Old White Men vs. Young Ambitious Women

Some months ago, I had a strange conversation with a woman a bit younger than me. While we talked about a workshop, she told me she had disliked it as she wasn’t willing to have an “old white man” explain *anything* to her. She used this phrase several times referring to the instructor, addressing herself as “a young, ambitious, but underestimated woman.”

Apparently, as soon as she saw the person giving the workshop she had grown an instant refusal against anything that person would say, as this would in any case be patronizing. Just because that person was a man and visibly older than she. The "white" aspect didn't make any sense here as they both have the same skin color.

But obviously she used the phrase "old white man" as a general fixed expression referring to "the other." And most probably she assumed that I would agree as we have the same age and gender (and skin color). But I'm sorry, my automatic solidarity is very limited and doesn't go along those lines.

I was really upset then and I’m still very annoyed, for two reasons:

First, I had never before heard someone with an academic background (!) explicitly referring to somebody else (not a group, a single person!) by assigning them a label used in a discriminating fashion.

Second, I was too surprised to react properly. I didn’t manage to tell her that what she just did was clearly discriminating -- and she could be sued for doing so.


She didn't discriminate against me but against another person; she hadn't said anything directly to this person. I had walked away and hoped that she would probably react that way directly to someone else one day and then *that person* would speak up. But that’s not the way one stops discrimination, right?

In retrospect, what had happened was abusing feminism, a single person got a label and had been filed under "enemy" on the spot. Any serious discussion of specific issues had been blocked. You don't talk to the enemy and you don't trust what they tell you, no matter what.

Another annoying thought, playing “the feminist card” assumes instant solidarity from other women, maybe even regardless of age. If you don't agree, you are at least suspicious. However, such an atmosphere does not foster any form of discourse, no serious discussion is possible. The only thing left is throwing labels and generic accusations at each other. That's not very grown-up. And it also doesn't help to address and overcome questionable power structures and attitudes.

German universities: #uberized or #unbezahlt? Or both?

On Twitter (mostly in German, though), there are some great discussions these days triggered by sometimes very personal, sometimes very pointed tweets tagged with #unbezahlt ("unpaid") and #unbefristet ("non-permanent") or more recently #FristIstFrust (roughly: "fixed-term is frustration"). Some years ago, the GEW ("Gewerkschaft Erziehung und Wissenschaft," the educators' union) encouraged members and non-members alike to share what they like and don't like in academia using the hashtag #TraumJobWiss (short for "dream job academia"). Most of the tweets are in German and most of them are rather critical if not downright sarcastic. Apparently, the non-professorial teaching and research staff (in German: "Mittelbau") is sick of being offered mostly fixed-term part-time jobs at universities. And "fixed-term" more often than not means contracts for less than two years, or even less than one year!

The hashtag #unbezahlt refers to jobs or tasks that academics do without being properly paid for them, e.g., reviewing, grant proposal writing, talk preparation, student supervision, workshop organization, edition of volumes of scholarly papers, thesis writing, teaching. Wait, but aren't these genuine academic tasks? Why would people not get paid for doing them in the first place, and secondly, why would they do them if they don't get paid (or not get paid properly, as is the case for adjunct lecturers at German universities; have you ever heard of "Titellehre", when you have to teach for not losing your status as "Privatdozent", and as you have to, universities can offer to pay, hm, nothing at all)? The main factor is probably the vague hope to be able to list all these tasks on your CV to be eligible for a professorship one day. Of course, one hast to be qualified, too, but this "only" means writing the so-called "second book" -- all the other things: being an active member of the scientific community, building a network, etc. are no hard conditions, but a widely agreed upon view is that without those you won't have a chance to get a professorship one day. However, due to the very limited number of professorships at German universities, having all this on your CV doesn't mean that you will get one sooner or later. And apparently, people in academia realize this more and more and they get upset more and more. I predict a rather hot academic summer! At least I hope so.

The German academic system (or the German university) is often seen as being rather feudalistic, old-fashioned, and out-dated. Which is also supported by the fact that there has been almost no investment in infrastructure of any kind in the last decades. Which is partly due to the German system of federal vs. state tasks; only recently the ban of cooperation with respect to education has been lifted. So German universities, German acadmia has to move and has to keep up with current developments, with the digital transformation -- people start to leave either the system as such (they rather aim for a job in industry) or they move into other academic systems (Switzerland, Scandinavia, USA, etc.) where they feel more welcome.

