Workshops & Tutorials
The workshops and tutorial day will be on Friday June 26th, 2015. The schedulling is the following:
09:00 - 10:30 Session 1
10:30 - 11:00 coffee break
11:00 - 12:30 Session 2
12:30 - 14:00 Lunch
14:00 - 15:·30 Session 3
15:30 - 16:00 coffee break
16:00 - 17:30 Session 4
17:45 - 18:30 Opening
18.30 – 20:00 Opening keynote
20:00 - … RECEPTION
WS-1: Graph-based Educational Data Mining (G-EDM 2015) - Full day
Dr. Collin F. Lynch, Department of Computer Science, North Carolina State University,
Dr. Tiffany Barnes, Department of Computer Science, North Carolina State University
Dr. Jennifer Albert, Department of Computer Science, North Carolina State University
Michael Eagle, Department of Computer Science, North Carolina State University
Graph data has become increasingly prevalent in data-mining and data analysis. Many types of data can be represented naturally as graphs including social network data, log traversal, and online discussions. This data can be used to address open questions such as:
* What path(s) do high-performing students take through
online educational materials?
* What social networks can foster or depress learning?
* Do users of online learning tools behave as we expect them to do so?
* What substructures are commonly found in student-produced diagrams?
* Can we use prior student data to identify students' solution plan, if any?
* Can we use prior student data to provide meaningful hints in
* Can we identify students who are particularly helpful in a course?
This workshop will provide a forum for interested researchers to discuss ongoing work, share lessons learned, and to identify common challenges. It will be structured as a mini-conference with time set aside for discussions, collaboration, networking, and small-group meetings.
It is open to researchers who have completed mature work on this area that they wish to discuss. It is also open to researchers who have developed graph-based mining techniques that they wish to share and researchers with potential graph data that they wish to discuss. We particularly welcome the submission of short papers and posters describing planned work or work in progress and demonstrations of novel techniques.
SMLIR workshop aims bringing together researchers from academia and industry practitioners with special interest in statistics/machine learning, information retrieval, data mining to (1) discuss current state of the art tools and technologies, (2) identify patterns for proper usage of various options for different tasks, and (3) lay out a vision regarding practical ways in which tools and technologies will influence future applications. The organizers hope to obtain common background knowledge for integrating various tools and technologies in future EDM research infrastructures.
AIED-EDM co-located workshop
- WS-3: International Workshop on Affect, Meta-Affect, Data and Learning (AMADL 2015) - Full day
- Genaro Rebolledo-Mendez, Manolis Mavrikis, Olga C. Santos, Benedict du Boulay, Beate Grawemeyer and Rafael Rojano-Cáceres
Emotions and affect play an important role in learning. There are indications that meta- affect —knowledge about self-affect — might also play a role. There have been various attempts to take them into account both during the design and during the deployment of AIED systems. The evidence for the consequential impact on learning is beginning to strengthen but the field has been mostly focusing on addressing only the complexities of affective and emotional recognition. This has largely slowed down progress on this area. Research needs to be carried out in order to better understand how to respond to what we detect and how to relate to the learner’s cognitive and meta-cognitive skills. One goal might be to design systems capable of recognizing, acknowledging, and responding to learners’ states with the aim of promoting those that are conducive to learning by means of tutorial tactics, feedback interventions, interface adaptations that take advantage of ambient intelligence, for instance. We therefore, need to deepen our knowledge of how emotions relate to issues such as cognition and context.
This workshop is intended to bridge the existing gap between previous research with the ever-increasing understanding and data availability. The colocation of AIED and EDM workshop raises the opportunity to bring the two communities together in a lively discussion about the overlap in the two fields and we therefore explicitly address and target both communities, as indicated by the workshop’s organisers background and the programme committee we are setting up.
Tut-1: Using Natural Language Processing Tools in Educational Data Mining - half day (morning)
Scott Crossley, Georgia State University
Laura Allen, Arizona State University
Danielle McNamara, Arizona State University
The workshop will cover the development, use, and educational data mining applications of a number of freely available natural language processing (NLP) tools such as Coh-Metrix, the Writing Assessment Tool (WAT), the Simple NLP (SiNLP) tool, the Tool for the Automatic Analysis of Lexical Sophistication (TAALES), and the Tool for the Automatic Analysis of Cohesion (TAACO). The workshop will provide the participants with an overview of the types of linguistic features that can be measured with these NLP tools. Additionally, it will describe how these features have been (and can be) used in text analyses that are of importance to the educational data mining community. Participants will receive hands-on training with the tools using data from computerized learning environments. Participants will also be shown how the output from these tools can be used to develop machine learning algorithms that can aid in predicting educational outcomes.
Tut-2: Student Modeling Applications, Recent Developments & Toolkits [SMART tutorial] - Full day
José González-Brenes, Pearson
Kai-Min Chang, Carnegie Mellon University
Michael Yudelson, Carnegie Learning, Inc.
Yoav Bergner, Educational Testing Services
Yun Huang, University Of Pittsburgh
The educational data mining community is starting to fulfill the promise of using data to improve education. The advancement of the field requires the community to be aware of existing tools and results from student modeling. But with a myriad of student modeling techniques and toolkits available, it is easy to be overwhelmed.
In this tutorial we will cover popular and promising toolkits – and the theory behind them. We will demystify the acronym soup in the educational data mining field (BKT, IRT, 1PL, etc). We will help practitioners and researchers alike to get up to speed on student modeling using the latest technology. We are fortunate enough that the toolkits will be presented by the authors who developed them.