Call for Paper:

There has been ongoing concern about early developments in students’ academic careers preventing them from having later access to a broad range of careers. In recent years, early-warning systems have emerged at the K-12 level. However, these systems typically warn school districts about only some potential forms of risk, most commonly dropout or school violence. While some efforts have extended to predicting college enrollment or SAT score, a student can graduate from high school, attend college, achieve an acceptable SAT score, and still find that their earlier learning experiences shape their opportunities to choose a broad range of careers.

In 2017-2018, researchers at Worcester Polytechnic Institute and the University of Pennsylvania released an 11-year longitudinal data set spanning from middle school usage of a blended learning system for mathematics (ASSISTments) to the students’ eventual choice of career, focusing on whether experiences in mathematics class corresponded to eventually choosing a STEM (Science, Technology, Engineering, and Mathematics) job or graduate program after college. This data set was released as part of a competition affiliated with the NSF Northeast Big Data Hub and the Spoke on Big Data for Education. Over two hundred participants registered in the competition and 74 participants eventually submitted solutions.

https://sites.google.com/view/assistmentsdatamining/data-mining-competition-2017

Topics:

In this workshop, we invite participants in the competition to share their findings, both in terms of data mining and education research, with each other and with the broader scientific community. Relevant paper topics may include but are not limited to:

• Detecting and tracking student interest in STEM careers
• Detecting and predicting participation in STEM careers
• Identifying the characteristics of students who will participate in STEM
• Identifying the attributes of students who are successful and/or engaged in STEM in middle school but who will lose interest in later years
• How the findings of this competition may contribute to the design of systems that increase interest in studying and working in STEM fields
• Long-term and longitudinal interventions related to this competition
• Longitudinal development and change in students from middle school to post-college
• Discoveries about machine learning, time series modeling, feature extraction and selection, and student modeling produced through participating in the competition
• Higher-level papers on the contributions of competitions such as this one to educational research

Submissions, Dates, and Format:

Submissions due: April 25, 2018, 11.59pm Hawaii time

Notification of acceptance: May 9, 2018

Papers must be submitted in the EDM conference format (not the JEDM format) http://educationaldatamining.org/EDM2018/submission/

Papers are single-blinded, and may be either 4-6 pages (short paper) or 6-10 pages (full paper).

Submit papers to: assistmentscomp2018 [at] wpi.edu

Workshop Organizers

Dr. Ryan Baker

Associate Professor, University of Pennsylvania

Dr. Neil Heffernan

Professor at Worcester Polytechnic Institute and the co-creator of ASSISTments

Dr Ivon Arroyo

Associate Professor, Worcester Polytechnic Institute

Dr. Beverly Woolf

Research Professor, University of Massachusetts Amherst

With Assistance From

Thanaporn "March" Patikorn

Ph.D. student, Worcester Polytechnic Institute

Contact information:

If you have any questions or concerns, please contact us at assistmentscomp2018 [at] wpi.edu