Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.
With each passing year, parents are getting more worried about how their children will fare once it's time to take that step from school to the workforce. They have good reason to fret. Some 17 million Americans under age 30--about one third of the under-30 population--are saddled with student debt. Many are worried about their career prospects despite having invested--heavily, in some cases--in education. The cost of college is being hotly debated.
Automated essay scoring (AES) is a broadly used application of machine learning, with a long history of real-world use that impacts high-stakes decision-making for students. However, defensibility arguments in this space have typically been rooted in hand-crafted features and psychometrics research, which are a poor fit for recent advances in AI research and more formative classroom use of the technology. This paper proposes a framework for evaluating automated essay scoring models trained with more modern algorithms, used in a classroom setting; that framework is then applied to evaluate an existing product, Turnitin Revision Assistant.
The crisis in science education and the need for innovative computer-based learning environments has prompted us to develop a multi-agent system, Betty's Brain that implements the learning by teaching paradigm. The design and implementation of the system based on cognitive science and education research in constructivist, inquiry-based learning, involves an intelligent software agent, Betty, that students teach using concept map representations with a visual interface. Betty is intelligent not because she learns on her own, but because she can apply qualitative-reasoning techniques to answer questions that are directly related to what she has been taught. The results of an extensive study in a fifth grade classroom of a Nashville public school has demonstrated impressive results in terms of improved motivation and learning gains. Reflection on the results has prompted us to develop a new version of this system that focuses on formative assessment and the teaching of selfregulated strategies to improve students' learning, and promote better understanding and transfer.
Bringing users into the process of content development may help to reduce the time and cost associated with tutoring system development, and may benefit users by deepening their understanding of the domain. We describe a pilot effort with middle school students who successfully authored word problems for the AnimalWatch intelligent tutoring system for Grade 6 math, and the design and pilot testing of a new module for user-authoring of AnimalWatch problems.