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.
Matt Coatney is a technology executive, business advisor, entrepreneur, author, and speaker. His focus is on bringing advanced artificial intelligence and analytic technologies to market. He has co-founded three companies, advised several others, and contributed to the early success of two different tech startups. Matt has also launched data analytics products designed for the fields of life sciences, healthcare, government, finance, and law. Currently Matt is the VP of Services for Exaptive; he previously was the IT strategy lead for global law firm WilmerHale, and was in charge of technology and operations for a legal search product at LexisNexis.
During which season of the year would a rabbit's fur be thickest? A computer program called Aristo can tell you because it read about bears growing thicker pelts during winter in a fourth-grade study guide, and it knows rabbits are mammals, too. Aristo is being developed by researchers at the Allen Institute for Artificial Intelligence in Seattle, who want to give machines a measure of common sense about the world. The institute's CEO, Oren Etzioni, says the best way to benchmark the development of their digital offspring is to use tests designed for schoolchildren. He's trying to convince other AI researchers to adopt standardized school tests as a way to measure progress in the field.
A group of schoolchildren who won a robotics competition were subjected to a barrage of racist abuse from some rival pupils and their parents who shouted: "Go back to Mexico". It was the first time that pupils from Pleasant Run Elementary School had entered the robotics challenge. Their victory over the youngsters from other Indianapolis schools, put them a step closer to the state championship. Yet as the children, aged nine and ten, left the event and walked out to the parking area, some of the children they had just beaten, along with their parents, unleashed racist comments. Kids on winning robotics team told, 'Go back to Mexico' https://t.co/iGmm9yOQsF