The other week I read "Digitale Gefolgschaft. Auf dem Weg in eine Stammesgesellschaft" by the philosopher Christoph Türcke, C.H.Beck Verlag, München 2019 (there is also an interview at Deutschlandfunk Kultur (also in German)). Türcke makes some interesting points and bold claims, but one thing struck me: He writes about how the digital transformation changes the working processes. People working with digitized data on mobile devices are able to work from anywhere at anytime they want. It even changes other fields like taxi driving (Uber) or hotel business (AirBnB). As customer, you just call for a service or a product and it will be delivered. Türcke doesn't mention the term, but "uberization" of whatever industrial field is everywhere. And this is the future, it has started already and it will increase.

However, on page 45 he writes:

"Universitäten sind längst dazu übergegangen, einen großen Teil von Forschung und Lehre auf Lieferbasis erledigen zu lassen. Die Mehrzahl hochqualifizierter Nachwuchswissenschaftler bewegt sich von Forschungsprojekt zu Forschungsprojekt, von Lehrauftrag zu Lehrauftrag, mit geringer Aussicht, daß ihr Engagement irgendwann einmal mit einer der wenigen festen Stellen belohnt wird."

(Translation by deepl.com: "Universities have long since started to have a large part of their research and teaching done on a delivery basis. The majority of highly qualified young scientists move from research project to research project, from teaching position to teaching position, with little prospect that their commitment will be rewarded some day with one of the few permanent positions.")

On the one hand, Türcke states what I just wrote in the beginning: the situation in academia is rather bad, people don't have a realistic long-term perspective. On the other hand, some sentences earlier he characterized the uberized society as the future; so the first sentence I cited could be turned into a rather optimistic picture: German universities are not left behind, they are far ahead! The already started uberizing research as well as teaching! Isn't that wonderful?

If only we could convince the academic staff to let go hoping for a professorship but doing scientific research and teaching in a similar fashion they drive their taxis -- and wasn't that always the fallback plan at least for students in the humanities and arts: to be a taxi driver with a doctorate?!

E-Learning, Digital Learning, Digitally Transformed Learning---Do They All Mean the Same?

Nowadays we often hear that the term "e-learning" is a bit outdated, we should rather speak about "digital learning" or even "digitally transformed learning" when we talk about things like using learning management systems (LMS), students collaborating online, etc. Are these terms interchangeable, then? Does it even mean that in learning we are ahead of other areas which are yet to be digitally transformed---right now or in the near future---as we have been doing e-learning for 15 or 20 years now, or perhaps even since the 1960s (have look at PLATO)?

As for other fields, you could talk about the aspects of digitization, digitalization, and digital transformation of learning, and what they imply when it comes to skills and competences you should have and you could acquire. We will do this in a separate article.

In an attempt to look at similarities with other areas, we could try to define various waves or eras of learning, or even try to define "learning revolutions" in analogy to "industrial revolutions." And then we would arrive at terms like "Learning 4.0" (to have the same version number as in "Industry 4.0," or rather "Learning 3.0," or maybe "Learning 3.11 for Workgroups---OK, just kidding). And then we would need cover terms to name the eras of learning.

When we take the industrial revolutions as tertium comparationis, we have the first revolution with the advent of looms---mechanical work done by hand had been automated on a small scale---and the steam engines---the automation of mechanical work at a larger scale. The second revolution was the advent of electricity. This also involved the introduction of the production of electricity as a utility, as a service. It became possible to produce energy at some place and transfer it over fairly long distances to run machines. It wasn't necessary any longer to produce energy directly in or very close to factories. Ford introduced assembly lines and mechanical work done by hand changed again, workers specialized in specific areas. The third revolution came with the introduction of computers, we got CAD/CAM, industrial robots, etc. In all those processes, the human was the main factor: humans control and regulate, they make decisions with the help of machines.

Now in the fourth revolution we face the merging of real and virtual worlds, we not only interact with machines, we let machines decide and call this digital transformation. Computer programs decide whether or not you are creditworthy---some years ago, the banker would inspect the gathered and aggregated data and then make a decision; now the computer decides on its own based on models it created from relevant and irrelevant data using machine learning. We are close to let machines decide whether or not you are prone to return to your bad habits after rehab or prison (see, e.g., AI Judges and Juries in the December issue of CACM).

Let's look at education, where can we position "e-learning"? With the advent of tele-learning in the 1950s and 1960s, we find some aspects of automating parts of teaching. But even earlier, in the 1920s, we have actual machines: mechanical devices as first introduced by Sidney Pressey to let people answer multiple choice questions. Later Skinner developed them further to provide automatic, immediate, and regular reinforcement, and thus trigger learning. And it could be shown that students actually learned while using those machines. Already then we find the discussion whether or not machines would replace human teachers in the near future.

