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Author

Brian McNely

Other affiliations: Ball State University
Bio: Brian McNely is an academic researcher from University of Kentucky. The author has contributed to research in topics: Communication design & Agile software development. The author has an hindex of 11, co-authored 22 publications receiving 323 citations. Previous affiliations of Brian McNely include Ball State University.

Papers
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Proceedings ArticleDOI
05 Oct 2009
TL;DR: This paper explores the development of backchannel persistence through microblogging platforms, and suggests an approach to studying the collaborative affordances of back channel communication by focusing on the related concepts of mobilization and recursive writing collaboration.
Abstract: Digital backchannel communication has become an increasingly important area of study for researchers and practitioners in several fields. From the emergence of wifi-enabled Internet Relay Chat (IRC) to contemporary instances of microblogging and SMS messaging, the role of digital backchannels in enabling collaborative affordances has received much recent attention. As backchannel communication continues to become more prevalent at professional conferences, in educational curricula, and in organizational settings, robust frameworks for understanding the role of backchannel environments in collaborative meaning-making are needed. Drawing upon cultural-historical activity theory and actor network theory, this paper explores the development of backchannel persistence through microblogging platforms, and suggests an approach to studying the collaborative affordances of backchannel communication by focusing on the related concepts of mobilization and recursive writing collaboration.

69 citations

Proceedings ArticleDOI
01 Oct 2012
TL;DR: This paper proposes and then explores a qualitative coding schema for understanding organizational implementations of Instagram within a prominent news organization, a non-profit, and a for-profit retailer.
Abstract: Popular genres of social software increasingly act as regularized discourse within organizations. Recently, image-intensive social software applications have seen rapid adoption as another communicative genre within the ecology of the contemporary organization's social software strategy. These social software genre ecologies may help organizations actively shape what Faber calls image-power, the organization's self-conscious, self-reflective management of public perception and the concomitant shaping of patron identities. This paper proposes and then explores a qualitative coding schema for understanding organizational implementations of Instagram within a prominent news organization, a non-profit, and a for-profit retailer.

49 citations

Journal ArticleDOI
TL;DR: At the time of publication B. McNely was at The University of Kentucky, C. Spinuzzi was atthe University of Texas at Austin, and C. Teston was at the Ohio State University in Columbus, Ohio.
Abstract: At the time of publication B. McNely was at The University of Kentucky, C. Spinuzzi was at The University of Texas at Austin, and C. Teston was at The Ohio State University in Columbus, Ohio.

41 citations

Proceedings ArticleDOI
29 Apr 2012
TL;DR: Uatu, a system designed to visualize the real time contribution and edit history of collaboratively written documents, is described and initial findings indicate both the challenges and promise of delivering useful metrics for collaborative writing scenarios in academe and industry.
Abstract: This paper explores the ways in which participants in writing intensive environments might use learning analytics to make productive interventions during, rather than after, the collaborative construction of written artifacts. Specifically, our work considered how university students learning in a knowledge work model---one that is collaborative, project-based, and that relies on consistent peer-to-peer interaction and feedback---might leverage learning analytics as formative assessment to foster metacognition and improve final deliverables. We describe Uatu, a system designed to visualize the real time contribution and edit history of collaboratively written documents. After briefly describing the technical details of this system, we offer initial findings from a fifteen week qualitative case study of 8 computer science students who used Uatu in conjunction with Google Docs while collaborating on a variety of writing and programming tasks. These findings indicate both the challenges and promise of delivering useful metrics for collaborative writing scenarios in academe and industry.

32 citations

Proceedings ArticleDOI
30 Sep 2013
TL;DR: The author provides a historical and theoretical overview of visual research methods before detailing three interrelated approaches that may be productively applied to work in communication design.
Abstract: Visual research methods include a variety of empirical approaches to studying social life and social processes, including communication and documentation. Developed largely in anthropology and sociology, visual methods typically involve the use of photography, videography, and drawing in qualitative studies of lived experience. Despite the use of visual methods in related fields such as CSCW, HCI, and computer science education, such approaches are underdeveloped in studies of communication design. In this paper, the author provides a historical and theoretical overview of visual research methods before detailing three interrelated approaches that may be productively applied to work in communication design. The author then illustrates how these approaches were adapted to communication design studies in industry and academe before describing implications for future work in this area.

28 citations


Cited by
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Book ChapterDOI
01 Jan 2001
TL;DR: A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers.
Abstract: A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

2,940 citations

Book
01 Nov 2002
TL;DR: Drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short), which aims to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved.
Abstract: From the Book: “Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons: Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). In Test-Driven Development, you: Write new code only if you first have a failing automated test.Eliminate duplication. Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy The two rules imply an order to the tasks ofprogramming: 1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first 2. Green—make the test work quickly, committing whatever sins necessary in the process 3. Refactor—eliminate all the duplication created in just getting the test to work Red/green/refactor. The TDD’s mantra. Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications: If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage. Courage Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects: Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and: Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet. The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test. Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately. That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress. There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code. Once you are finished reading this book, you should be ready to: Start simplyWrite automated testsRefactor to add design decisions one at a time This book is organized into three sections. An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples. I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference. Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read. A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

1,864 citations

01 Jan 2016

1,572 citations

01 Jan 2016
TL;DR: The cognition in the wild is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for reading cognition in the wild. Maybe you have knowledge that, people have look hundreds times for their favorite books like this cognition in the wild, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. cognition in the wild is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the cognition in the wild is universally compatible with any devices to read.

