The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching
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The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word “ASSISTments” blends tutoring “assistance” with “assessment” reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013–4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra “Put the teacher in charge, not the computer.” This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about what the platform supports and does not support. In conclusion, goals for the future will be presented.
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In recent years, ITS have mostly been subject and topic specific like ASSISTments, iTalk2Learn and Aida Calculus.
“In recent years, ITS have mostly been subject and topic specific like ASSISTments [25], iTalk2Learn [23] and Aida Calculus.”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [25]
ASSISTments uses homework assignments from existing textbooks.
“One rare exception is ASSISTments, which uses homework assignments from existing textbooks (Heffernan & Heffernan, 2014).”
From Evolution and Revolution in Artificial Intelligence in Education (2016) · cites this paper as Heffernan & Heffernan, 2014
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- Authors
- Heffernan, N. T., & Heffernan, C. L.
- Affiliations
- Worcester Polytechnic Institute (US)
- Corresponding author
- Heffernan, Neil T.
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 24
- Issue
- 4
- Pages
- 470-497
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W1995864528
- Cited by
- 616
- References
- 71
- Citations by year
- 2026: 92 · 2025: 96 · 2024: 87 · 2023: 65 · 2022: 40 · 2021: 54 · 2020: 39 · 2019: 33 · 2018: 32 · 2017: 31 · 2016: 27 · 2015: 19 · 2014: 1
- FWCI
- 25.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
- Keywords
- ASSISTments, online learning platforms, mathematics education, teacher participation in research, randomized controlled trials, student learning data, immediate feedback, teacher autonomy, learning sciences, educational technology design
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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