When we look at developments in the 1970s, with the PLATO systems, we find the same ideas: to provide automatic, immediate, and consistent feedback. That's part of "teaching," though, it doesn't redefine "learning"!

Then, at the start of the 21st century, we integrated computers into teaching and learning. We often talk about "e-learning," but we only rarely talk about "e-teaching." However, even with LMS and all of their still improving (or let's rather say: accumulating) functionalities, we still focus on automatic, immediate, and consistent feedback. That's what all the e-assessment, peer activities, forums, etc. are about. And we all agree that just using your fully-fledged LMS to distribute your PowerPoint slides doesn't qualify to be named "e-learning." But still, we have no actual interaction of human and machine, you just get feedback and then decide what to do next. So "e-learning" in this sense is just a contemporary (as in "use mobile electronic devices") teaching machine.

But wait, we also had intelligent (adaptive) tutoring systems in these first years of e-learning! Actually, those teaching machines by Skinner as well as the PLATO V were also intelligent tutoring systems (ITS)---and they were advertised as such. So also this isn't a brand new idea! For various reasons, these systems weren't successful at the time. But most contemporary e-learning research doesn't refer to those old publications when talking about adaptive systems. But maybe now, given the available computing power, it would be time to revisit those old ideas. If technology (including bandwidth) for distribution and interaction was the bottleneck back then, we may be able to solve those issues now.

There have been various attempts around the start of this century, though, tackling another potential bottleneck: the learner model. Using computers, it was more comfortable to implement and maintain various learner models accounting for different learning paths through the material towards the final goal of acquiring some specific competencies or skills. Attempts like <ML>³, (Multidimensional Learning Objects and Modular Lectures Markup Language) or elml (eLesson Markup Language) aimed at foreseeing learning paths and provide students with the appropriate next steps depending on previous actions and (formative) testing outcomes. It turned out that creating such material was rather challenging and demanding. The same was true for testing formats like SET (Satzergänzungstests), which allow you to answer a question by adding parts of sentence(s), an instantiation of "Reihenerweiterungswahl" (Closed Sequence Selected Extension Items) according to the typology of Rütter (Rütter, T., 1973. Formen der Testaufgabe. Eine Einführung für didaktische Zwecke. C. H. Beck, München.) as we showed in a paper. Even with a rather sophisticated editor, it was a nightmare to produce those tests.

Writing learner models using rules, manually, is probably not working. One simply cannot foresee all possible activities and interests. A truly adaptive intelligent system would need a model covering all those possibilities. For now this seems achievable only by using machine learning. In the way we construct language models we could create learner models to feed into tutoring systems and let the machine decide what experience the learner makes next, what problems the learner should solve next, etc. And there we would have it: the digitally transformed teaching/learning as a blending of real and virtual worlds with the machine not only providing information to support human decision-making but with the machine deciding and interacting with the human. Of course this also raises ethical questions: is it OK to have the computer model you as a learner? But that's along the same lines as in "is it OK to have the computer model you to decide whether or not you will get this credit or whether or not you can get that life insurance?"

As long as we identify "e-learning" with "using the full potential of LMS and (apps on) mobile devices" (does anybody remember "clickers"? You can have them as apps now, yeah!), we don't talk about the digital transformation, but about the electronic re-engineering of teaching machines. But as long as we're just deploying "electronic teaching machines," we should stick with the term e-learning. Oh, and we still have vast communities who use LMS as PDF or PowerPoint distribution vehicles only, there isn't even digitalization involved, only digitization.

Clearly, with all those MOOCs around where you interact with the video and the instructors/tutors, a lot of logging is could be going on. This data will be used to model learners. And as for language models in Natural Language Processing, those models created by machine learning might be not exact but appropriate or good enough for specific tasks. The big issues there revolve around the questions of "which features matter, which features do you use?" The same will be true for learner models or learning models. What we have in e-learning are various models of teaching, and those could be described by manually crafted rules. They are based on hundreds of years of research and developments in didactics and pedagogy (and schools thereof). For determining and weighting features for learning, we shouldn't leave the fields to the usual suspects of Big Data processing. This research and development and thus the digital transformation of learning should be driven by the field of teaching and learning, by the experts involved with didactics and pedagogy.

