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 learner-generated 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.

The results of a study of online peer learning suggests that it may be advantageous to automatically assign students to small peer learning groups based on how many students initially get answers to questions correct.

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.

We study effects of introducing a real-time chatroom into a massive open online course with several thousand students, supplementing an existing forum. The chatroom was supported by teaching assistants, and generated thousands of lines of discussion by 28\% of 681 consenting chat condition participants, mostly on-topic. Despite this, chat activity remained low ($\mu=8.2$ messages per hour) and we could find no significant effect of chat use on objective or subjective dependent variables such as grades, retention, forum participation, or students' sense of community. Further investigation reveals that only 12\% of chat participants have substantive interactions, while the remainder are either passive or have trivial interactions that are unlikely to result in learning.

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.

Domain-expert productivity programmers desire scalable application performance, but usually must rely on efficiency programmers who are experts in explicit parallel programming to achieve it. Since such programmers are rare, to maximize reuse of their work we propose encapsulating their strategies in mini-compilers for domain-specific embedded languages (DSELs) glued together by a common high-level host language familiar to productivity programmers. The nontrivial applications that use these DSELs perform up to 98% of peak attainable performance, and comparable to or better than existing hand-coded implementations. Our approach is unique in that each mini-compiler not only performs conventional compiler transformations and optimizations, but includes imperative procedural code that captures an efficiency expert's strategy for mapping a narrow domain onto a specific type of hardware. The result is source- and performance-portability for productivity programmers and parallel performance that rivals that of hand-coded efficiency-language implementations of the same applications. We describe a framework that supports our methodology and five implemented DSELs supporting common computation kernels.

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.

Inverted indexes using sequences of characters (n-grams) as terms provide an error-resilient and language-independent way to query for arbitrary substrings and perform approximate matching in a text, but present a number of practical problems: they have a very large number of terms, they exhibit pathologically expensive worst-case query times on certain natural inputs, and they cannot cope with very short query strings. In word-based indexes, static index pruning has been successful in reducing index size while maintaining precision, at the expense of recall. Taking advantage of the unique inclusion structure of n-gram terms of different lengths, we show that the lexicon size of an n-gram index can be reduced by 7 to 15 times without any loss of recall, and without any increase in either index size or query time. Because the lexicon is typically stored in main memory, this substantially reduces the memory required for queries. Simultaneously, our construction is also the first overlapping n-gram index to place tunable worst-case bounds on false positives and to permit efficient queries on strings of any length. Using this construction, we also demonstrate the first feasible n-gram index using words rather than characters as units, and its applications to phrase searching.

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.