Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

Supporting Distributed Problem-Based Learning: The Use of Feedback Mechanisms in Outline Learning

Abstract: In this chapter we discuss possibilities and shortcomings of Internet usage for distributed problem-based learning. Several problems with the use of computer-mediated communication for collaborative learning online are identified. In our approaches we use data that is automatically tracked during computer-mediated communication and extract relevant information for feedback purposes. Partly automatically, partly manually prepared the feedback is a rich resource for learners to manage their own collaboration 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB9544 IDEA GROUP PUBLISHING This chapter appears in the book, Online Collaborative Learning: Theory and Practice, edited by Tim S. Roberts. Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Supporting Distributed Problem-Based Learning 87 Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. process as well as subsequent problem-solving processes. In a synchronous and an asynchronous distributed problem-based learning environment, we show how we applied this methodology to support learners’ motivation and problem solving. Analyses show encouraging benefits of our approach in overcoming common problems with computer-mediated communication. INTRODUCTION When James Cook started his last journey to find the Northwest Passage through North America, his wife was angry with him because he had promised her that he would never go on a long voyage again. During the whole trip he was supposed to be in an ill-tempered mood totally different from his normal style, badly collaborating with his crew and behaving harshly and unfairly to the native people he met. No wonder that he was killed on the islands of Hawaii in 1779. What he did not know was that his wife had already forgiven him, so some might say that if he had seen her smile, this would have changed the whole course of history. Is this true? Does such a form of emotional feedback have an impact on people’s performance in a group situation? Did Cook die due to a lack of feedback? Nowadays, most of the white spots on Earth have been explored and Internet technologies have made the world smaller. People communicate, collaborate and even learn together using the Internet. There is much ongoing research about how to use computer-mediated communication (CMC) for task oriented groups. Actually, little research is dedicated to the use of technology for feedback purposes during online collaboration, especially in distributed problembased learning. There are also many studies exploring feedback mechanisms in individual computer-based learning, especially for knowledge acquisition purposes. Research concerning intelligent tutoring systems (ITS) has provided evidence for a meaningful use of individual feedback based on learner-program interaction (Wenger, 1987). Unfortunately, this tradition has yet not reached contemporary learning approaches using computer-supported collaborative learning (CSCL). Besides the use of computer generated feedback on a task level, there is hardly any exploration of its effects on a group’s interaction level. Although interacting and communicating is crucial to problem-based learning (PBL), most approaches transferring PBL into a network-based learning environment do not pursue approaches to give learner support on this level. Some earlier research, for example Mandl, Fischer, Frey and Jeuck (1985), discusses some computer-based feedback mechanisms and functions, but does not specifically refer to a group context. So far, these investigations have not been carried further. Possible reasons might be a lack of underlying theoretical assumptions and derivations of specific hypotheses. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/supporting-distributed-problembased-learning/27718

Note-taking within MetaTutor: interactions between an intelligent tutoring system and prior knowledge on note-taking and learning

Abstract: Hypermedia learning environments (HLE) unevenly present new challenges and opportunities to learning processes and outcomes depending on learner characteristics and instructional supports. In this experimental study, we examined how one such HLE—MetaTutor, an intelligent, multi-agent tutoring system designed to scaffold cognitive and metacognitive self-regulated learning (SRL) processes—interacts with learner’s prior domain knowledge to affect their note-taking activities and subsequent learning outcomes. Sixty (N = 60) college students studied with MetaTutor for 120 min and took notes on hypermedia content of the human circulatory system. Log-files and screen recordings of learner-system interactions were used to analyze notes for several quantitative and qualitative variables. Results show that most note-taking was a verbatim copy of instructional content, which negatively related to the post-test measure of learning. There was an interaction between prior knowledge and pedagogical agent scaffolding, such that low prior knowledge students took a greater quantity of notes compared to their high prior knowledge counterparts, but only in the absence of MetaTutor SRL scaffolding; when agent SRL scaffolding was present, the note-taking activities of low prior knowledge students were statistically equivalent to the number of notes taken by their high prior knowledge counterparts. Theoretical and instructional design implications are discussed.

TECH8 intelligent and adaptive e-learning system

Abstract: E-materials and various e-learning systems have become regular features in lower secondary schools in Slovenia and around the world. Many different systems and materials have been created for students, but only a few offer the same amount of individualisation that is present in traditional one to one teaching (one teacher to one student). The purpose of this research is to demonstrate the design and evaluation of an adaptive, intelligent and, most important, an individualised intelligent tutoring system (ITS) based on the cognitive characteristics of the individual learner. The TECH8 model presented is designed modularly, based on a system for collecting a range of metadata and variables that are vital for the teaching process. Prepared in such a way, the proposed system supports individualization and differentiation; because of this, it can be adapted to each individual's level of knowledge and understanding of the subject matter.This TECH8 system was evaluated in a real learning environment. The evaluation sample of the study consists of 117 students from five schools (suburban and urban). Qualitative and quantitative data was gathered with a system for collecting metadata and variables. The assembled data was analysed and statistically processed using descriptive analysis. This data was also compared to data from national assessments of knowledge, which encompassed the entire student population (approx. 5000) in the years 2008, 2010 and 2013. The study and the comparisons indicated that appropriately created TECH8 e-learning material, yields results that are better than those from traditional teaching but not better than one to one teaching. With the help of the collected metadata, optimisation, evaluation and an upgrade of the TECH8 itself will be carried out. In addition, such individualized e-learning systems can reinforce knowledge gained through traditional classroom education. Research question: Is it possible to replace a human teacher with a virtual one?The presented modified version of ITS includes hybrid model TECH8.TECH8 can adapt the learning process to the needs of an individual student.TECH8 does not only symbolise the learning process, but also the social environment.Cybernetic pedagogy and presented model TECH8 can lead to the progress of ITS.

Utterance classification in AutoTutor

Abstract: This paper describes classification of typed student utterances within AutoTutor, an intelligent tutoring system. Utterances are classified to one of 18 categories, including 16 question categories. The classifier presented uses part of speech tagging, cascaded finite state transducers, and simple disambiguation rules. Shallow NLP is well suited to the task: session log file analysis reveals significant classification of eleven question categories, frozen expressions, and assertions.

Children's Interactions with Inspectable and Negotiated Learner Models

Abstract: The Learner Model of an Intelligent Tutoring System (ITS) may be made visible (opened) to its users. An Open Learner Model (OLM) may also become a learning resource in its own right, independently of an ITS. OLMs offer potential for learner reflection and support to metacognitive skills such as self-assessment, in addition to improving learner model accuracy. This paper describes an evaluation of an inspectable and a negotiated OLM (one that can be jointly maintained through student-system discussion) in terms of facilitating self-assessment accuracy and modification of model contents. Both inspectable and negotiated models offered significant support to users in increasing the accuracy of self-assessments, and reducing the number and magnitude of discrepancies between system and user beliefs about the user's knowledge. Negotiation of the model demonstrated further significant improvements.