Twitter #BookChallenge

Some weeks ago, a friend invited me on Twitter to take part in the #BookChallenge. You’ve probably heard about it: people post covers of their favorite books for seven days, one book per day. It runs in various languages and usually includes a statement like “7 days, 7 favorite books, no explanations,” you mention the person who invited you and you might invite or challenge somebody else. A really nice version of a chain letter, I think.

I accepted the challenge by Ruth Mell:

#BookChallenge 7 days, 7 favorite books Challenged by ⁦@KonstanzeMarx 2/7 Ein unterschätztes Buch. Die Charaktere sind einfach großartig! I invite @CerstinMahlow pic.twitter.com/tdT71bgqE0

— Ruth M. Mell (@Ninchen2) 6. November 2018

And I posted 9 books, you can find all of my tweets via Twitter Search.

Wait, why 9? Because I’ve read so many books that are important for me, I didn’t manage to reduce their number to 7 in the end.

I’d like to give some explanations on the books I posted — and on the books I finally didn’t post. I’ll explain them in a different order than I originally posted them, though.

I’ve always loved reading, and as a kid I always had a book with me. When you just start reading, blackletter is quite difficult. So my grandmother read these old children’s books with me: we were both sitting in a really wide armchair, looking at the book, and she was reading aloud. The armchair was placed near the heating and we probably also had some snacks and cocoa or coffee. When we went on vacation, it was a hard fight every time about how many and which books I could take with me — we were traveling by train and somebody had to carry those books in the end. So, of course I wanted to include at least one of the books from these days. I love everything by Benno Pludra (“Bootsmann auf der Scholle,” “Heiner und seine Hähnchen,” etc.), the children’s books by Christoph Hein (e.g., “Das Wildpferd unterm Kachelofen”), Alfred Wellm (e.g., “Das Pferdemädchen”) and Fred Rodrian (e.g., “Das Wolkenschaf,” “Schwalbenchristine”) and so on. Later I read all books about American Indians by Liselotte Welskopf-Henrich (all the volumes of “Die Söhne der Großen Bärin” and “Das Blut des Adlers”) — some of them I received as gifts, some of them my grandfathers had bought for themselves — and of course all books by Karl May one could buy (which was a bit of an issue in the GDR: partly because of the dispute about publication rights and partly because of the low number of books printed in the GDR). But finally, I decided to post “Geschichten aus der Murkelei” by Hans Fallada, with illustrations by Conrad ‘Conny’ Neubauer. Printed in 1960, it’s a book my father got when he was a kid.

It’s a collection of short stories Fallada first told his kids and then later wrote down. The stories contain all kinds of nonsense: there are days turned upside down, caps that make you invisible, and my favorite story “Mäuseken Wackelohr.” This is the story of a little mouse with an ear that’s a bit jagged, and who finally succeeds in getting into the house across the street despite the cat, and with the help of ants (who do everything for candies) and doves.

I also love the radio play from 1980 with all the great actors of the time!

An author I really like is Neal Stephenson. I chose “Reamde,” which is not his latest book but the one I read most recently:

The two books published after “Reamde” are still waiting for me on the “To Read” shelf. When I first discovered Stephenson, I read the novels in their German translations. Starting with the Baroque Cycle, I’ve been reading his books in their original English versions. Maybe I should get the first ones in English, too, as I really like his style. I like him as a writer since he writes (or at least used to write) using Emacs, and I admire how he manages to create futuristic or historical places and scenarios that are totally reasonable and plausible. He also wrote about more technical stuff like “In the Beginning was the Command Line” or “Mother Earth Mother Board.”

The ability to write about futuristic sceneries with a scientific touch — there is no magic, it’s all very plausible, and can be explained by scientific reasoning — is even more prominent in the novels and stories by Stanisław Lem. I think I have all of his books that have been translated into German.

The book I chose was “Gast im Weltraum” (“The Magellanic Cloud,” original Polish title: “Obłok Magellana”):

It’s from the 1950s and this copy was one of my grandfather’s books. I don’t remember when I first read it — my grandfather died when I turned 7 — but I inherited it from him, and so he somehow introduced me to science fiction and cybernetics (OK, that’s stretching it a bit, especially since he was a musician). I recently discovered that the version published at the time had been censored and that the original version was only published in the 1990s — unfortunately it seems that there’s no English or German translation of this version.

