[on behalf of Joseph Moxley & the organizers of workshops/tutorials @ EDM
2016]
Most of the workshops & tutorials have posted their schedules!
URL: http://www.educationaldatamining.org/EDM2016/workshops.html
*EDM 2016 Workshops*
WS-1: Computer-Supported Peer Review in Education (CSPRED-2016) - Full day
http://www.cspred.org
Edward Gehringer, North Carolina State University
Ferry Pramudianto, North Carolina State University
Yang Song, North Carolina State University
Computer-supported peer review is drawing increasing attention from
educators and researchers. It produces more copious feedback than the
instructor or course staff could provide, and delivers it more quickly. It
provides authors with multiple perspectives on their work, rather than the
singular voice of a teacher. For the instructor, it generates multiple
performance measures that can be used to judge the class’s progress.
As an inherently interdisciplinary topic, peer review stands to benefit
from the perspectives of learning scientists, technologists, and
instructors, as well as psychologists, anthropologists, statisticians,
designers, and other interested parties. The workshop calls for
presentation of both early and mature research. Technology demonstrations
are also welcome.
WS-2: Writing Analytics, Data Mining, and Writing Studies - Full day
http://toolsforwriters.com/edm-workshop-writing-analytics-data-mining-writing-studies/
Val Ross, University of Pennsylvania
Alex Rudniy, Fairleigh Dickinson University
Joe Moxley, University of South Florida
David Eubanks, Furman University
The primary goal of this workshop is to facilitate a research community
around the topic of large-scale data analysis with a particular focus on
writing studies, data mining, and analytics. The workshop hopes to generate
cross-disciplinary research among writing program directors and faculty,
computational linguists, computer scientists, and educational measurement
specialists.
Presenters will address
How can data mining and analytics be leveraged to better meet the needs of
students and educational institutions?
What are the best practices for adapting the state-of-the-art data mining
approaches to the educational domain, with specific attention to teaching
and assessing writing?
How can researchers detect and assess students’ affective and emotional
states while engaging the writing construct?
For assessing writing, automated grading, automated commenting, natural
language or textual data processing:
What are applications of massive parallel computations?
What are current advances and future directions in the artificial
intelligence field?
What methods, tools or big data platforms are more efficient?
What are effective pre-processing techniques, e.g. for the
Extract/Transform/Load phase?
What are successful evaluation and validation methods?
Digital tools such as My Reviewers that enable instructors to grade and
comment on student papers and peer reviews online are transforming how
instructors and students critique documents and have the potential to
transform how writing and writing programs are assessed. Beyond profoundly
altering how faculty and students respond to writing, these tools aggregate
e-portfolios, facilitate distributive evaluation, and archive data that
allow researchers to mine texts and map student outcomes in order to
produce analytics that inform users, researchers, and administrators.
Rather than limit assessment to cognitive measures, these toolsets
facilitate gathering authentic assessment information about students’
intrapersonal and interpersonal competencies.
WS-3: Educational Data Analysis using LearnSphere - Full day
http://pslcdatashop.web.cmu.edu/EDM2016/
John Stamper, Carnegie Mellon University
Kenneth Koedinger, Carnegie Mellon University
Philip Pavlik, University of Memphis
Carolyn Rose, Carnegie Mellon University
Ran Liu, Carnegie Mellon University
Michael Eagle, Carnegie Mellon University
Michael Yudelson, Carnegie Mellon University
Kalyan Veeramachaneni, Massachusetts Institute of Technology
LearnSphere’s goal is to support any custom analysis workflow that can be
applied to educational datasets (such as those in DataShop, DiscourseDB,
MOOCdb) and to produce standardized workflow outputs that facilitate
quantitative and qualitative model comparisons. We invite researchers to
submit 2-4 page analysis descriptions. Strong submissions will have high
level descriptions of the analysis workflow and detailed information on the
format of input data and resulting outputs. Accepted participants will be
eligible for a travel scholarship and have the opportunity to publish
outcomes in the EDM workshop proceedings.
This workshop will explore the application and refinement of novel
educational data mining workflows using LearnSphere, a new $5 million NSF
funded data sharing and analysis portal that extends the existing DataShop
infrastructure and includes teams from Carnegie Mellon, Stanford, Memphis,
and MIT. Increased flexibility to accommodate custom educational analysis
workflows is one of the core ways in which LearnSphere expands upon
DataShop.
*EDM 2016 Tutorials*
Tutorial 1: SAS Tools for Educational Data Mining - Full day
http://blogs.sas.com/content/sascp/2016/05/19/edm-2016-tutorial-sas-tools-for-educational-data-mining/
Jennifer Sabourin, SAS Institute
Scott McQuiggan, SAS Institute
Andre De Waal, SAS Institute
Researchers in the EDM community have always relied on sophisticated tools
to analyze data and build models. As the amount of data that can be
collected and stored grows, the need for tools capable of handling "big
data" becomes ever more prevalent. SAS® Analytics U is a new initiative for
making SAS data analysis and mining tools available for free to educational
researchers and instructors. These tools are designed for handling very
large data sets and can be run in the cloud, saving researchers valuable
time and resources. Furthermore, SAS Analytics U provides a community of
SAS educators and learners to share resources and information about SAS
tools and techniques. This tutorial aims to introduce researchers to the
tools available through SAS Analytics U and how they can be applied to the
field of Educational Data Mining. We will provide an overview of the SAS
architecture and provide instruction on the key features of each tool in
the suite.
If you intend to participate in the hands-on activities, please bring a
laptop with SAS University Edition already installed. The process can take
up to an hour so there will not be time for it on the day of the tutorial.
The free download is available at
http://www.sas.com/en_us/software/university-edition.html
Tutorial 2: Massively Scalable EDM with Spark - Full day
Tristan Nixon, Institute for Intelligent Systems, University of Memphis
The creation and availability of ever-larger datasets is motivating the
development of new distributed technologies to store and process data
across clusters of servers. Apache Spark has emerged as the new standard
platform for developing highly scalable cluster computing applications. It
offers a wide range of connectors to numerous databases and enterprise data
management systems, an ever-growing library of machine-learning algorithms
and the ability to process streaming data in near-realtime. Developers can
write their applications in Java, Scala, Python and R. Applications can be
run locally (for easy development and testing), and deployed to dedicated
clusters or on clusters leased from cloud-computing providers. This will be
a full-day tutorial at EDM 2016 on developing massively scalable machine
learning and data mining applications with Spark. Participants will be
expected to follow along with all examples on their own laptops throughout
the tutorial. All code used in the tutorial will either be taken from
publicly available examples, or be available for download from the IEDMS
github repository, and made available under a very liberal open source
license. All examples will be designed to process a modestly sized sample
of the KDD cup dataset available from the DataShop.
