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Notice

AISB miscellaneous Bulletin Item

2nd CFP: UMUAI Special Issue on Data Mining for Personalized Educational Systems

http://www.uco.es/grupos/kdis/umuai/special_issue.html

CALL FOR PAPERS

Special Issue on Data Mining for Personalized Educational Systems

User Modeling and User-Adapted Interaction:
The Journal of Personalization Research
(An international journal published by Springer Verlag)

Web site: http://www.uco.es/grupos/kdis/umuai/special_issue.html

***********************************************************************

SCOPE OF THE SPECIAL ISSUE:

Educational data mining (EDM) is an emerging discipline, concerned with
developing methods for exploring the unique types of data that can be
gathered in an educational context. The increase in instrumented educational
software and in databases of student test scores has created large data
repositories reflecting how students learn. EDM focuses on computational
approaches for using those data to address important educational questions.

One focus of EDM consists in improving personalized educational systems,
which is the topic of this special issue.

EDM can enhance the effectiveness, personalization and/or adaptivity of such
learning environments. In turn, student data coming from personalized
systems are semantically richer than data from traditional web-based
education system, and can lead to deeper diagnostic analysis.

Contributions to this special issue are particularly welcome in, but not
limited to, the following topics related to Data Mining for Personalized
Educational
Systems:

- Analysis and visualization of student interactions.
- Applying recommender system in educational environments.
- Prediction of performance and marks.
- Applying sequence mining in educational data.
- User modeling using data mining.
- Applying text mining in educational data.
- Improving educational software.
- Detecting outliers, cheating, gaming, errors, misuse, gifted, etc.
- Detecting motivation, affective, behavior, learning styles, etc.
- Improving teacher support and feedback.
- Test item analysis.

HOW TO SUBMIT

Submissions to the special issue should follow the UMUAI formatting
guidelines and submission instructions available at:
http://www.umuai.org/paper_submission.html

Each submission should note that it is intended for the Special Issue on
Data Mining for Personalized Educational Systems.
Potential authors are asked to submit a tentative title and short abstract
(which can be altered for the actual submission) to assist in the formation
of a panel of appropriate reviewers.

UMUAI is an archival journal that publishes mature and substantiated
research results on the (dynamic) adaptation of computer systems to their
human users, and the role that a model of the system about the user plays in
this context.
Many articles in UMUAI are quite comprehensive and describe the results of
several years of work.
Consequently, UMUAI gives "unlimited" space to authors (so long as what they
write is important).
Authors whose paper exceeds 40 pages in journal format (including
illustrations and references) are however requested to supply a short
justification upon submission that explains why a briefer discussion of
their research results would not be advisable.

REVIEW PROCESS

Submissions will undergo the normal review process, and will be reviewed by
three established researchers selected from a panel of reviewers formed for
the special issue.
Barring unforeseen problems, authors can expect to be notified regarding the
review results within three-four months of submission.

IMPORTANT DATES:

- Notification of Intent to Submit: as soon as possible
- Submission of Title and Abstract: March 1, 2010    - Manuscript 
Submission: April 16, 2010   
GUEST EDITORS

Dr. Cristóbal Romero, Dept. of Computer Sciences, Univ. of Córdoba, Spain.
email: cromero@uco.es
http://www.uco.es/~in1romoc/

Dr. Sebastián Ventura, Dept. of Computer Sciences, Univ. of Córdoba, Spain.
email: sventura@uco.es
http://www.uco.es/~ma1vesos/

If you have any questions do not hesitate to ask the guest editors