Lem published essays and stories covering neural networks, nanotechnology, the Internet, artificial intelligence, etc., long before these things existed, and he predicted and explained everything in a way that lets you read his publications from the 1960s and 1970s just like very recent books on contemporary topics. He continued writing even as an old man, and the essays from the volume “Die Megabit-Bombe” (“The Megabit Bomb,” original: “Bomba megabitowa”) from 1999 discuss technical, and even more importantly, ethical issues that are highly relevant today! So I included this book, too, as a sidekick.

When you discuss the effects and impacts of the digital transformation in the humanities, in science, in politics and society, you should read those essays! It seems that they have been only translated into German and Russian, though.

I discovered Bret Easton Ellis when I was 17 or so. I read the German translation of his book “American Psycho.”

At that time, the book was on the index in Germany (which meant that it was not allowed to be advertised and not to be sold to customers younger than 18), but IIRC my father had received a review copy from a newspaper before the book was put on the index. Both of us liked the book very much — both of us didn’t like my German teacher at school. When one could write a somewhat longer essay to substitute for one of the written exams I decided to write about the “image of the American society in the early 1990s as described in contemporary novels.” I used “American Psycho” and “Leviathan” by Paul Auster. And I included extensive samples from “American Psycho,” so my teacher was forced to read it — and I knew she didn’t like splatter movies and horror scenes. It was a bit mean, I guess. However, this book is also the first novel I read in English — I wanted to read the original version. It was impossible to get in Germany, so I asked one of my schoolmates — the son of the pastor — who was about to go to the US for a summer camp whether he could buy the book for me. And he did; he never commented on it, though :)

I liked Genesis and Phil Collins already before I read the book, but based on Ellis’s extensive reviews, I bought specific albums, first on cassette and later on CD. Much, much later I discovered Ellis’s account on Twitter and followed him. And in summer 2013, he posted a recommendation for a book I only bought because of his recommendation, and I didn’t regret it: “Stoner” by John Williams.

I’ve read “Franziska Linkerhand” by Brigitte Reimann in the new unabridged edition from 1998 with an extensive afterword by Withold Bonner. There was a first edition in 1974 (one year after Reimann had died at the age of 39) which had been censored and edited a lot — and as Brigitte Reimann wrote in her diaries, she herself had censored and changed a lot during writing after “helpful and friendly” exchanges with lectors and other writers. This new edition ends with the unfinished pages and sentences by Reimann: there is no end of the novel. Maybe it’s true that, from a literary point of view, Reimann would have had to revise the whole book to find a proper ending anyway; it’s true that the story somehow gets stuck in the end.

Franziska Linkerhand is a young architect trying to find her way in the 60s in the GDR, in the building sites of one of those cities that were thrown up to house mineworkers. I recommend reading the novel together with Reimann’s diaries “Ich bedaure nichts” (1955 to 1963) and “Alles schmeckt nach Abschied” (1964 to 1970), so you can understand why Franziska acts as she does. And there is also a nice edition of letters she exchanged with Hermann Henselmann (“Mit Respekt und Vergnügen”, published 2001), one of the most influential architects of the GDR (he’s the architect of the Stalinallee boulevard in Berlin, for example), you should read to understand the construction-related political scenes. And in the letters with Christa Wolf from 1964 to 1973 (“Sei gegrüßt und lebe”, published 1993), you can follow how Reimann struggled with writing and finding ways for her protagonist. I read the book again this summer while commuting and I always forgot the time and completely immersed in the book and in the time. I didn’t live during the time Reimann describes, but the cities and streets were close to reality even 20 years later. And also the arguments of politicians and superiors were still the same, as was the killer argument: you surely are in favor of peace (or even world peace!), aren’t you?

“Jahrestage” by Uwe Johnson I read during my last school year. It took some time and I showed up with one of those black books wherever I went, I even read when waiting at the traffic lights. I had read other novels by Johnson before, I liked his style and I could understand the Low German and the Russian snippets he used. And of course I liked and I still like the Baltic Sea, Mecklenburg, and those small towns and villages Johnson describes. He writes in a way the people in the North speak and react: very reduced, a bit uncommunicative.

“100 Eier des Kolumbus” by Dr. Gerhard Niese, printed in 1962, is one of my mother’s children’s books.

It contains magic tricks, mathematical, physical, and chemical experiments or problems, nicely illustrated by Heinz-Karl Bogdanski. I actually used it when working on problems in the math club as the explanations were much better than the ones given by my teachers or in the school books.

“Maus” by Art Spiegelman was the first comic I read. I had never been into comics as a kid, no Digedags, no Abrafaxe, let alone Asterix or Spirou. “Maus“ had been on the index in Germany as well — just because there are swastikas all over, but how would you tell/draw the story of the Holocaust without showing Nazi symbols?

