Professor Heffernan receives an alumni award from CMU.
A50 (2020) Heffernan selected to be keynote speaker at the AIED 2020 conference. https://www.youtube.com/watch?v=S-AydzWsjeU&feature=youtu.be
A50 Best Student Paper Award! Patikorn, T. & Heffernan, N. T. (2020, August 12) Effectiveness of Crowd-Sourcing On-Demand Tutoring from Teachers in Online Learning Platforms. Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S). Pages 115–124. https://doi.org/10.1145/3386527.3405912. Best Student Paper Awardee.
A49 (2019) A study on the effectiveness of ASSISTments was reviewed by the U.S. Department of Education's What Works Clearinghouse, and was awarded the WWC status of "positive effects without reservation". Detail: A draft press release.
A46 (2016) Dr. and Cristina Heffernan were invited in December of 2016 to present on ASSISTments at the White House to educate the President's staff on cutting-edge educational developments. The recorded talk is available here.
A45 (2016) Spoke on panel at the White House about using platforms to do open science. Heffernan was chosen to speak because of his article written for the Brookings Institute about the ASSISTments as a shared scientific instrument.
A44 (2016) At the same October meeting SRI was asked to present on the ASSISTments results that were at that time not yet released by the AERA OPEN journal. Roschelle, J., Feng, M., Murphy, R. & Mason, C. (2016). Online Mathematics Homework Increases Student Achievement. AERA OPEN. October-December 2016, Vol. 2, No. 4, pp. 1–12. DOI: 10.1177/2332858416673968
A43 (2016) Heffernan was one of the seven founders of the International Educational Data Mining Society (“EDM”). It is now an open society with 244 attendees at the most recent conference, EDM2016. This is a selective conference, accepting 27% of 109 submissions as full papers for a thriving journal. For EDM2016, of the 80 papers (full and short), Heffernan co-authored three, one other paper used ASSISTments to run studies, four papers used ASSISTments data (three of them replicating the innovative deep learning research published at NIPS that used Khan Academy Data and ASSISTments data), and an additional 17 of the papers cite Heffernan's work. In the last few years, over 11 papers have been published utilizing the data we generated. WPI's leadership role in EDM was documented in a paper by external researchers who looked at all the EDM related papers; WPI and CMU are the two hubs.
A42 (2014) Cited in another report from the U.S. Department of Education about ASSISTments as an exemplar for its giving data back to teachers quickly. To quote the report:
"ASSISTments is an example of a computer-based system that uses a bank of standards-aligned math problems to analyze the patterns in students’ responses, providing timely feedback and appropriate scaffolding tailored to each student. In addition, the system provides analytics for the teacher that describe individual student progress and common conceptual challenges, supporting teacher decisions about coaching for individual students and about the class discussion topics that will be most productive for the most students."
A41 (2015) Selected to co-chair the AIED 2015 conference. The Chairs oversee what papers are selected, by soliciting good reviewers and ultimately make the decision of what to accept.
A40 (2014) Recognized as having the second highest number of papers in a publication analyzing the publication record at EDM. WPI is cited many times for being tied with CMU for worldwide leadership in this area. See this paper for details: Nawaz, S., Marbouti, F. & Strobel, J. (2014). Analysis of the Community of Learning Analytics. Retrieved from http://ceur-ws.org/Vol-974/lakdatachallenge2013_05.pdf.
A39 Elected to Executive Committee of AIED and the Steering Committee for EDM. These groups run these respective scientific societies. For EDM, I was one of the handful of people who started the EDM. After a few years, when the conference was established, we turned it into a democratically open organization. Later, I was elected to serve on the Steering Committee. I am proud of the fact that we have an open society and a "publisher-free" policy where all papers are freely available on the web.
A38 (2014) Invited to write one of four papers for a special issue on "Landmark Systems" in Educational Technology. The paper is below.
Heffernan, N. & Heffernan, C. (2014) The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. International Journal of Artificial Intelligence in Education. Link to the Springer version DOI 10.1007/s40593-014-0024-x . December 2014, Volume 24, Issue 4, pp 470-497.
A37 (2013) Gave a talk at Rice University's Personalized Learning Workshop.
A36 (2013) Took a team of WPI students to two "hack-a-thons." The first was for InBloom in North Carolina, and the second was at Facebook Headquarters, funded in part by the Gates Foundation. The students won an award of $1,000 in 2013.
