There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The power of feedback.

Videos are a widely-used kind of resource for online learning. This paper presents an empirical study of how video production decisions affect student engagement in online educational videos. To our knowledge, ours is the largest-scale study of video engagement to date, using data from 6.9 million video watching sessions across four courses on the edX MOOC platform. We measure engagement by how long students are watching each video, and whether they attempt to answer post-video assessment problems.Our main findings are that shorter videos are much more engaging, that informal talking-head videos are more engaging, that Khan-style tablet drawings are more engaging, that even high-quality pre-recorded classroom lectures might not make for engaging online videos, and that students engage differently with lecture and tutorial videos.Based upon these quantitative findings and qualitative insights from interviews with edX staff, we developed a set of recommendations to help instructors and video producers take better advantage of the online video format. Finally, to enable researchers to reproduce and build upon our findings, we have made our anonymized video watching data set and analysis scripts public. To our knowledge, ours is one of the first public data sets on MOOC resource usage.

http://up.csail.mit.edu/other-pubs/las2014-pguo-engagement.pdf

How video production affects student engagement: an empirical study of MOOC videos

The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

A tutorial on human activity recognition using body-worn inertial sensors

A Tutorial on Human Activity Recognition Using Body-Worn

Many people learn how to use complex authoring applications through tutorials. However, user interfaces for authoring tools differ between versions, platforms, and competing products, limiting the utility of tutorials. Our goal is to make tutorials more useful by enabling users to repurpose tutorials between similar applications. We introduce UI translation interfaces which enable users to locate commands in one application using the interface language of another application. Our end-user tool, ShowMeHow, demonstrates two interaction techniques to accomplish translations: 1) direct manipulation of interface facades and 2) text search for commands using the vocabulary of another application. We discuss tools needed to construct the translation maps that enable these techniques. An initial study (n=12) shows that users can locate unfamiliar commands twice as fast with interface facades. A second study showed that users can work through tutorials written for one application in another application.

ShowMeHow: translating user interface instructions between applications

Developers increasingly consult online examples and message boards to find solutions to common programming tasks. On the web, finding solutions to debugging problems is harder than searching for working code. Prior research introduced a social recommender system, HelpMeOut, that crowdsources debugging suggestions by presenting fixes to errors that peers have applied in the past. However, HelpMeOut only worked for statically typed, compiled programming languages like Java. We investigate how suggestions can be provided for dynamic, interpreted web development languages. Our primary insight is to instrument test-driven development to collect examples of bug fixes. We present Crowd::Debug, a tool for Ruby programmers that realizes these benefits.

/pdf/crowdsourcing-suggestions-to-programming-problems-for-2cihk6wey5.pdf

Crowdsourcing suggestions to programming problems for dynamic web development languages

3D modelers often wish to showcase their models for sharing or review purposes. This may consist of generating static viewpoints of the model or authoring animated fly-throughs. Manually creating such views is often tedious and few automatic methods are designed to interactively assist the modelers with the view authoring process. We present a view authoring assistance system that supports the creation of informative view points, view paths, and view surfaces, allowing modelers to author the interactive navigation experience of a model. The key concept of our implementation is to analyze the model's workflow history, to infer important regions of the model and representative viewpoints of those areas. An evaluation indicated that the viewpoints generated by our algorithm are comparable to those manually selected by the modeler. In addition, participants of a user study found our system easy to use and effective for authoring viewpoint summaries.

/pdf/history-assisted-view-authoring-for-3d-models-2ob31k3nyc.pdf

History assisted view authoring for 3D models

Crowdsourcing and human computation are transforming human-computer interaction, and CHI has led the way. The seminal publication in human computation was initially published in CHI in 2004 [1], and the first paper investigating Mechanical Turk as a user study platform has amassed over one hundred citations in two years [5]. However, we are just beginning to stake out a coherent research agenda for the field. This workshop will bring together researchers in the young field of crowdsourcing and human computation and produce three artifacts: a research agenda for the field, a vision for ideal crowdsourcing platforms, and a group-edited bibliography. These resources will be publically disseminated on the web and evolved and maintained by the community.

/pdf/crowdsourcing-and-human-computation-systems-studies-and-25p21bvcpa.pdf

Crowdsourcing and human computation: systems, studies and platforms

As personal fabrication becomes increasingly accessible and popular, a larger number of makers, many without formal training, are dabbling in embedded and electronics design. However, existing general-purpose, board-level circuit design techniques do not share desirable properties of modern software development, like rich abstraction layers and automated compiler checks, which facilitate powerful tools that ultimately lower the barrier to entry for programming, by allowing a higher level of design-separating specification from implementation-and providing automated guidance and feedback. In this paper, we present a novel methodology for embedded design generation that allows the generation of complete designs from high-level specifications. We present an implementation capable of synthesizing a variety of examples to show that our approach is viable. Starting from user-specified requirements and a library of available components, our tool encodes the design space as a system of constraints. Off-the-shelf solvers then reason over these constraints to create a block diagram with sufficient information to generate the device firmware and circuit netlist.

Björn Hartmann

Papers

ShowMeHow: translating user interface instructions between applications

Crowdsourcing suggestions to programming problems for dynamic web development languages

History assisted view authoring for 3D models

Crowdsourcing and human computation: systems, studies and platforms

Turning coders into makers: the promise of embedded design generation