Spiegelman tells about talking with his father — a survivor of the Holocaust — about his experiences and he tries to understand what had happened and why his father is now the way he is. It is very impressive and very depressing. Much later I bought the book where Spiegelman documents the making of “Maus,” also as a comic.

And then there are other books and authors I find important and that I considered — but which eventually didn’t make it into the final nine.

When it comes to children’s books, I also always liked “Lustige Geschichten” (original Russian title: “Забавные истории”) by Wladimir Sutejew.

Especially as he was able (or at least that’s what he told his readers) to draw and write at the same time!

The fairy tales of the Brothers Grimm in an old edition, printed in 1954. Originally my mother received it as a gift when she was a kid.

My grandfather used to do fretwork. He did “Rotkäppchen” (“Little Red Riding Hood“) for me and he used the illustration in the book as a master. It used to hang in my bedroom.

The first North American author I discovered was John Irving in the early 1990s, and the first novel I read was “A Prayer for Owen Meany” (in the German translation published as “Owen Meany”). But as this book isn’t mine but my father’s I couldn’t take a picture of the cover for the challenge.

Over the years, I’ve read more of Irving’s novels, and as for Ellis and Stephenson, I regularly check whether he’s published something new and then go and get it. So there are also some novels of his waiting on my “To Read” shelf.

And then all the great books by Walter Moers!

I also regularly check for new books by him. His novels are full of fantastic adventures, crazy twists, and creative names and descriptions. They’re like fairy tales for grown-ups. And the books are also nicely done when it comes to typography and layout.

An author I only recently discovered is Stefan Heym. I discovered him by chance while diving into literature on architects and architecture in the GDR, and Hermann Henselmann in particular. Heym wrote “The Architects” in the 1960s, and it discusses many of the same things as the movie “Spur der Steine” and Brigitte Reimann’s novel “Franziska Linkerhand,” which were also censored and/or banned in the GDR, so it does not come as a surprise that the German translation was only published in 2000, shortly before he died (the English original was published in 2005).

This got me interested and I began reading more, Heym’s autobiography “Nachruf” and more of his novels. And I reread “Franziska Linkerhand.”

As I strolled by my bookshelf, I discovered many other books I think I should read again. So, thanks for this challenge that got me thinking about my books!

Supervision families

When you do your PhD, you‘re not completely on your own, you have a supervisor. And then maybe a PhD committee and a second and a third reader and so on. However, during the process of developing your research question, diving into literature and possible approaches, making discoveries, and finding a place for your future academic persona, you primarily interact with your PhD supervisor. He or she should act both as a coach and as a mentor to support you on your academic adventures.

Interestingly, in German speaking academia, your supervisor is still rather called „Doktorvater“ (doctoral father) or „Doktormutter“ (doctoral mother). Which implies a more family-like relationship. And which also is in line with the traditional notion of not studying somewhere at a certain university, but to study with someone, i.e., be the (graduate) student of a specific professor. And thus become a member of a specific „school.“ In the old days, the members of an academic family stood together, supported one another, helped with getting promoted, etc. Which is what you would actually also expect from a mentor. So the role somehow fits.

By the way, how are the PhD students called, I‘m not aware of a label as „Doktorkind“ (doctoral child). You are the „Doktorand“ (male) or „Doktorandin“ (female) (doctoral student) of someone. However, this is derived from the present participle of the verb meaning „to do a PhD.“ Which means, after the defense of the thesis, the label doesn‘t fit any longer. You might be a „former PhD student“ of someone, but this person doesn‘t turn into your „former PhD supervisor“ or your „former Doktormutter.“ He or she keeps the label and thus probably also the role, even after dozens of years.

Surprisingly with this family notion, at least on the side of PhD students expectation grows that these ties will last for longer — you cannot get rid of fatherly or motherly duties — and that mentoring or coaching support also will last for longer. So they tend to get disappointed when mentoring-like support stops, no information on (future) projects or even jobs are passed on, no reference letters are written any longer, and so on. Of course, one could argue that during your PhD you should also find your own way, stand on your own feet, and leave your supervision family to start your own. And of course family relationships aren‘t always positive, there is abusive behavior which is hard to report and will stay within the family. And as long as everything looks great from the outside, nobody will believe that the inside isn‘t as bright as current incidents at the ETH show.