A35 (2013) Cited in the new U.S. Department of Education called "Promoting Grit, Tenacity, and Perseverance—Critical Factors for Success in the 21st Century." See here.
A34 (2013) Asked to give a webinar by the Maine Department of Education for principals to learn about the grant for the state of Maine.
A33 (2013) Heffernan gave an hour-long talk at Columbia's Teachers College EdLab.
A32 Invited to be one of two speakers representing the EDUCUASE portfolio at the Council of Chief State School Officers’ (CCSSO) National Conference on Student Assessment (NCSA). The Bill and Melinda Gates Foundation is the primary funder of the EDUCAUSE’s Next Generation Learning Challenge managed by EDUCAUSE. Of the 18 grantees in that program ASSISTments was honored to be one of two projects that was chosen to be highlighted. This happened a second time, and we will present to CCSSO in San Diego in February.
A31 (2012) Invited to give talk at FLAIRS and asked to submit to paper. Pardos, Z. & Heffernan, N. (2012) Student Modeling vs. Student Modeling. 25th International Florida Artificial Intelligence Research Society Conference.
A30 (2012) Invited to speak at the Cyberlearning Summit in January of 2012. The talk is here.
A29 (2012) Featured in the U.S. Department of Education recent report on Big Data, “Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics.” 2012.
A28 (2011) Keynote speaker for the School Science and Mathematics Association's National Conference, 2011.
A27 (2011) Selected to be a Professor of Computer Science to address Congress on behalf of the Computing Research Association. The program is called CCC Leadership in Science Policy Institute (LiSPI), 2011.
A26 (2011) Erdos number = 2 via Sarkozy from the following paper. Trivedi, S., Pardos, Z., Sarkozy, G. & Heffernan, N. (2011). Spectral Clustering in Educational Data Mining. Educational Data Mining 2011 Conference.
A25 (2011) Graduate students, Yu Tao, nominated for Best Student Paper at FLAIRS. She developed a new way to use extra information about the number of hints a student takes to improve upon the current state of the art method of tracking student knowledge. CP40, 2011.
A24 (2011) Asked by Susan Fuhrman, Brian Rowan, and Jim Gee to serve on a National Academy of Education panel they are convening called Exploring the Uses of Data Captured from Adaptive Educational Technologies. "We are writing to invite you to participate in a series of two meetings that the National Academy of Education will convene on the use of data captured from adaptive educational technologies in education research. An important purpose of these meetings is intended to move forward the research community’s understanding of available data from adaptive educational technologies and their possible applications, as well as the development of a future research agenda to promote effective use of these data across the K-12 and higher education fields." See here their full description, 2011.
A23 (2010) Awarded the Carnegie Mellon University Alumni Award, 2010.
A22 (2010) Zach Pardos, (Heffernan’s graduate student who earned his Ph.D. in 2012), placed 4th out of over 100 competitors on the KDD Cup. See press release, 2010. This led to tsunami of 11 papers the following year.
A21 (2008) Best Paper nomination. Of the 61 papers at ITS 2008, eight were nominated for "Best Paper" awards and I was an author of three of the eight.
A20 (2009) Best Paper award. “I am pleased to inform you that your recent UMUAI article was selected as the winner of the 2009 James Chen Annual Award for Best UMUAI Paper. A prize committee of three editorial board members reviewed all nominated articles from the 2009 production volume and selected your paper as the winner,” said an editor of UMAUI. UMAUI is one of the journals that has the highest impact factor ratings, 2009.
The following paper follows up on this work and shows an even more impressive result in that in this work we controlled for time and showed ASSISTments is a better assessor than traditional paper and pencil.
Feng, M. & Heffernan, N. (2010). Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? Educational Data Mining 2010.
A19 (March 2010) ASSISTments was cited in the National Educational Technology Plan of March 2010. Here is what they had to say:
“The ASSISTment system, currently used by more than 4,000 students in Worcester County Public Schools in Massachusetts, is an example of a web-based tutoring system that combines online learning and assessment activities (Feng, Heffernan, & Koedinger, 2009). The name “ASSISTment” is a blend of tutoring “assistance” with “assessment” reporting to educators. The ASSISTment system was designed by researchers at Worcester Polytechnic Institute and Carnegie Mellon University to teach middle school math concepts and to provide educators with a detailed assessment of students’ developing math skills and their skills as learners. It gives educators detailed reports of students’ mastery of 100 math skills, as well as their accuracy, speed, help-seeking behavior, and number of problem-solving attempts. The ASSISTment system can identify the difficulties that individual students are having and the weaknesses demonstrated by the class as a whole so that educators can tailor the focus of their upcoming instruction.
