The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

By Neil Heffernan III, Cristina Heffernan. International Journal of Artificial Intelligence in Education, 2014

570 citations recorded by the source corpus.

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controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies.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.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/b20bc662ee7b686294af71b227038c98e44352a4

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Citations in the corpus

Vision, challenges, roles and research issues of Artificial Intelligence in Education (2020, background): “A more recent example is ASSISTments that combines the features of intelligent tutoring with assesstement to provide real-time feedback to students while woking on assignments and offers data-driven reports for teachers on each assignment (Heffernan & Heffernan, 2014).”
Learning at Scale (2018, background): “Another example is Intelligent Tutoring Systems that adapt the learning experience to individual students’ knowledge and performance (cf. Cognitive Tutors and AssistMENTS; Koedinger and Corbett 2006; Heffernan and Heffernan 2014).”
Stupid Tutoring Systems, Intelligent Humans (2016, background): “I am referring, of course, to the ASSISTments system (Heffernan and Heffernan 2014 ), one of the most widely used (and simplest) intelligent tutoring systems in common usage today.”
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher-AI Complementarity (2019, background): “…that a student has mastered particular skills) are one of the most central analytics presented by common reporting systems for ITSs (e.g., Heffernan & Heffernan, 2014; Khachatryan et al., 2014; Ritter, Carlson, Sandbothe, & Fancsali, 2015), the superpower “Knowing whether students really…”
Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education (2021, background): “From supporting the automatic or semi-automatic assessment of students’ performance and tracking of students’ progress (Heffernan and Heffernan 2014; Luckin 2017) to providing students with personalized scaffolding and recommendations (Albacete et al. 2019; Tarus et al. 2018).”
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education (2021, background): “All of three data sets are provided from ASSISTments [9].”