tool (R-SAT, Magliano et al., 2006), and how these NLP techniques can be used in assessing students’ input in iSTART and R-SAT. This article also discusses other related NLP techniques which are used in other applications and may be of use in the assessment tools or intelligent tutoring systems.

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