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: The dialog patterns from natural tutoring interactions with AutoTutor are explored and how short pedagogical feedback is related to learning is analyzed to support the conclusion that autoTutor is an effective intelligent tutoring system that uses pedagogy strategies that are appropriate for individual learners.
Abstract: At the University of Memphis we have created an intelligent tutoring system, called AutoTutor, that helps students learn by holding a conversation in natural language. Decades of research on human tutoring have guided our creation of AutoTutor, which implements effective tutoring strategies. Several studies have shown that AutoTutor promotes significant learning gains. The current research examines which features of the dialog can account for the learning gains, and assesses AutoTutor?s appropriate use of dialog. Specifically, we explored the dialog patterns from natural tutoring interactions with AutoTutor and analyzed how short pedagogical feedback is related to learning. We found that AutoTutor creates an appropriate model of student knowledge and responds to the students in a manner consistent with their overall performance. These results together with previous findings support the conclusion that AutoTutor is an effective intelligent tutoring system that uses pedagogical strategies that are appropriate for individual learners.
20 citations
01 Jan 2014
TL;DR: The aim of this paper is to investigate the most recent state of art in the development of the tutor model and student model of the intelligent tutoring systems.
Abstract: The intelligent tutoring system (ITS) is an educational software system that provides personalized and adaptive tutoring to students based on their needs, profiles and preferences. The tutor model and student model are two dependent components of any ITS system. The goal of any ITS system is to help the students to achieve maximum learning gain and improve their engagements to the systems by capturing the student's interests through the system's adaptive behavior. In other words an ITS system is always developed with the aim of providing an immediate and efficient solution to student's learning problems. In recent years a lot of work has been devoted to improving student and tutor models in order enhance the teaching and learning activities within the ITS systems. The aim of this paper is to investigate the most recent state of art in the development of these two vital components of the intelligent tutoring systems.
20 citations
01 Dec 2007
TL;DR: In this paper, Shikshak, an ITSAT developed by us is presented and its deployment in the district of Paschim Medinipur, West Bengal is discussed along with its sample effect on primary education.
Abstract: Low literacy scenario in India and other developing nation demands an alternative learning environment to deal with the problem. Lack of trained teachers, high dropout rates are some of the major problems that need to be addressed. Intelligent Tutoring System (ITS) or ITS Authoring tools (ITSAT) can be thought of as a possible solution to these problems. In this paper we present Shikshak, an ITSAT developed by us and discuss its deployment in the district of Paschim Medinipur, West Bengal along with its sample effect on primary education.
20 citations
31 Oct 2016
TL;DR: The feasibility and potential of using the PPG signals implicitly recorded by the built-in camera of smartphones to facilitate mobile MOOC learning are demonstrated.
Abstract: We present Context and Cognitive State triggered Feed-Forward (C2F2), an intelligent tutoring system and algorithm, to improve both student engagement and learning efficacy in mobile Massive Open Online Courses (MOOCs). C2F2 infers and responds to learners' boredom and disengagement events in real time via a combination of camera-based photoplethysmography (PPG) sensing and learning topic importance monitoring. It proactively reminds a learner of upcoming important content (feed-forward interventions) when disengagement is detected. C2F2 runs on unmodified smartphones and is compatible with courses offered by major MOOC providers. In a 48-participant user study, we found that C2F2 on average improved learning gains by 20.2% when compared with a baseline system without the feed-forward intervention. C2F2 was especially effective for the bottom performers and improved their learning gains by 41.6%. This study demonstrates the feasibility and potential of using the PPG signals implicitly recorded by the built-in camera of smartphones to facilitate mobile MOOC learning.
20 citations
20 Jul 2009
TL;DR: A machine learning model that can automatically identify off-task behaviors of students while using an intelligent tutoring system is proposed and a robust Ridge Regression algorithm is designed to estimate model parameters.
Abstract: The paper proposes a machine learning model that can automatically identify off-task behaviors of students while using an intelligent tutoring system. Only log files that record students' actions with the system are used for the development of the model. The model utilizes a set of time features, performance features and mouse movement features and is compared to i) a model that only utilizes time features, ii) a model that uses time and performance features. In order to address data sparseness problem, a robust Ridge Regression algorithm is designed to estimate model parameters. An extensive set of experiment results demonstrate the power of using multiple types of evidence as well as the robust Ridge Regression algorithm.
20 citations