When students respond to ASSISTment problems, they receive hints and tutoring to the extent they need them. At the same time, how individual students respond to the problems and how much support they need from the system to generate correct responses constitute valuable assessment information. Each week, when students work on the ASSISTment website, the system “learns” more about the students’ abilities and thus can provide increasingly appropriate tutoring and can generate increasingly accurate predictions of how well the students will do on the end-of-year standardized test. In fact the ASSISTment system has been found to be more accurate at predicting students’ performance on the state examination than the pen-and-paper benchmark tests developed for that purpose (Feng, Heffernan, & Koedinger, 2009).” National Ed. Tech. Plan: http://www.ed.gov/sites/default/files/netp2010.pdf, 2010.
A18 (2009-10) Journal paper cited as one of the Best of the Year in the journal. It was selected by the editors to appear in the 2009-2010 IEEE Computer Society Publications Sampler. It had earlier been selected at the featured article of that month’s publication.
Feng, M., Heffernan, N.T., Heffernan, C., Mani, M. (2009). Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models. IEEE Transactions on Learning Technologies, 2(2), 79-92. Featured Article of eight articles in the edition. (Based on PP8 and WP15)
A17 (2009) Best Paper First Authored by a Student (Zach Pardos) at the 2nd International Educational Data Mining Conference.
Pardos, Z.A., Heffernan, N.T. (2009). Determining the Significance of Item Order In Randomized Problem Sets. In Barnes, Desmarais, Romero & Ventura (Eds) Proc. of the 2nd International Conference on Educational Data Mining. pp. 111-120.
A16 (2009) Honorable Mention for Best Paper. First authored by a student at the Artificial Intelligence in Education Conference in Brighton, England. The conference rejected over 60% of the papers, our paper was selected as one of two honorable mentions out of a set of approximately 25 papers.
Razzaq, L. & Heffernan, N. (2009). To Tutor or Not to Tutor: That is the Question. In Dimitrova, Mizoguchi, du Boulay & Graesser (Eds.) Proceedings of the 2009 Artificial Intelligence in Education Conference. pp. 457-464.
A15 (2009) Cited by Provost Office for having the most number of students presenting their research at WPI Grad Day 2009, a campus wide event with about 150 graduate students presenting posters of their work. 2009.
A14 Award from Massachusetts Association of School Committee’s Annual Award for “Community Leader for Public Education.” The citation reads: “Since coming to Worcester in 2002, Dr. and Cristina Heffernan have generously dedicated their time, talent and boundless energy to building ASSISTments, an online math tutoring system, for Worcester middle and high school students and their teachers. This program is a magnificent tool that is distinguished by, among other facets, its clarity and easy of access and use.”
A13 (2008) Outstanding Junior Faculty Researcher award from Sigma Xi Research Award, 2008.
A12 (2007) Honored for meritorious achievement as an adviser to best Major Qualifying Project in the department, Worcester Polytechnic Institute, 2007.
A11 (2006) Best 3-page "Poster" Award (with student Jason Walonoski), International Intelligent Tutoring System Conference, 2006.
A10 (2006) Patent - US Patent Filed by WPI for technology used in the ASSISTment intelligent tutoring system. 2006.
A9 (2006) Nominated for Best Paper First Authored by a student (with Mingyu Feng), a conference with an 11% acceptance rate; World Wide Web Conference (WWW'06), 2006.
A8 Invited to serve on the NSF/CRA CyberLearning Workshop focused on Technology-Enabled Assessment to help shape a research agenda for NSF's EHR Directorate.
A7 Received National Science Foundation's most prestigious award for young researchers called the CAREER award.
A6 Nominated for the Massachusetts Teacher Associations “Friend of Education” award by Forest Grove Middle School for ASSISTment project work.
A5 Nominated for WPI's Ambassador Award for doing an outstanding job of representing WPI to the community.
A4 (2004) Best 3-page "Poster" Award (with student Leena Razzaq), International Intelligent Tutoring System Conference, 2004.
A3 Patent - US Patent #6,634,887: Methods and systems for tutoring using a tutorial model with interactive dialog (with Ken Koedinger).
A2 (2002) Post-Doctoral Fellowship from The National Academy of Education/Spencer Foundation, 2002.
A1 (1997) The David Marr Award for Best Student Paper Award, (with Ken Koedinger), Cognitive Science Conference, 1997.