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Rapid remediation of student misconceptions and knowledge gaps is one of the most effective ways to help students learn. We present a system for recommending additional resources, such as videos, reading materials, and web pages for students working through on-line course materials. This can provide remediations of knowledge gaps involving complex concepts. The system relies on learners suggesting resources which helped them, leveraging economies of scale as found in MOOCs and similar at-scale settings in order to build a rich body of remediations. The system allows for remediation of much deeper knowledge gaps than in prior work on remediation in MOOCs. We validated the system through a deployment in an introductory computer science MOOC. We found it lead to more in-depth remediation than prior strategies.

Rising college tuitions are putting increasing financial pressure on families, while at the same time, teachers are repeating tasks like grading and problem creation thousands of times across campuses. Open educational resources (OERs) have the promise of reducing this inefficiency, offering higher quality education at lower cost. However, the adoption of OERs remains limited in part due to issues with organization, quality, search, and fit to the curriculum of individual classrooms. This paper presents a new format for OERs based on a common course skeleton shared among a community of students and instructors. That community jointly creates and improves educational resources around that skeleton. This model resolves many of the classical problems in finding and organizing OERs. We present ways to use such communities to create richer educational resources, such as intelligent tutoring systems (ITS).

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

6.002x is the first electronic circuits course to be taught online to tens of thousands of students. The goal of the 6.002x experiment was to explore ways to use computer assisted instruction to surpass the quality of traditional residential teaching. By providing superior on-line content delivery and assessment, we hope to both be able to educate people without access to education, and to improve residential education by allowing professors to focus on higher value tasks. We improved on the classroom experience in several ways. Students can actively monitor their current levels of mastery and to self-pace in response. They can identify and break through misconceptions before moving on to more advanced material. The massive scale of the classroom has participants on-line 24/7, allowing students to ask questions and receive peer answers in almost real-time. The platform allows for substantial data collection on testing, allowing us to incrementally and scientifically improve courses. Finally, the amount of effort that can be invested into a course is much greater when it can be amortized across tens of thousands. In order to achieve this, we had to overcome a number of challenges: finding mechanisms to allow automated grading, overcoming the lack of in-person interactions, and overcoming the lack of student access to laboratory equipment. The course was shown successful in both residential and mass-scale settings.