1,268 citations

Journal ArticleDOI
TL;DR: The Pasteurization of France can be viewed as a battle, with its field and its myriad contestants, in which opposing sides attempted to mould and coerce various forces of resistance.
Abstract: BRUNO LATOUR, The pasteurization of France, trans. Alan Sheridan and John Law, Cambridge, Mass., and London, Harvard University Press, 1988, 8vo, pp. 273, £23.95. GEORGES CANGUILHEM, Ideology and rationality in the history of the life sciences, trans. Arthur Goldhammer, Cambridge, Mass., and London, The MIT Press, 1988, 8vo, pp. xi, 160, £17.95. Bruno Latour has written a wonderfully funny book about himself. It is difficult, however, to summarize a text committed to the view that \"Nothing is, by itself, either reducible or irreducible to anything else\", (p. 158). In Latour's opinion, the common view that sociologists of knowledge and scientists are opposed is incorrect. Both groups, according to Latour, are the authors of identical mistakes: reductionism and, relatedly, attempting to conjoin (in the instance of the sociologist) science and society, or (in the instance of the scientist) keeping them apart. For Latour, there are only forces or resistances which different groups encounter and attempt to conquer by forming alliances. These groups, however, are not simply the actors of conventional sociology. They include, for example, microbes, the discovery of the Pasteurians, with which they have populated our world and which we must now take notice of in any encounter or war in which we engage. War is a fundamental metaphor for Latour, since in a war or a battle clashes of armies are later called the \"victory\" of a Napoleon or a Kutuzov. Likewise, he argues, the Pasteurization of France can be viewed as a battle, with its field and its myriad contestants, in which opposing sides attempted to mould and coerce various forces of resistance. Strangely, he points out, the outcome of this huge battle, the labour and struggle of these masses, we attribute to the scientific genius of Pasteur. Pasteur's genius, however, says Latour, lay not in science (for this could be yet another way of making science and society distinct) but as strategist. Pasteur was able to cross disciplinary lines, recruiting allies to laboratory science by persuading them that they were recruiting him. This was possible because, like the armies in battle, they had already done the work of the general. Thus Pasteur's microbiology, which might conventionally be seen as a whole new science, can also be construed as a brilliant reformulation of all that preceded it and made it possible. Hygienists seized on the work of the Pasteurians and the two rapidly became powerful allies because \"The time that they [the hygienists] had made was now working for them\" (p. 52). French physicians, on the other hand, resisted recruitment, since for them it meant enslavement. Finally, however, they recruited the Pasteurians to their enterprise. Pasteurian public health was turned into a triumph of medicine. It is impossible to read this book and not substitute Latour for Pasteur. At the head of his own army, increasingly enlarged by the recruitment of allies, Latour now presents us, in his own language, with something we have made, or at least made possible. The cynic might say, using the old jibe against sociologists, that Latour has explained to us in his own language everything we knew anyway. Retorting thus, however, would be to unselfconsciously make an ally of Latour and miss the point by a narrow margin that might as well be a million miles. Latour says all this much more clearly (and certainly more wittily) than any review. Read it, but beware; in spite of Latour's strictures about irreducibility, the text is not what it seems. This is a recruitment brochure: Bruno needs you. Among the many historians whom Latour convicts by quotation of mistaking the general for the army, Pasteur for all the forces at work in French society, is Georges Canguilhem. Latour uses two quotes from Canguilhem, both taken from the original French version of Ideology and rationality in the life sciences, first published in 1977. Reading Canguilhem after Latour induces a feeling akin to culture shock. Astonishingly, Canguilhem seems almost Anglo-American. Anyone familiar with Canguilhem's epistemological universe would hardly be surprised to discover that Latour finds in it perspectives different from his own. After all, Canguilhem remains committed to the epistemologically distinct entity science or, better still, sciences. Likewise he employs distinctions between science and ideology, as in Spencerian ideology and Darwinian science, which will seem familiar, possibly jaded to English-reading eyes. His text is liberally seeded with unLatourian expressions, including injunctions to distinguish \"between ideology and science\" (p. 39), lamentations that eighteenth-century medicine \"squandered its energy in the erection of systems\" (p. 53), rejoicing that physiology \"liberated itself' from classical anatomy (p. 54), and regret that \"Stahl's influence ... seriously impeded experimental

1,212 citations