NLP Techniques in Intelligent Tutoring Systems

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.

scholarly journals Recent Advances in Conversational Intelligent Tutoring Systems

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 42-54 ◽  
Author(s):  
Vasile Rus ◽  
Sidney D’Mello ◽  
Xiangen Hu ◽  
Arthur Graesser
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Individual Student ◽  
Learning Progressions ◽  
Tutoring Systems ◽  
Recent Advances ◽  
Processing Techniques

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

Principles of Natural Language Processing and Adaptive Courseware in E-Assessments

2019 ◽  
pp. 335-366
Author(s):  
Ani Grubišić ◽  
Slavomir Stankov ◽  
Branko Žitko ◽  
Suzana Tomaš ◽  
Emil Brajković ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Knowledge Domains ◽  
Education Levels ◽  
Supported Education

Over the last few decades, researchers put efforts to improve intelligent tutoring systems' abilities with the aim to get them as close as possible to the ultimate goal of one-to-one tutoring. CoLaB Tutor and AC-ware Tutor are intelligent tutoring systems based on conceptual knowledge learning and are notable due to the fact they are relatively easy to generalize to multiple knowledge domains. CoLaB Tutor's forte lies in teacher-learner communication in controlled natural language, while AC-ware Tutor focuses on the automatic and dynamic generation of adaptive courseware. In order to compare various intelligent tutoring system supported education environments, in this chapter, the authors summarize several empirical evaluations of CoLaB Tutor and AC-ware Tutor. The results of intelligent tutoring systems' effectiveness in these environments offer the possibility to observe the specific intelligent tutoring system across various education levels, as well as to compare the intelligent tutoring systems' supported education environments.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals Towards a New Generation of Intelligent Tutoring Systems

2019 ◽  
Vol 14 (14) ◽  
pp. 105
Author(s):  
Mohammed Beyyoudh ◽  
Mohammed Khalidi Idrissi ◽  
Samir Bennani
Keyword(s):  
Intelligent Tutoring Systems ◽  
Serious Games ◽  
Intelligent Tutoring ◽  
Evaluation Process ◽  
Learning Context ◽  
Tutoring Systems ◽  
New Generation ◽  
Adaptive Workflows ◽  
Intelligent Tutor

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.

Intelligent tutoring systems for aviation automation

2000 ◽  
Author(s):  
Christine Mitchel ◽  
Alan Chappell ◽  
W. Gray ◽  
Alex Quinn ◽  
David Thurman
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems
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