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.
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.
Learn Machine Learning Stanford University Professor and earn certification to full proof your career. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
Education has mostly followed the same structure for centuries -- e.g., the "sage on a stage" and "assembly line" models. As AI continues to disrupt industries like consumer electronics, ecommerce, media, transportation, and healthcare, is education the next big opportunity? Given that education is the foundation that prepares people to pursue advancements in all the other fields, it has the potential to be the most impactful application of AI. The three segments of the education market -- K-12, higher education, and corporate training -- are going through transitions. In the K-12 market, we are seeing the effect of the newer, more rigorous academic standards (Common Core, Next Generation Science Standards) shifting the focus toward measuring students' critical thinking and problem-solving skills and preparing them for college and career success in the 21st century.
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.