Another aspect seems to be gender, actually. And maybe more on the side of the supervisor. Female supervisors (so the „Doktormütter“) seem to be more protective and more supportive, at least they report such actions on social media and they even use selfdescriptions as „mama bear advisor“ and the like. And from what I see (which is obviously a very small snapshot), more female supervisors state how proud they are when their current or former PhD students report success stories (an award, a talk at a prestigious venue, a good job, another grant, etc.). Male supervisors also show success of their PhD students, but with much less emotion, they rather mention this as a success story of their lab/institute/project. Which fits stereotypes of motherly and fatherly support within families, so the German terms actually are apropriate, don‘t you think?

Back where I started

So, we have 2018 and I'm an e-learning specialist at a university of applied sciences in Switzerland.

Last week I found my old business cards when I was looking for something else in my desk drawers. And I realized that exactly 10 years ago, I also had business cards as e-learning consultant of a university of applied sciences in Switzerland — in 2008, I was at the School of Social Work at the University of Applied Sciences Northwestern Switzerland (HSA FHNW), today I'm with the Bern University of Applied Sciences (BFH), at the Competence Center for Higher Ed Didactics and E-Learning. And the fun thing is: exactly 10 years after, I now have a 50% permanent position as I had then, I do similar things — although I'm not on my own as I was 10 years ago but a member of a wonderful team — , and even my salary is the same (yes, I also found old pay slips when looking for documents needed for the 2017 tax declarations).

So, what did I do in between — and was it worth it when I end up almost where I started? Let's look at the business card circle (the current card is at the top, the starting card is the one right to it and the circle goes clockwise).

The e-learning job at HSA FHNW wasn't my first one, but the first outside the University of Zurich (and I even had an e-learning job there from 2004 to 2008). After that I held various positions and did various things as senior researcher at the University of Basel, as acting professor at the University of Konstanz (I blogged about that experience and most of the posts are still quite relevant, I guess), as postdoc and scientific coordinator at the University of Stuttgart, as scientific coordinator and later as senior researcher at the Institut für Deutsche Sprache in Mannheim, as guest professor again at the University of Stuttgart, and as researcher at the University of Bern. All of those activities where related to my other live as computational linguist and writing researcher — I used e-learning activities and tools for my teaching, but I wasn't active in that area during that time.

I had left the e-learning business in 2010 for a chance to pursue my scientific career and to become a professor one day. Turns out, I didn't succeed.

Yes, I published a lot, yes I finished and defended my thesis (so at least I have a different academic title on my current business card), yes I grew an international network, yes I founded two workshop series (both of them have fallen asleep because of decreasing interest), yes I edited various proceedings (oh, and yes I know a lot about publishing and “added values” by publishers now and I do have an expletive explicit opinion about those and their work flows), yes I was what's called “an active member of the scientific community” (I founded and still run a doctoral consortium, I'm co-leader of a SIG, I was a newsletter editor), yes I was active in various scientific areas (computational morphology, writing research, document engineering, corpus linguistics), yes I taught a lot (as acting and as guest professor I did the usual German 9 hours per week (so 3 to 4 courses per term) teaching load and I taught one course per term at the other places), yes I have been acting professor, yes I reviewed for various prestigious conferences and journals — all those activities you find listed as recommended or even necessary on your way towards professorship. But I'm still not a professor.

However, I grew older.

And last year when I got the chance to “go back to e-learning” I decided to no longer actively pursue the scientific career road — my chances will not increase. I will not apply for grants any longer, I will reduce reviewing activities, I might continue publishing but will carefully chose where and how (considering issues of access and submission formats). I hopefully will continue teaching and I hopefully will get chances to do small scale research at the intersection of document engineering, linguistics, and writing research. I will not apply to scientific jobs — my list of rejected or even unanswered applications is long enough now. I'm not sure whether all of this equals “I'm leaving academia”, but time will tell.

So the question remains: was it worth it? I guess so. I got chances to do research, I taught a lot (and I didn't know that I actually enjoy teaching before!), I could present my research at various places I wouldn't have visited otherwise, and last but not least I got to know a lot of people with similar interests, some of whom I call friends today.

COST ELN STSM on multi-word production (3): A first look at the data

For keystroke-logging the writing sessions in our experiment, we use Inputlog. It's developed at the University of Antwerp and free to use for everybody. On the Inputlog website you also find information on how to use it and on how it has been used in other studies.

