Modern computer systems have been built around the assumption that persistent storage is accessed via a slow, block-based interface. However, new byte-addressable, persistent memory technologies such as phase change memory (PCM) offer fast, fine-grained access to persistent storage.In this paper, we present a file system and a hardware architecture that are designed around the properties of persistent, byteaddressable memory. Our file system, BPFS, uses a new technique called short-circuit shadow paging to provide atomic, fine-grained updates to persistent storage. As a result, BPFS provides strong reliability guarantees and offers better performance than traditional file systems, even when both are run on top of byte-addressable, persistent memory. Our hardware architecture enforces atomicity and ordering guarantees required by BPFS while still providing the performance benefits of the L1 and L2 caches.Since these memory technologies are not yet widely available, we evaluate BPFS on DRAM against NTFS on both a RAM disk and a traditional disk. Then, we use microarchitectural simulations to estimate the performance of BPFS on PCM. Despite providing strong safety and consistency guarantees, BPFS on DRAM is typically twice as fast as NTFS on a RAM disk and 4-10 times faster than NTFS on disk. We also show that BPFS on PCM should be significantly faster than a traditional disk-based file system.

Massive open online courses (MOOCs) rely primarily on discussion forums for interaction among students. We investigate how forum design affects student activity and learning outcomes through a field experiment with 1101 participants on the edX platform. We introduce a reputation system, which gives students points for making useful posts. We show that, as in other settings, use of forums in MOOCs is correlated with better grades and higher retention. Reputation systems additionally produce faster response times and larger numbers of responses per post, as well as differences in how students ask questions. However, reputation systems have no significant impact on grades, retention, or the students' subjective sense of community. This suggests that forums are essential for MOOCs, and reputation systems can improve the forum experience, but other techniques are needed to improve student outcomes and community formation. We also contribute a set of guidelines for running field experiments on MOOCs.

This research investigates how to introduce synchronous interactive peer learning into an online setting appropriate both for crowdworkers (learning new tasks) and students in massive online courses (learning course material). We present an interaction framework in which groups of learners are formed on demand and then proceed through a sequence of activities that include synchronous group discussion about learnergenerated responses. Via controlled experiments with crowdworkers, we show that discussing challenging problems leads to better outcomes than working individually, and incentivizing people to help one another yields still better results. We then show that providing a mini-lesson in which workers consider the principles underlying the tested concept and justify their answers leads to further improvements. Combining the mini-lesson with the discussion of the multiple-choice question leads to significant improvements on that question. We also find positive subjective responses to the peer interactions, suggesting that discussions can improve morale in remote work or learning settings.

Peer learning, in which students discuss questions in small groups, has been widely reported to improve learning outcomes in traditional classroom settings. Classroom-based peer learning relies on students being in the same place at the same time to form peer discussion groups, but this is rarely true for online students in MOOCs. We built a software tool that facilitates chat-based peer learning in MOOCs by 1) automatically forming ad-hoc discussion groups and 2) scaffolding the interactions between students in these groups. We report on a pilot deployment of this tool; post-use surveys administered to participants show that the tool was positively received and support the feasibility of synchronous online collaborative learning in MOOCs.

Type-preserving compilers translate well-typed source code, such as Java or C#, into verifiable target code, such as typed assembly language or proof-carrying code. This paper presents the implementation of type-preserving compilation in a complex, large-scale optimizing compiler. Compared to prior work, this implementation supports extensive optimizations, and it verifies a large portion of the interface between the compiler and the runtime system. This paper demonstrates the practicality of type-preserving compilation in complex optimizing compilers: the generated typed assembly language is only 2.3% slower than the base compiler's generated untyped assembly language, and the type-preserving compiler is 82.8% slower than the base compiler.