We offer high quality courses from the top universities, for free to everyone. We currently host courses from Princeton University, Stanford University, University of California, Berkeley, University of Michigan-Ann Arbor, and University of Pennsylvania. We are changing the face of education globally, and we invite you to join us.
We are a social entrepreneurship company that partners with the top universities in the world to offer courses online for anyone to take, for free. We envision a future where the top universities are educating not only thousands of students, but millions. Our technology enables the best professors to teach tens or hundreds of thousands of students.
Through this, we hope to give everyone access to the world-class education that has so far been available only to a select few. We want to empower people with education that will improve their lives, the lives of their families, and the communities they live in.
Classes offered on Coursera are designed to help you master the material. When you take one of our classes, you will watch lectures taught by world-class professors, learn at your own pace, test your knowledge, and reinforce concepts through interactive exercises. When you join one of our classes, you’ll also join a global community of thousands of students learning alongside you. We know that your life is busy, and that you have many commitments on your time. Thus, our courses are designed based on sound pedagogical foundations, to help you master new concepts quickly and effectively. Key ideas include mastery learning, to make sure that you have multiple attempts to demonstrate your new knowledge; using interactivity, to ensure student engagement and to assist long-term retention; and providing frequent feedback, so that you can monitor your own progress, and know when you’ve really mastered the material.
We offer courses in a wide range of topics, spanning the Humanities, Medicine, Biology, Social Sciences, Mathematics, Business, Computer Science, and many others. Whether you’re looking to improve your resume, advance your career, or just learn more and expand your knowledge, we hope there will be multiple courses that you find interesting.
The design of our platform is based on sound pedagogical foundations that aim to help students learn the material quickly and effectively. This design is inspired by the work of many researchers who have helped shape our understanding of pedagogical techniques that contribute to student learning and engagement. While there are many papers that contributed to our understanding of key ideas in pedagogy, here are a few that were particularly influential.
The efficacy of online learning
There is sometimes controversy regarding the extent to which online instruction is as effective as face-to-face instruction. In September 2010, the Department of Education issued a detailed report that conducts a meta-analysis of 45 published studies that compare online and face-to-face learning. This analysis demonstrates very convincingly that online learning methods are, on average, at least as effective as face-to-face learning. Further, hybrid methods, which involve both methods of instruction, and is being offered by our partner universities to many of their on-campus students using our platform, are considerably more effective than either method alone.
The importance of retrieval and testing for learning
Many people think that the primary purpose of homeworks is to assess or to evaluate students. We believe that a far more important purpose is that they drive learning, and ensure long-term retention. A key factor in the design of the Coursera system is the extensive use of interactive exercises, which we believe are critical for student engagement and learning. Even within our videos, there are multiple opportunities for interactions: the video frequently stops, and students are asked to answer a simple question to test whether they are tracking the material. This strategy has value not only in maintaining student focus and engagement. Research shows that even simple retrieval questions have significant pedagogical value. For example, in two papers in Science, (Karpicke and Roediger III, 2008; Karpicke and Blunt, 2011) show that activities that require students to retrieve or reconstruct knowledge produces significant gains in learning – much more so than many other learning strategies.
Many of our courses’ homeworks are designed to give students multiple opportunities to learn the content and demonstrate their knowledge. In many traditional classes, if a student attempts a homework and does not do well, he or she simply get a low score on the assignment, and instruction moves to the next topic, providing the student a poor basis for learning the next concept. The feedback is also often given weeks after the concept was taught, by which point the student barely remembers the material, and rarely goes back to review the concepts to understand them better. In the Coursera platform, we typically give immediate feedback on that concept the student did not understand. In many cases, we provide randomized versions of the same assignment, so that a student can re-study and re-attempt the homework. This process is called Mastery Learning, and was shown in a seminal paper by Bloom to increase student performance by about one standard deviation over more traditional forms of instruction. This means that if in a traditional class 50% of all students pass a certain (median) level of performance, with Mastery Learning, about 84% of students now achieve this level of performance.
In many courses, the most meaningful assignments do not lend themselves easily to automated grading by a computer. For example, in a poetry course, we would want the students to practice critical thinking and interpretive skills by answering essay-style questions, which do not have clear right or wrong answers. Similar issues arise when we are evaluating business plans, engineering designs, medical chart reviews, or many others. This is particularly an issue in courses in the Humanities, Social Sciences, Business, and other disciplines where a relatively small fraction of the content lends itself well to an auto-graded format. Given our commitment to offer courses from a broad range of disciplines, we have invested substantial effort in developing the technology of peer assessments, where students can evaluate and provide feedback on each other’s work. This technology draws on two bodies of literature: First, the education literature on peer assessments. Following the literature on student peer reviews, we have developed a process in which students are first trained using a grading rubric to grade other assessments. This has been shown to result in accurate feedback to other students, and also provide a valuable learning experience for the students doing the grading. Second, we draw on ideas from the literature on crowd-sourcing, which studies how one can take many ratings (of varying degrees of reliability) and combine them to obtain a highly accurate score. Using such algorithms, we expect that by having multiple students grade each homework, we will be able to obtain grading accuracy comparable or even superior to that provided by a single teaching assistant.
