Topic
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.
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TL;DR: A three layers model that explicitly connect the description of learners’ cognitive processes to LOs is presented that has been successfully implemented in an intelligent tutoring system for teaching Boolean reduction that provides highly tailored instruction thanks to the model.
Abstract: In the field of e-learning, a popular solution to make teaching material reusable is to represent it as learning object (LO). However, building better adaptive educational software also takes an explicit model of the learner’s cognitive process related to LOs. This paper presents a three layers model that explicitly connect the description of learners’ cognitive processes to LOs. The first layer describes the knowledge from a logical and ontological perspective. The second describes cognitive processes. The third builds LOs upon the two first layers. The proposed model has been successfully implemented in an intelligent tutoring system for teaching Boolean reduction that provides highly tailored instruction thanks to the model.
22 citations
Proceedings Article•
20 Jun 2008TL;DR: A computational model of student learning based on GMU BICA and its use as an ITS called Cognitive Constructor, which has two components called a Science Microworld and a Pedagogical Agent (GMU bICA agent) and results show that the system will be useful in elementary school education.
Abstract: Significant progress can be made in the part of elementary school education that relies on intelligent tutoring systems (ITS), if the role of a referee and a peer advisor will be performed by a pedagogical agent that is a computer implementation of a cognitive architecture modeling the process of learning Recent studies in cognitive architectures funded by the DARPA IPTO BICA Program have identified the key potential of feasible today artificial intelligence as bootstrapped cognitive growth (ie, gradual acquisition of knowledge and skills using previously acquired knowledge and skills), up to a human level of intelligence in a selected domain This approach is not limited to laboratory settings and short-term paradigms, it is intended for a long-term, open-ended learning scenario in real-world settings Several cognitive architectures were designed for this purpose, among which is GMU BICA, a self-aware biologically inspired cognitive architecture Here we describe a computational model of student learning based on GMU BICA and its use as an ITS called Cognitive Constructor, which has two components called a Science Microworld and a Pedagogical Agent (GMU BICA agent) Results of our analysis show that the system will be useful in elementary school education
22 citations
01 Dec 2013
TL;DR: This outline describes a strategy to address key ITS design challenges and expand the horizons of self-regulated learning methods to augment institutional training and inform and educate stakeholders, and focus potential collaborators on relevant issues within the adaptive tutoring research space.
Abstract: NOTICES Disclaimers The findings in this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. Citation of manufacturer's or trade names does not constitute an official endorsement or approval of the use thereof. Destroy this report when it is no longer needed. Do not return it to the originator. Approved for public release; distribution unlimited. Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Current Army standards for training and education are group instruction and classroom training also known as one-to-many instruction. Recently, the Army has placed significant emphasis on self-regulated learning (SRL) methods to augment institutional training. Per the Army Learning Model (ALM), Soldiers will be largely responsible for their own learning. One-to-one human tutoring has been shown to be significantly more effective than one-to-many instruction, but is not practical. An alternative to one-to-one human tutoring is one-to-one computer-based tutoring using Intelligent Tutoring Systems (ITSs), which have been shown to be effective in promoting individual learning in static, simple, well-defined domains (e.g., mathematics). To be practical, high authoring costs and limited adaptiveness barriers must be addressed. This outline describes a strategy to address key ITS design challenges and expand the horizons of SRL. Research is needed to: reduce cost/skill to author ITSs; enhance the adaptiveness of ITSs; and expand ITSs domains to support more dynamic, complex, and ill-defined domains to match the Army's operational mission. The interdependent nature of Army tasks also requires tutoring of squads and other teams. The intent of this report is to inform and educate stakeholders, and focus potential collaborators on relevant issues within the adaptive tutoring research space.
22 citations
01 Jan 2007
TL;DR: This paper computationally translates the teaching of cognitive skills into simple sets and represents the instructional process in terms of rules and algorithms operating on these sets.
Abstract: Current research efforts have shifted from the development of intelligent tutoring systems (ITSs) that focus on the teaching of "content" knowledge to those that focus on teaching cognitive skills. This shift is seen as necessary because cognitive skills are increasingly recognized by educational establishments as the foundation for knowledge acquisition, comprehension, and application. Knowledge construction is a cognitive skill and can be broadly divided into "top-down" and "bottom-up" approaches. The former splits a concept to form subordinate concepts, while the latter one groups concepts together to form a superordinate concept. Both approaches require the skill of classification and form different semantic networks or classification schemes with different levels of significance and suitability. List-making games operate within a bottom-up environment, where one has to arrange a list of items according to their respective categories. EpiList is developed along the line of a list-making game. It requires the student to suitably arrange items into categories that they have selected from a given list of categories. EpiList has employed both inductive and deductive teaching strategies to tutor the students and implicitly teach them the skills of generalization and comparison. This is achieved through the use of rules and algorithms. The rules and algorithms focus not only on the incorrect categorization of items but also on the migration of one classification scheme to another scheme that is more significant and suitable under the current teaching context. This paper computationally translates the teaching of cognitive skills into simple sets and represents the instructional process in terms of rules and algorithms operating on these sets. The field evaluations of EpiList demonstrated its capability to develop generic cognitive skills
22 citations
25 May 2007
TL;DR: Using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents and the approach, the architecture, and the agent characteristics for such system are described.
Abstract: Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student’s learning behavior and presents to the student the learning material (content, questions-answers, assignments) accordingly. In today’s distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents. The paper describes the approach, the architecture, and the agent characteristics for such system. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students’ learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and student model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system. Keywords—CBR, ITS, Student Modeling, Distributed System, Intelligent Agent.
22 citations