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Assessment in traditional courses has been limited to either instructor grading, or problems that lend themselves well to relatively simple automation, such as multiple-choice bubble exams. Progress in educational technology, combined with economies of scale, allows us to radically increase both the depth and the accuracy of our measurements of what students learn. Increasingly, we can give rapid, individualized feedback for a wide range of problems, including engineering design problems and free-form text answers, as well as provide rich analytics that can be used to improve both teaching and learning. Data science and integration of data from disparate sources allows for increasingly inexpensive and accurate micro-assessments, such as those of open-ended textual responses, as well as estimation of higher-level skills that lead to long-term student success.

Voice communication adds nuance and expressivity to virtual discussions, but its one-shot nature tends to discourage collaborators from utilizing it. However, text-based interfaces have made voice editing much easier, especially with recent advancements enabling live, time-aligned speech transcription. We introduce SimpleSpeech, an easy-to-use platform for asynchronous audio communication (AAC) with lightweight tools for inserting content, adjusting pauses, and correcting transcript errors. Qualitative and quantitative results suggest that novice audio producers, such as high school students, experience decreased mental workload when using SimpleSpeech to produce audio messages than without editing. The linguistic formality in SimpleSpeech messages was also studied, and found to form a middle ground between oral and written media. Our findings on editable voice messages show new implications for the optimal design and use cases of AAC systems.

In this demo, we present RichReview, a multi-modal peer discussion system, implemented as an XBlock in the edX courseware platform. The system brings richness similar to face-to-face communication into online learning at scale. With this demonstration, we discuss the system?s scalable back-end architecture, semantic voice editing user interface, and a future research plan for the profile based group-assignment scheme.

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.

We present results from a pilot study where students successfully created complex assessments for a MOOC in introductory electronics -- an area with a very large expert-novice gap. Previous work in learnersourcing found that learners can productively contribute through simple tasks. However, many course resources require a high level of expertise to create, and prior work fell short on tasks with a large expert-novice gap, such as textbook creation or concept tagging. Since these constitute a substantial portion of course creation costs, addressing this issue is prerequisite to substantially shifting MOOC economics through learnersourcing. This represents one of the first successes in learnersourcing with a large expert-novice gap. In the pilot, we reached out to 206 students (out of thousands who met eligibility criteria) who contributed 14 complex high-quality design problems. This results suggests a full cohort could contribute hundreds of problems. We achieved this through a four-pronged approach: (1) pre-selecting top learners (2) community feedback process (3) student mini-course in pedagogy (4) instructor review and involvement.

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.