[ATTW-L] December 2018 Issue of IEEE Transactions on Professional Communication

Thu Dec 6 20:18:14 UTC 2018

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Volume 61, Number 4, December 2018

Guest Editorial:
Introduction to the Special Issue: Data-Driven Approaches to Research and Teaching in Professional and Technical Communication<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fbmsmail2.ieee.org%3A80%2Fctd%2Flu%3FRID%3D1-5USWORY%26CON%3D1-1H2-1085%26PRO%3D%26AID%3D%26OID%3D1-5UJUDK5%26CID%3D1-5UJS0XU%26COID%3D1-5UJV85O%26T%3Dhttps%253a%252f%252fieeexplore.ieee.org%252fdocument%252f8482472%26Z%3Daa3465624f2c2939d7547bbe1c89a688%26TN%3Dieeexplore.ieee.org%252fdocument%252f8482472%26RT%3DClicked%2BOn%2BURL&data=02%7C01%7Chayhoe_g%40mercer.edu%7C5d75dada6e3345ea156f08d65bb20143%7C4fb34d2889b247109bcc30824d17fc30%7C0%7C0%7C636797217425559051&sdata=VzjQTrwxeJaNFChQX5UHqEF2RlOsERdvXlfWAEdjIY8%3D&reserved=0>
by R. K. Boettger and S. Ishizaki

This issue extends the work of Thomas Orr's 2006 special issue by examining some current data-driven approaches to the study of technical and professional communication. The articles in the present issue explore the nuances of professional communication using computational tools by embracing the interdisciplinary approach known as data science.

Research Articles:
Locating and Describing the Work of Technical Communication in an Online User Network<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fbmsmail2.ieee.org%3A80%2Fctd%2Flu%3FRID%3D1-5USWORY%26CON%3D1-1H2-1085%26PRO%3D%26AID%3D%26OID%3D1-5UJUDK5%26CID%3D1-5UJS0XU%26COID%3D1-5UJV85O%26T%3Dhttps%253a%252f%252fieeexplore.ieee.org%252fdocument%252f8491345%26Z%3D2b79219eb4329b8df56f35c46713ee8%26TN%3Dieeexplore.ieee.org%252fdocument%252f8491345%26RT%3DClicked%2BOn%2BURL&data=02%7C01%7Chayhoe_g%40mercer.edu%7C5d75dada6e3345ea156f08d65bb20143%7C4fb34d2889b247109bcc30824d17fc30%7C0%7C0%7C636797217425559051&sdata=MhW%2B1u%2FUeQhWHEaXuk3iYllINWS40qsW%2FjHEpWUYCnY%3D&reserved=0>
by J. Swarts

This study examines a user network for a software product and asks how we can study such networks, with the aim of identifying important people, practices, and relationships associated with the technical communication practiced in those settings. Social network analysis is used to visualize the network's structural properties to identify central figures and their relationships to others. Verbal data analysis techniques are used to find themes in their contributions. People central to the structure of online interaction engage users in exchanges of information and influence user practices. They also link users and developers. Broadly, their exchanges are a kind of distributed technical communication.
Exploring an Ethnography-Based Knowledge Network Model for Professional Communication Analysis of Knowledge Integration<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fbmsmail2.ieee.org%3A80%2Fctd%2Flu%3FRID%3D1-5USWORY%26CON%3D1-1H2-1085%26PRO%3D%26AID%3D%26OID%3D1-5UJUDK5%26CID%3D1-5UJS0XU%26COID%3D1-5UJV85O%26T%3Dhttps%253a%252f%252fieeexplore.ieee.org%252fdocument%252f8492458%26Z%3Dd5de68cab2f1c21dc4665a1c5bb3ce%26TN%3Dieeexplore.ieee.org%252fdocument%252f8492458%26RT%3DClicked%2BOn%2BURL&data=02%7C01%7Chayhoe_g%40mercer.edu%7C5d75dada6e3345ea156f08d65bb20143%7C4fb34d2889b247109bcc30824d17fc30%7C0%7C0%7C636797217425569051&sdata=%2BLadw%2Bx4wsHCV1PYY%2Fje%2F4HISj4I7HY7L2qwZTDXUUY%3D&reserved=0>
by M. A. Hannah and M. Simeone

