Computational Aspects of the Intelligent Tutoring System MetaTutor

2012 ◽  
pp. 247-260 ◽  
Author(s):  
Mihai Lintean ◽  
Vasile Rus ◽  
Zhiqiang Cai ◽  
Amy Witherspoon-Johnson ◽  
Arthur C. Graesser ◽  
...  
Keyword(s):  
Natural Language ◽  
Prior Knowledge ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Metacognitive Strategy ◽  
Tutoring System ◽  
Knowledge Activation ◽  
Prior Knowledge Activation ◽  
Goal Assessment ◽  
Dialogue Mechanism

We present in this chapter the architecture of the intelligent tutoring system MetaTutor that trains students to use metacognitive strategies while learning about complex science topics. The emphasis of this chapter is on the natural language components. In particular, we present in detail the natural language input assessment component used to detect students’ mental models during prior knowledge activation, a metacognitive strategy, and the micro-dialogue component used during sub-goal generation, another metacognitive strategy in MetaTutor. Sub-goal generation involves sub-goal assessment and feedback provided by the system. For mental model detection from prior knowledge activation paragraphs, we have experimented with three benchmark methods and six machine learning algorithms. Bayes Nets, in combination with a word-weighting method, provided the best accuracy (76.31%) and best human-computer agreement scores (kappa=0.63). For sub-goal assessment and feedback, a taxonomy-driven micro-dialogue mechanism yields very good to excellent human-computer agreement scores for sub-goal assessment (average kappa=0.77).

Evidence of an Intelligent Tutoring System as a Mindtool to Promote Strategic Memory of Expository Texts and Comprehension With Children in Grades 4 and 5

2017 ◽  
Vol 55 (7) ◽  
pp. 1022-1048 ◽  
Author(s):  
Kausalai (Kay) Wijekumar ◽  
Bonnie J. F. Meyer ◽  
Puiwa Lei ◽  
Weiyi Cheng ◽  
Xuejun Ji ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Prior Knowledge ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Text Structure ◽  
Tutoring System ◽  
Randomized Controlled Studies ◽  
Strategic Memory ◽  
Memory Structures

Reading and comprehending content area texts require learners to effectively select and encode with hierarchically strategic memory structures in order to combine new information with prior knowledge. Unfortunately, evidence from state and national tests shows that children fail to successfully navigate the reading comprehension challenges they face. Schools have struggled to find approaches that can help children succeed in this important task. Typical instruction in classrooms across the country has focused on procedural application of strategies or content-focused approaches that encourage rich discussions. Both approaches have achieved success but have limitations-related transparency and specificity of scaffolds and guidance for the teacher and learner in today’s diverse and complex classroom settings. The text structure strategy combines content and strategy to provide pragmatic, transparent, and scaffolded instruction addressing these challenges. A web-based intelligent tutoring system for the text structure strategy, named ITSS, was designed and developed to provide consistent and high-quality instruction to learners in Grades 4 and 5 about how to read, select main ideas, encode strategic memory structures, make inferences, and monitor comprehension during reading. In this article, we synthesize results from two recent large-scale randomized controlled studies to showcase how the ITSS supports selection and encoding of students’ strategic memory structures and how prior knowledge affects the memory structures. We provide greater depth of information about such processing than examined and reported in extant literature about overall increases in reading comprehension resulting from students using ITSS.

Overlay Method in Testing Knowledge by Natural Language Dialogue

2011 ◽  
pp. 187-207
Author(s):  
Branko Žitko
Keyword(s):  
Natural Language ◽  
Domain Knowledge ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Natural Language Generation ◽  
Problem Solution ◽  
Tutoring System ◽  
Teaching Process ◽  
Language Generation ◽  
Learning And Teaching

Irrespective how domain knowledge is well formalized, and irrespective of applying appropriate pedagogical techniques in Intelligent tutoring system, learning and teaching process can seem complex because of unfamiliar communication with computer tutor. Using even simplest NLP techniques makes benefits in computer‘s usage, not only in e-learning domain. Natural language generation is basic technique which can make learning and teaching process much more adaptable to the student. Presenting formal knowledge by natural language sentences, as well as testing in a form of dialogue are aims to be accomplished in new Intelligent Tutoring System based on Tutor-Expert System model. In making previous system more acceptable to the student, our first step is to perform natural language generation with Croatian localization for formalized domain knowledge following by problem generation and dialog based guidance to the problem solution.

An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning

2006 ◽  
Vol 38 (1) ◽  
pp. 25-46 ◽  
Author(s):  
Chong Woo Woo ◽  
Martha W. Evens ◽  
Reva Freedman ◽  
Michael Glass ◽  
Leem Seop Shim ◽  
...  
Keyword(s):  
Natural Language ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Natural Language Dialogue ◽  
Multi Level

Developing an Intelligent Tutoring System that has Automatically Generated Hints and Summarization for Algebra and Geometry

2015 ◽  
Vol 11 (2) ◽  
pp. 14-31 ◽  
Author(s):  
Yatao Li ◽  
Ke Zhao ◽  
Wei Xu
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
After School ◽  
Tutoring System ◽  
Learning Gain ◽  
Web Based ◽  
Tutoring Systems ◽  
Natural Language Interface

Intelligent tutoring systems (ITSs), which provide step-by-step guidance to students in problem-solving activities, have been shown to enhance student learning in a range of domains. However, they tend to be preestablished and cannot supply the tutoring function immediately from the diverse mathematical questions. The MITSAS (multiagent intelligent tutoring system after school) is a web-based ITS in algebra and geometry with a natural language interface which is designed to extract the hint and summarization from the detailed solving answer automatically. In this paper, its Design principles and functionality is analysed firstly. Then, the framework including the natural language understanding agent, automatic modelling agent and automatic problem-solving agent are discussed in the following in order to support the real-time problems solution. Next, the methods for automatically extracting tutoring function such as hint and summarization is given based on the difficulty of knowledge components and the type of problem acquired from the detailed answer. Finally, the effectiveness of MITSAS at improving the Chinese Students' learning gain is shown by an experiment conducted in junior school.

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