Active learning in the classroom
Many of our partner institutions are planning to use the capabilities of our platform to provide their on-campus students with a significantly improved learning experience. Many studies have demonstrated that standard lecturing is not the most effective mode of instruction. Considerably more effective are the teaching methods that use active learning and interactive engagement between faculty and students, and between students and their peers. For example, Deslauriers, Schelew and Wieman (Science 2011) describe an experiment in an introductory physics class that compares a traditional lecture setting to one that uses active learning. In the active learning group, student engagement nearly doubled, attendance increased by 20%, and average scores on the same test increased from 41% to 74% (where random guessing would give a score of 23%). Similar results, by Wieman, Mazur, and others, were obtained across multiple disciplines and diverse institutions. Our platform offers universities the opportunity to move much of the traditional lecturing – required for conveying the necessary material – from inside to outside the classroom, in an online learning format that is, in many ways, more interactive and more engaging. By doing so, they open up space in the curriculum for the active learning strategies that are considerably more effective in increasing engagement, attendance, and learning.
Daphne Koller is the Rajeev Motwani Professor in the Computer Science Department at Stanford University and the Oswald Villard University Fellow in Undergraduate Education. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. She is the author of over 180 refereed publications, which have appeared in venues that include Science, Cell, and Nature Genetics (her H-index is over 80). She also has a long-standing interest in education. She founded the CURIS program, the Stanford Computer Science Department’s undergraduate summer internship program, and the Biomedical Computation major at Stanford. She pioneered in her classroom many of the ideas that are key to Stanford’s massive online education effort. She was awarded the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001the MacArthur Foundation Fellowship in 2004, the ACM/Infosys award in 2008, and was inducted into the National Academy of Engineering in 2011. Her teaching was recognized via the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and by being named a Bass University Fellow in Undergraduate Education.
Andrew Ng is an Associate Professor of Computer Science at Stanford University. He is also the Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 15 professors and about 150 students/post docs. In 2008, together with SCPD he started SEE (Stanford Engineering Everywhere), Stanford’s first major attempt at free, online distributed education, which made publicly available about a dozen Stanford engineering classes. Over a million people have viewed SEE’s videos. At Stanford, he also led the development of the OpenClassroom and the ml-class/db-class online education platforms, which were the precursor to the Coursera platform. In Fall 2011, he was the instructor of ml-class, a Machine Learning class that was one of Stanford’s first massive online courses, and had an enrollment of over 100,000 students.
In addition to his work on online education, Ng also works on machine learning, specifically on building AI systems via large scale brain simulations. His previous work includes autonomous helicopters, the STanford AI Robot (STAIR) project, and ROS (the most widely used open-source robotics software platform today). Ng is the author or co-author of over 150 published papers in machine learning, and his group has won best paper/best student paper awards at ICML, ACL, CEAS, 3DRR. He is a recipient of the Alfred P. Sloan Fellowship, and the 2009 IJCAI Computers and Thought award, one of the highest honors in AI.
John Doerr is a partner at Kleiner Perkins Caufield & Byers. Doerr earned his B.S. and M.S. degrees in electrical engineering from Rice University and an M.B.A. from Harvard Business School. Since joining KPCB in 1980, John and his partners have backed some of the world’s most successful entrepreneurs, including Larry Page, Sergey Brin and Eric Schmidt of Google; Jeff Bezos of Amazon.com, Scott Cook and Bill Campbell of Intuit; and Mark Pincus of Zynga. John’s passion is helping entrepreneurs create the “Next Big Thing” in mobile and social networks, greentech innovation, education and economic development. Ventures sponsored by John have created more than 200,000 new jobs. Outside of KPCB, Doerr also supports entrepreneurs focused on the environment, public education and alleviating global poverty. These include NewSchools.org, TechNet.org, the Climate Reality Project and ONE.org. Doerr is a member of the American Academy of Arts and Sciences, and a member of U.S. President Barack Obama’s Council on Jobs and Competitiveness.
Scott Sandell joined NEA in 1996 and became a General Partner in 2000. Sandell holds an AB in Engineering Sciences from Dartmouth College and an MBA from Stanford. He focuses on investments in information technology and alternative energy, and is responsible for NEA’s activities in China. Present board memberships include Bloom Energy, CloudFlare, DreamFactory, Fusion-io, HelioVolt, SolFocus, Spreadtrum Communications, SugarCRM, Tableau Software, and Workday. Sandell has also sponsored investments in 3ware, Amplitude Software, Data Domain, Fineground Networks, Neoteris (NASDAQ: JNPR), NetIQ (NASDAQ: NTIQ), Playdom,Salesforce.com (NYSE: CRM) and WebEx (NASDAQ: WEBX). In 2011, Sandell was also named to the #5 position on Forbes’ Midas List of the 100 most successful technology investors.