Difference between revisions of "CUMULATE"
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* assertions about user's knowledge of concepts ([concepts'] knowledge) | * assertions about user's knowledge of concepts ([concepts'] knowledge) | ||
* summaries of user's interaction with learning objects ([learning objects'] progress) | * summaries of user's interaction with learning objects ([learning objects'] progress) | ||
| − | [[CUMULATE]] implements several inference mechanisms to produce assertions about user's knowledge and progress | + | [[CUMULATE]] implements several inference mechanisms to produce assertions about user's knowledge and progress, including the following. |
| + | * thresholded averaging | ||
| + | * asymptotic user knowledge assessment | ||
| + | * time-spent-reading | ||
| + | |||
| + | == Modeling User's Knowledge == | ||
| + | [[CUMULATE]] models user's knowledge of concepts with respect to the following levels of [http://www.officeport.com/edu/blooms.htm Bloom's taxonomy]. | ||
| + | * knowledge - corresponds to reading tutorials or book chapters ([[Knowledge Sea II]], [[AnnotatEd]]) | ||
| + | * understanding - reviewing examples, watching demos ([[WebEx]], [[WADEIn]] demo mode) | ||
| + | * comprehension - solving problems ([[QuizPACK]], [[SQL KnoT]], [[QuizJET]] | ||
== CUMULATE protocol == | == CUMULATE protocol == | ||
Revision as of 22:42, 23 February 2009
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This page is under construction. More content will be added soon |
CUMULATE (Centralized User Modeling Architecture for TEaching) is a central user modeling server
designed to provide user modeling functionality to a student-adaptive educational system. It collects evidence (events) about student learning from multiple servers that interact with the student. It stores students' activities and infers their learning characteristics, which are the basis for an individual adaptation to them. ... External and internal inference agents process the flow of events and update the values in the inference model of the server. Each inference agent is responsible for maintaining a specific property in the inference model, such as the current motivation level of the student or the student's current level of knowledge for each course topic...
The Adaptive Web. Methods and Strategies of Web Personalization (pp. 136-154). Berlin / Heidelberg: Springer.
Contents
Overview
CUMULATE is a centralized user modeling server built for the [[ADAPT2|ADAPT2 architecture. It is mainly targeted at providing user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE's data model consists of the following.
- users, groups of users, and group membership links (syndicated from Knowledge Tree)
- learning objects, learning object providers (client applications)
- knowledge components - concepts - grouped in learning domains
- metadata index - links between learning objects and corresponding concepts
- user activity log - historical log of all reported user interaction with learning objects
- assertions about user's knowledge of concepts ([concepts'] knowledge)
- summaries of user's interaction with learning objects ([learning objects'] progress)
CUMULATE implements several inference mechanisms to produce assertions about user's knowledge and progress, including the following.
- thresholded averaging
- asymptotic user knowledge assessment
- time-spent-reading
Modeling User's Knowledge
CUMULATE models user's knowledge of concepts with respect to the following levels of Bloom's taxonomy.
- knowledge - corresponds to reading tutorials or book chapters (Knowledge Sea II, AnnotatEd)
- understanding - reviewing examples, watching demos (WebEx, WADEIn demo mode)
- comprehension - solving problems (QuizPACK, SQL KnoT, QuizJET
CUMULATE protocol
Interaction with CUMULATE is regulated by a set of protocols specified in this document.
more coming soon
Publications
- Zadorozhny, V., Yudelson, M., and Brusilovsky, P. (2008) A Framework for Performance Evaluation of User Modeling Servers for Web Applications. Web Intelligence and Agent Systems 6(2), 175-191. DOI
- Yudelson, M., Brusilovsky, P., and Zadorozhny, V. (2007) A user modeling server for contemporary adaptive hypermedia: An evaluation of the push approach to evidence propagation. In Conati, C., McCoy, K. F., and Paliouras, G. Eds., User Modeling, volume 4511 of Lecture Notes in Computer Science, pp 27-36. Springer, 2007. PDF DOI
- Brusilovsky, P., Sosnovsky, S. A., and Shcherbinina, O. (2005). User Modeling in a Distributed E-Learning Architecture. Paper presented at the 10th International Conference on User Modeling (UM 2005), Edinburgh, Scotland, UK, July 24-29, 2005. PDF DOI
