Travis Mandel

ContextType: using hand posture information to improve mobile touch screen text entry

The challenge of mobile text entry is exacerbated as mobile devices are used in a number of situations and with a number of hand postures. We introduce ContextType, an adaptive text entry system that leverages information about a users hand posture (using two thumbs, the left thumb, the right thumb, or the index finger) to improve mobile touch screen text entry. ContextType switches between various keyboard models based on hand posture inference while typing. ContextType combines the users posture-specific touch pattern information with a language model to classify the users touch events as pressed keys. To create our models, we collected usage patterns from 16 participants in each of the four postures. In a subsequent study with the same 16 participants comparing ContextType to a control condition, ContextType reduced total text entry error rate by 20.6%.

Towards automatic experimentation of educational knowledge

We present a general automatic experimentation and hypothesis generation framework that utilizes a large set of users to explore the effects of different parts of an intervention parameter space on any objective function. We also incorporate importance sampling, allowing us to run these automatic experiments even if we cannot give out the exact intervention distributions that we want. To show the utility of this framework, we present an implementation in the domain of fractions and numberlines, using an online educational game as the source of players. Our system is able to automatically explore the parameter space and generate hypotheses about what types of numberlines lead to maximal short-term transfer; testing on a separate dataset shows the most promising hypotheses are valid. We briefly discuss our results in the context of the wider educational literature, showing that one of our results is not explained by current research on multiple fraction representations, thus proving our ability to generate potentially interesting hypotheses to test.

Practical error correction for resource-constrained wireless networks: unlocking the full power of the CRC

Bit errors are common in wireless networks, and techniques for overcoming them traditionally consist of expensive retransmission (e.g. Automatic Repeat reQuest (ARQ)) or expensive Forward Error Correction (FEC), both of which are undesirable in resource-constrained wireless networks such as wireless sensor networks (WSNs). In this paper, we present TVA (Transmit-Verify-Acknowledge), a protocol that can correct errors without adding additional redundancy to data packets. Instead, TVA corrects errors using the redundancy inherent in Cyclic Redundancy Checks (CRCs). The ubiquity of CRCs has the advantage of allowing TVA to be both backwards-compatible and backwards-efficient with link-layer protocols such as IEEE 802.15.4. We present a novel method of CRC error correction, which is compact and computationally efficient, and is designed to correct the most common error patterns observed in WSNs. We demonstrate that TVA provides reliability effectively equivalent to that of ARQ. We perform trace-driven simulations using data from sensor network deployments in different environments and analyze TVAs performance at different message lengths. To demonstrate the practicality of TVA, we implement it in TinyOS, and perform experiments on MicaZ motes to evaluate TVA in the presence of 802.11 interference. We find that TVA improves over ARQ and FEC-based protocols, using 31% less redundant communication and 30% less additional time to recover errored packets compared to ARQ.