This article explores how professional communicators can use the distribution of knowledge on teams as a way to plan interventions in the work of teams, and what kinds of insights networks of specific knowledge areas offer about team communication challenges. The authors conducted an ethnography of a scientific team and used observations to create a survey of terms that measured understanding of key concepts. They then used the results to produce a network model of agents and terms. The model demonstrated that collaboration broke into two distinct groupings. Ego networks extracted from this parent network showed that concepts commonly well-understood in the team join multiple subgroups of expert knowledge.
Hand Collecting and Coding vs. Data-Driven Methods in Technical and Professional Communication Research<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fbmsmail2.ieee.org%3A80%2Fctd%2Flu%3FRID%3D1-5USWORY%26CON%3D1-1H2-1085%26PRO%3D%26AID%3D%26OID%3D1-5UJUDK5%26CID%3D1-5UJS0XU%26COID%3D1-5UJV85O%26T%3Dhttps%253a%252f%252fieeexplore.ieee.org%252fdocument%252f8490223%26Z%3D5bf7f41af3cc35774c6dd4abe501b54%26TN%3Dieeexplore.ieee.org%252fdocument%252f8490223%26RT%3DClicked%2BOn%2BURL&data=02%7C01%7Chayhoe_g%40mercer.edu%7C5d75dada6e3345ea156f08d65bb20143%7C4fb34d2889b247109bcc30824d17fc30%7C0%7C0%7C636797217425569051&sdata=xMYf2CNmO00pw%2BKoFglV%2FqvWAjmpNhEQ8YfGOwz84ZM%3D&reserved=0>
by C. Lauer, E. Brumberger, and A. Beveridge

This study investigates to what degree hand collection results from qualitative research can be replicated by automated data collection, to what degree hand-coded results can be replicated by machine coding, and what the affordances and limitations of each method are. Using an existing hand-collected and hand-coded dataset, the authors found that web scraping had the obvious advantage of speed. Machine coding provided comparable outputs to hand coding for certain types of data; for more nuanced and verbally complex data, machine coding was less useful and less reliable. Ultimately, an approach that combines hand coding and automated coding should prove to be the most productive.

Teaching Case:
More Than a Feeling: Applying a Data-Driven Framework in the Technical and Professional Communication Team Project<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fbmsmail2.ieee.org%3A80%2Fctd%2Flu%3FRID%3D1-5USWORY%26CON%3D1-1H2-1085%26PRO%3D%26AID%3D%26OID%3D1-5UJUDK5%26CID%3D1-5UJS0XU%26COID%3D1-5UJV85O%26T%3Dhttps%253a%252f%252fieeexplore.ieee.org%252fdocument%252f8490738%26Z%3D908f3fb894ac85cbb4d50552dab3b9a%26TN%3Dieeexplore.ieee.org%252fdocument%252f8490738%26RT%3DClicked%2BOn%2BURL&data=02%7C01%7Chayhoe_g%40mercer.edu%7C5d75dada6e3345ea156f08d65bb20143%7C4fb34d2889b247109bcc30824d17fc30%7C0%7C0%7C636797217425579065&sdata=x%2F30c%2Ff3aheIx%2Bd%2FMQzOHvyo9Icl7Zk800aVNPnjrjM%3D&reserved=0>
by C. Lam

Student group projects present unique challenges, such as unequal distribution of work, unequal levels of learning, and perceptions of fairness. This case describes the implementation and outcomes of a data-driven framework for decision making. After learning about the framework, students completed a series of data-driven exercises during the three stages of the team project. Perceptions of the framework's effectiveness were collected after the project ended. Students realized many benefits from the framework, and some team leaders felt empowered in certain instances by appealing to data. However, instructors should consider scaffolding data literacy and teamwork skills to fully prepare students for successful teamwork.

Tutorial:
Introducing Fireant: A Freeware, Multiplatform Social Media Data-Analysis Tool<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fbmsmail2.ieee.org%3A80%2Fctd%2Flu%3FRID%3D1-5USWORY%26CON%3D1-1H2-1085%26PRO%3D%26AID%3D%26OID%3D1-5UJUDK5%26CID%3D1-5UJS0XU%26COID%3D1-5UJV85O%26T%3Dhttps%253a%252f%252fieeexplore.ieee.org%252fdocument%252f8490230%26Z%3D5dec93fa08366709f1a7ee5d0596f56%26TN%3Dieeexplore.ieee.org%252fdocument%252f8490230%26RT%3DClicked%2BOn%2BURL&data=02%7C01%7Chayhoe_g%40mercer.edu%7C5d75dada6e3345ea156f08d65bb20143%7C4fb34d2889b247109bcc30824d17fc30%7C0%7C0%7C636797217425579065&sdata=ciOk7d0sTZD64U6y8U5Vi4vsKBH8%2BeyDNkOR2byqjvc%3D&reserved=0>
by L. Anthony

This paper introduces a new social media data analysis tool called FireAnt that allows technical and professional communication researchers, instructors, and students to easily collect, analyze, and visualize social media communication. FireAnt has applications in various technical and professional communication settings, for example, in developing a social media presence and in analyzing company-customer interactions and user perceptions of goods and services. It can also be used in the technical writing classroom as part of instructor- and student-centered instruction on the safe, appropriate, and effective use of language on social media platforms.

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