Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

Pinkwart, Niels and Aleven, Vincent and Ashley, Kevin D and Lynch, Collin (2009) Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO. E-learning and Education, 1 (5). ISSN 1860-7470

Available under License : See the attached license file.

Download (373kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


The Intelligent Tutoring System LARGO is designed to help law students learn argumentation skills. The approach implemented in LARGO uses transcripts of oral arguments as learning resources: Students annotate them and create graphical representations of the argument flow. The system encourages students to reflect upon arguments proposed by the attorneys and helps students detect possible weaknesses in their analysis of the dispute. Technically, graph grammar and collaborative filtering algorithms are employed to detect these weaknesses. This article describes how “usage contexts” are determined and used to create adaptive feedback in LARGO. On the basis of a controlled study with the system that took place with law students at the University of Pittsburgh, we discuss to what extent the automatically calculated usage contexts can predict student’s learning gains.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Pinkwart, Niels
Aleven, Vincent
Ashley, Kevin Dashley@pitt.eduASHLEY
Lynch, Collin
Date: 2009
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: E-learning and Education
Volume: 1
Number: 5
Publisher: FernUniversität Hagen, CampusSource
Institution: University of Pittsburgh
Schools and Programs: School of Law > Law
School of Law > Law > Faculty Publications
Refereed: Yes
Uncontrolled Keywords: e-learning, tutoring, systems, learning, management, system
ISSN: 1860-7470
Official URL:
Article Type: Research Article
Date Deposited: 17 Dec 2012 18:54
Last Modified: 01 Nov 2017 13:56


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item