Abstract: Interview analysis is a technique employed in qualitative research. Researchers annotate (code) interview transcriptions, often with the help of Computer-Assisted Qualitative Data Analysis Software (CAQDAS). The tools available today largely replicate the manual process of annotation. In this article, we demonstrate how to use natural language processing (NLP) to increase the reproducibility and traceability of the process of applying codes to text data. We integrated an existing commercial machine–learning (ML) based concept extraction service into an NLP pipeline independent of domain specific rules. We applied our prototype in three qualitative studies to evaluate its capabilities of supporting researchers by providing recommendations consistent with their initial work. Unlike rule based approaches, our process can be applied to interviews from any domain, without additional burden to the researcher for creating a new ruleset. Our work using three example data sets shows that this approach shows promise for a real–life application, but further research is needed.
Reference: Kaufmann, A., Barcomb, A., & Riehle, D. (2020). Supporting Interview Analysis with Autocoding. In Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS 2020), pp. 752-761.
The other day I ran into one of the oldest software engineering tropes in the book: That software engineering should be more like work in a factory, and that developers are best equated to assembly line workers who put together a software product by assembling components to a specification. I wasn’t sure whether I should be amused or irritated. In any case, this nonsensical idea has long been debunked by Peter Naur, before it even took roots in later work by others. In Naur’s words, programming is (best viewed as) theory building, and this gets to the heart of the matter.
The ACM Hypertext 2019 conference will take place in Hof, Germany, on September 17-20, 2019. Here is the conference’s scope in its own words:
The ACM Hypertext conference is a premium venue for high quality peer-reviewed research on hypertext theory, systems and applications. It is concerned with all aspects of modern hypertext research including social media, semantic web, dynamic and computed hypertext and hypermedia as well as narrative systems and applications.
Regular paper submissions are due April 14th, 2019. Please submit plenty.
Software product management by case is a college-level course that I created for teaching product management to computer science students. Using the case method, it helps students understand complex real-life situations in product management as well as the strategies and methods used to deal with them.
Some cases are not about product management, though. An example is our case about stock options. Using the IPO situation of one of the dotcom bubble darlings, Caldera Systems Inc, the case helps students understand employee incentive systems and stock options. We were fortunate enough this time to have Stefan Probst in class, who ran Caldera’s German subsidiary.
Stefan answered students’ questions after we finished the case analysis and shared war stories of the dotcom bubble days. Thank you, Stefan, for teaching us!
At CES 2019, IAV, a German automotive engineering firm, presented the side-window entertainment showcase. In this demo, you can see users interact with the side-window of a car. A camera records the view out of the window, another camera tracks the passenger’s focus, and a transparent OLED display overlaid on top of the window both shows the passenger interesting location-sensitive content as well as interacts with them. Below, please see a demo video and/or read the article (in German). The first version of the software was developed by students of TU Berlin in a 2017/18 AMOS Project.