Difference between revisions of "Open Learner Modeling"

From PAWS Lab
Jump to: navigation, search
(→‎Open Learner Modeling with Adaptive Navigation Support in QuizGuide and JavaGuide)
(→‎Open Learner Modeling in ELM-ART)
 
(11 intermediate revisions by the same user not shown)
Line 2: Line 2:
  
 
== Open Learner Modeling in ELM-ART ==
 
== Open Learner Modeling in ELM-ART ==
−
Peter
+
[[ELM-ART]], a combination of a Web-based Electronic Textbook and Intelligent Tutoring System for LISP, was the first Web-Based System to implement open editable learner model. ELM-ART offered a dynamic view, which visualized the current state of learner knowledge about concepts and functions of LISP programming language and allowed learners to correct system's estimation of their knowledge.
 +
 
 +
[[Image:ELM-ART-OLM.gif|400px|OLM in ELM-ART]]
  
 
== Open Learner Modeling in InterBook ==
 
== Open Learner Modeling in InterBook ==
Line 9: Line 11:
 
== Open Learner Modeling with Adaptive Navigation Support in QuizGuide and JavaGuide ==
 
== Open Learner Modeling with Adaptive Navigation Support in QuizGuide and JavaGuide ==
  
−
In our work with [[QuizGuide]] and [[JavaGuide]] systems we attempted to merge the power of open learner models with the power of adaptive navigation support. While traditional OLM focus on reflecting student knowledge, we wanted to give students an immediate chance to act. For example, observing that knowledge of a specific topic is too low, a student might want to immediately access learning content that could help to bridge the gap.
+
In our work with [[QuizGuide]] and [[JavaGuide]] systems we attempted to merge the power of open learner models with the power of adaptive navigation support. While traditional OLM focuses on reflecting student knowledge, we wanted to give students an immediate chance to act. For example, observing that knowledge of a specific topic is too low, a student might want to immediately access learning content that could help to bridge the gap.
  
−
[[QuizGuide]] is an adaptive front-end to a collection of interactive self-assessment questions in the domain of C programming. The domain model in QuizGuide is formed by 22 topics such as variables, constants or character processing. The system is able to model learner knowledge independently by topic and visualize the state of this learner model through adaptive link annotation of the “target-arrow” icons. The number of arrows in the target reflects the level of knowledge the student has acquired on the topic: the more arrows the target has, the higher the level of knowledge. The intensity of the target’s color shows the relevance of the topic to the current learning goal: the more intense the color is, the more relevant the topic. This interface combined successful features of open learner models and navigation support: it provides a clear presentation of student knowledge by topic while also helping the student to select the optimal topic to work (one that is ready to be explored, but still not mastered).
+
[[QuizGuide]] is an adaptive front-end to a collection of interactive self-assessment questions in the domain of C programming. The system is able to model learner knowledge independently by topic and visualize the state of this learner model through adaptive link annotation of the “target-arrow” icons. The number of arrows in the target reflects the level of knowledge the student has acquired on the topic: the more arrows the target has, the higher the level of knowledge. The intensity of the target’s color shows the relevance of the topic to the current learning goal: the more intense the color is, the more relevant the topic. This interface combined successful features of open learner models and navigation support: it provides a clear presentation of student knowledge by topic while also helping the student to select the optimal topic to work (one that is ready to be explored, but still not mastered). Check [[QuizGuide]] section to learn more about the systems and the value of this integrated approach.
  
−
[[Image:Quizguide.gif|QuizGuide]]
+
[[Image:Quizguide.gif|500px|QuizGuide]]
  
−
Despite a relatively simple adaptation approach, the navigation support provided by QuizGuide resulted in a remarkable impact on student performance and motivation to work with the system. In comparison with QuizPACK (Brusilovsky & Sosnovsky, 2005b), an earlier version of the system which provided access to the same quizzes with no navigation support, the average knowledge gain (a difference between post-test and pre-test results on a 10-point test) for the students using QuizGuide increased from 5.1 to 6.5. By guiding students to the right topics at the right time, the system caused a significant increase in the percentage of correctly answered questions from 35.6% to 44.3% (Brusilovsky & Sosnovsky, 2005a). Most remarkable, however, was an increase in the students’ interest in working with the system. The number of attempts, the percentage of students using the system actively, and the percentage of attempted topics increased significantly (Brusilovsky & Sosnovsky, 2005a). A re-implementation of QuizGuide’s adaptive navigation support approach for SQL (Sosnovsky et al., 2008) and Java programming (Hsiao et al., 2009) confirmed this impact in two other domains.
+
== Multi-Layer Zoomable Learner Models ==
 +
Julio
  
 
== Open Social Learner Modeling in Progressor, QuizMAP ==
 
== Open Social Learner Modeling in Progressor, QuizMAP ==

Latest revision as of 20:32, 27 January 2018

About Open Learning Modeling

Open Learner Modeling in ELM-ART

ELM-ART, a combination of a Web-based Electronic Textbook and Intelligent Tutoring System for LISP, was the first Web-Based System to implement open editable learner model. ELM-ART offered a dynamic view, which visualized the current state of learner knowledge about concepts and functions of LISP programming language and allowed learners to correct system's estimation of their knowledge.

OLM in ELM-ART

Open Learner Modeling in InterBook

Peter

Open Learner Modeling with Adaptive Navigation Support in QuizGuide and JavaGuide

In our work with QuizGuide and JavaGuide systems we attempted to merge the power of open learner models with the power of adaptive navigation support. While traditional OLM focuses on reflecting student knowledge, we wanted to give students an immediate chance to act. For example, observing that knowledge of a specific topic is too low, a student might want to immediately access learning content that could help to bridge the gap.

QuizGuide is an adaptive front-end to a collection of interactive self-assessment questions in the domain of C programming. The system is able to model learner knowledge independently by topic and visualize the state of this learner model through adaptive link annotation of the “target-arrow” icons. The number of arrows in the target reflects the level of knowledge the student has acquired on the topic: the more arrows the target has, the higher the level of knowledge. The intensity of the target’s color shows the relevance of the topic to the current learning goal: the more intense the color is, the more relevant the topic. This interface combined successful features of open learner models and navigation support: it provides a clear presentation of student knowledge by topic while also helping the student to select the optimal topic to work (one that is ready to be explored, but still not mastered). Check QuizGuide section to learn more about the systems and the value of this integrated approach.

QuizGuide

Multi-Layer Zoomable Learner Models

Julio

Open Social Learner Modeling in Progressor, QuizMAP

Sharon

Multi-Content Open Social Learner Modeling in Progressor+

Sharon

Mastery Grids: Open Source Open Social Learned Modeling Infrastructure

Julio

Rich OLM - Concept-Level OSLM in Mastery Grids

Julio

Retrieved from "http://adapt2.sis.pitt.edu/w/index.php?title=Open_Learner_Modeling&oldid=3869"