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JournalISSN: 1560-4292

International Journal of Artificial Intelligence in Education 

Springer Science+Business Media
About: International Journal of Artificial Intelligence in Education is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Educational technology & Computer science. It has an ISSN identifier of 1560-4292. Over the lifetime, 369 publications have been published receiving 9290 citations.


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Journal ArticleDOI
TL;DR: Why ASSISTments has been successful and what lessons were learned are shared and goals for the future will be presented.
Abstract: 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controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies.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.

325 citations

Journal ArticleDOI
TL;DR: This study was one of the first large-scale classroom evaluations of the integrated use of an Intelligent Tutoring System (ITS) in high schools and showed that a new algebra curriculum with an embedded intelligent tutoring system dramatically enhanced high-school students’ learning.
Abstract: Our 1997 article in IJAIED reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students’ learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research embedded in them could have real impact in schools. This study was one of the first large-scale classroom evaluations of the integrated use of an Intelligent Tutoring System (ITS) in high schools. A core challenge was figuring out how to embed this new technology into a curriculum and into the existing social context of schools. A key element of the study design was to include multiple kinds of assessments, including standardized test items and items measuring complex problem solving and use of representations. The results were powerful: “On average the 470 students in experimental classes outperformed students in comparison classes by 15 % on standardized tests and 100 % on tests targeting the [course] objectives.” We suggested that the study was evidence “that laboratory tutoring systems can be scaled up and made to work, both technically and pedagogically, in real and unforgiving settings like urban high schools.” Since this study, many more classroom studies comparing instruction that includes an ITS against business as usual have been conducted, often showing advantages for the ITS-enhanced curricula. More rigorous randomized field trials are now more commonplace, but the approach of using multiple assessments in large-scale randomized field trials has not caught on. Cognitive task analysis will remain fundamental to the success of ITSs. A key remaining question for ITS is to find out how they can be used most effectively to support open-ended problem solving, either online or offline. Given all the recent excitement around Massive Open Online Courses (MOOCs), it is interesting to note that our field of Artificial Intelligence in Education has been making huge, less recognized, progress with impact on millions of students and with the majority of those students finishing the course!

313 citations

Journal ArticleDOI
TL;DR: A comprehensive review of ASAG research and systems according to history and components concludes that an era of evaluation is the newest trend in ASAGResearch, which is paving the way for the consolidation of the field.
Abstract: Automatic short answer grading (ASAG) is the task of assessing short natural language responses to objective questions using computational methods. The active research in this field has increased enormously of late with over 80 papers fitting a definition of ASAG. However, the past efforts have generally been ad-hoc and non-comparable until recently, hence the need for a unified view of the whole field. The goal of this paper is to address this aim with a comprehensive review of ASAG research and systems according to history and components. Our historical analysis identifies 35 ASAG systems within 5 temporal themes that mark advancement in methodology or evaluation. In contrast, our component analysis reviews 6 common dimensions from preprocessing to effectiveness. A key conclusion is that an era of evaluation is the newest trend in ASAG research, which is paving the way for the consolidation of the field.

265 citations

Journal ArticleDOI
TL;DR: It is suggested that two parallel strands of research need to take place in order to impact education in the next 25 years: one is an evolutionary process, focusing on current classroom practices, collaborating with teachers, and diversifying technologies and domains, and the other is a revolutionary process.
Abstract: The field of Artificial Intelligence in Education (AIED) has undergone significant developments over the last twenty-five years. As we reflect on our past and shape our future, we ask two main questions: What are our major strengths? And, what new opportunities lay on the horizon? We analyse 47 papers from three years in the history of the Journal of AIED (1994, 2004, and 2014) to identify the foci and typical scenarios that occupy the field of AIED. We use those results to suggest two parallel strands of research that need to take place in order to impact education in the next 25 years: One is an evolutionary process, focusing on current classroom practices, collaborating with teachers, and diversifying technologies and domains. The other is a revolutionary process where we argue for embedding our technologies within students’ everyday lives, supporting their cultures, practices, goals, and communities.

264 citations

Journal ArticleDOI
TL;DR: The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time.
Abstract: To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not occurred in the data before. We provide a detailed description of the system’s implementation and perform a technical evaluation on a small set of data to determine the effectiveness of the component algorithms and ITAP’s potential for self-improvement. The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time.

222 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202329
202243
202169
202019
201919
201824