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.
Papers published on a yearly basis
Papers
More filters
•
01 Jan 2008TL;DR: A data mining and visualization tool for the discovery of student trails in web-based educational systems is presented and described, allowing non-expert users, such as course instructors, to interpret its output.
Abstract: A data mining and visualization tool for the discovery of student trails in web-based educational systems is presented and described. The tool uses graphs to visualize results, allowing non-expert users, such as course instructors, to interpret its output. Several experiments have been conducted, using real data collected from a web-based intelligent tutoring system. The results of the data
mining algorithm can be adjusted by tuning its parameters or filtering to concentrate on specific trails, or to focus only on the most significant paths.
49 citations
••
TL;DR: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not, for teaching and research institutions in France or abroad, or from public or private research centers.
Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Mascaret: Pedagogical multi-agents system for virtual environment for training. Cédric Buche, Ronan Querrec, Pierre De Loor, Pierre Chevaillier
49 citations
•
01 Dec 2009TL;DR: A formative evaluation of LARGO (Legal ARgument Graph Observer), a system that enables law students graphically to represent examples of legal interpretation with hypotheticals they observe while reading texts of U.S. Supreme Court oral arguments, and how it helped lower-aptitude students learn argumentation skills.
Abstract: Argumentation is a process that occurs often in ill-defined domains and that helps deal with the illdefinedness. Typically a notion of "correctness" for an argument in an ill-defined domain is impossible to define or verify formally because the underlying concepts are open-textured and the quality of the argument may be subject to discussion or even expert disagreement. Previous research has highlighted the advantages of graphical representations for learning argumentation skills. A number of intelligent tutoring systems have been built that support students in rendering arguments graphically, as they learn argumentation skills. The relative instructional benefits of graphical argument representations have not been reliably shown, however. In this paper we present a formative evaluation of LARGO (Legal ARgument Graph Observer), a system that enables law students graphically to represent examples of legal interpretation with hypotheticals they observe while reading texts of U.S. Supreme Court oral arguments. We hypothesized that, compared to a text-based alternative, LARGO's diagramming language geared toward depicting hypothetical reasoning processes, coupled with non-directive feedback, helps students better extract the important information from argument transcripts and better learn argumentation skills. A first pilot study, conducted with volunteer first-semester law students, provided support for the hypothesis. The system especially helped lower-aptitude students learn argumentation skills, and LARGO improved the reading skills of students as they studied expert arguments. A second study with LARGO was conducted as a mandatory part of a first-semester University law course. Although there were no differences in the learning outcomes of the two conditions, the second study showed some evidence that those students who engaged more with the argument diagrams through the advice did better than the text condition. One lesson learned from these two studies is that graphical representations in intelligent tutoring systems for the ill-defined domain of argumentation may still be better than text, but that engagement is essential.
49 citations
••
TL;DR: Inq-ITS (inquiry intelligent tutoring system) as discussed by the authors uses educational data mining to assess science inquiry skills, as described as 21st century skills, in the context of complex systems.
49 citations
••
28 Jun 2011TL;DR: An automated detector that can predict a student's future performance on a transfer post-test, a post- test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics is presented.
Abstract: We present an automated detector that can predict a student's future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics. We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tutors that has been shown to predict performance on paper post-tests of the same skills studied in the intelligent tutor. We also find that this detector only needs limited amounts of student data (the first 20% of a student's data from a tutor lesson) in order to reach near-asymptotic predictive power.
49 citations