P
---------- Forwarded message ----------
From: George Siemens <gsie...@gmail.com>
Date: 10 August 2011 14:06
Subject: [learninganalytics] CALL FOR PAPERS: JEDM SPECIAL ISSUE:
"EDUCATIONAL DATA MINING ON MOTIVATION, META-COGNITION, AND
SELF-REGULATED LEARNING"
To: learning...@googlegroups.com
Cc: "Ryan S.J.d. Baker" <rsb...@wpi.edu>
Hi all - see below for special issue on educational data mining...
---------- Forwarded message ----------
From: Ryan S.J.d. Baker <ry...@educationaldatamining.org>
Date: 8 August 2011 14:50
Subject: CALL FOR PAPERS: JEDM SPECIAL ISSUE: "EDUCATIONAL DATA MINING
ON MOTIVATION, META-COGNITION, AND SELF-REGULATED LEARNING"
To: edm-an...@freelists.org
Cc: Phil Winne <wi...@sfu.ca>, Kalina Yacef <kal...@it.usyd.edu.au>
CALL FOR PAPER SUBMISSIONS FOR SPECIAL ISSUE OF
THE JOURNAL OF EDUCATIONAL DATA MINING
EDUCATIONAL DATA MINING ON
MOTIVATION, META-COGNITION, AND SELF-REGULATED LEARNING
Guest Editors
Ryan S.J.d. Baker, Worcester Polytechnic Institute (rsb...@wpi.edu)
Philip H. Winne, Simon Fraser University (wi...@sfu.ca)
Aim of Special Issue
We invite paper submissions for a special issue of the peer-reviewed
Journal of Educational Data Mining that focuses on using educational
data mining methods to advance basic and applied research on the
nature and roles of motivation, meta-cognition, and self-regulated
learning (SRL) in learning sciences. Increasingly, it is acknowledged
that SRL processes interact in key fashions with motivational and
meta-cognitive processes. We seek papers that use EDM to explore these
interactions, as well as papers that explore any of these three areas
in isolation or in relation to other important processes and
constructs.
Papers should apply accepted or novel educational data mining methods
in rigorous, demonstrably valid ways to study these topics. We are
interested to assemble a collection of articles that explore how new
methods for measurement and analysis that EDM affords enable new
discoveries in these areas. Because an important goal of this special
issue is to educate researchers who are not familiar with the power
and benefits of data mining, papers should be written in a style that
is simultaneously meaningful to experts in data mining, and
educational for those who are entirely new to these methods. Data can
be drawn from any educational source (e.g. interaction logs,
questionnaire instruments, field observations, video or text replays,
collaborative chats, discussion forums) so long as it supports valid
inference; simulated data is not admissible for this special issue.
All papers must make a contribution to research in the domain studied
and must give full detail on the educational data mining methods used
to derive these contributions; it is not necessary, however, that a
paper make innovations in educational data mining methods although
these are, of course, welcome (so long as they are valid).
Review Process
As stipulated by JEDM reviewing guidelines, each submission will be
peer-reviewed by three colleagues in the field, including both members
of the JEDM editorial board plus reviewers chosen specifically for
this issue.
Submission Guidelines
We invite submissions of any length; see the JEDM submission
guidelines. All submissions can be made electronically via email to
Ryan S.J.d. Baker (rsb...@wpi.edu).
Deadlines
Please submit your manuscript by December 1, 2011. We plan a review
cycle of approximately three months so that you should receive
feedback and a decision by approximately March 1, 2011.
Please direct questions to the guest editors at rsb...@wpi.edu and
wi...@sfu.ca.
We are launching our latest school based badging project. In brief - real brief - the rubrics related to the badges are from 0-3. Youth will earn their achievement if their work earns a "3" rating. In order to get an achievement, youth will submit Voicethread portfolios of their work to the committee associated with a particular badge.
If a youth get a "3" they receive the achievement. If they get less than 3, they only get feedback. That means there is no scaffolding to advance towards an achievement - it's a boolean system. You get it or you don't.
If the process were automated, we could offer leveled achievements - Informational Literacy Achievement, L. 1, Informational Literacy Achievement, L. 2, etc. That would be ideal for the students, and be more game-like (by rewarding for smaller tasks). But for the school staff, the work would be overwhelming, more than four times the work currently planned.
Does any one have any ideas about how to find a balance between these two extremes - between a boolean system with no scaffolding and a scaffolded system the requires more human resources than available?
Barry Joseph
Global Kids