In the record tab, you enter the information on the participant and the writing session and press "Record". MS Word opens automatically with a fresh new document. The Inputlog window goes to the background and doesn't disturb your writing. When you finish your writing, you bring the Inputlog window back to the front, press the "Stop recording" button and you're done. Inputlog switches to the Analyze tab and lets you select analyzing scripts which you can also modify.

Of course, you could run your own scripts on the recorded file. All keystrokes are stored in an idfx file, which is an XML file containing the participants information as meta data and all information on keys pressed and mouse movements as events. You could load it into an XML Database like BaseX and run XQuery scripts.

So, everything looks as ready for processing. But it's better to look more closely at your data first. The main issue when dealing with non-English language data is always encoding. The snippet shown above actually has Greek letters and it is encoded in UTF-8 (Emacs makes this information explicit at the bottom). Students wrote texts in Greek, all final texts also show Greek letters. So everything should be fine, shouldn't it?

Actually, the information in the idfx file is not taken from MS Word, but directly from the keyboard. So no matter what your setting in MS Word is, the setting for the keyboard in Windows is relevant. And we discovered that for some sessions, this was set to English and not to Greek. Which means that the information in the idfx file are actually ASCII keys pressed -- because of the setting in MS Word, this information is converted into the corresponding Greek characters and the characters in MS Word appear as Greek characters.

The question is: Does this affect the analysis? We could simply replace the English letter with the corresponding Greek letter. There are conversion tables available and even the keyboards are labeled accordingly, so this should be easy. But then, Greek has accented vowels which are not characters of its own, but are constructed similar as you would write them by hand: You put an accent on the vowel. Which means you press the key for the tonos (the key right to P (which would be the "ü" on a German keyboard and the ";" on a US-English keyboard)) and then the vowel. The result is a vowel with tonos, one character only although we pressed two keys. And that's how it is recorded when the keyboard is set to Greek.

However, if the keyboard is still set to English, Inputlog records that two keys have been pressed, the key for the tonos is not treated as a dead key. The following image shows two idfx files

In both cases, we produce the same character, the small letter eta with tonos: ή. In the right file (GR-03_0.idfx), the key for the tonos is pressed (VK_OEM_1) at position 375 as the 491st event. Then the key VK_H for the small eta is pressed as the 492nd event, but we are still at position 375. The tonos key is actually treated as dead key (there is no key value) and after pressing the eta key η, the value is "ή". But if we look at the left file (GR-11_0.idfx), we find the production of ή to be recorded differently: the key for the tonos is pressed as event 38 at position 19 and there actually is a value: ";". Then the key VK_H is pressed as event 39 at position 20 and the value is "h". So the tonos is not treated as a dead key, but as any other key with an actual value. In the idfx file, no accented value is visible, we cannot simply replace ASCII values with the corresponding UTF-8 characters. A more sophisticated recoding would be needed.

Let's see what this means for our analyses in a later post.

COST ELN STSM on multi-word production (2): Data collection

In order to explore how multi-word expressions are produced, we need somebody to write something. We decided to have students come to write short argumentative essays. In those texts, you would expect to find discourse expressions like "in my opinion", or "on the one hand -- on the other hand". This would give us freely produced MWE without explicitly triggering them.

Students come to the lab and first get some information about the experiment on paper. They sign a consensus form and fill in a small questionnaire. The questionnaire asks about their native language and other languages the speak/write, and about their writing (how many fingers do they use, do the look at the screen or at the keyboard, etc.). I also take observational notes and we will later see whether or not their self image is true. They get a topic to write about. First they can plan for 5 minutes and make notes on paper, after that they start writing for 30 to 35 minutes.

Students write a text about one of two topics: "Should students pay tuition for post-graduate studies in Greek Universities?" or " argue in favor or against having the options to be tested in all courses they take at each semester". The target audience are other students, so they write a letter to the editor of an imagined student news paper.

It's a small lab, so we can have four students at a time. However, they drop in from time to time and sometimes there are four, most often there is one writing while we start analyzing the incoming data. I will tell about this in the next post.

All four computers run Windows, but different versions. Ioannis installed Inputlog for keystroke-logging. You start the logging and MS Word is opening. You write as usual, Inputlog does not interfere with MS Word.

According to our plans, we will have around 60 writing sessions with Greek data in the end.

COST ELN STSM on multi-word production (1): The start

At the end of 2014, the COST Action IS1401 Strengthening Europeans' Capabilities by Establishing the European Literacy Network (ELN) started. We will explore how to help people (students, adults, novices and experts, and foreign language learners) to write and read better. You can read about he official statement, goals, and working groups on the COST ELN website.

One instrument in COST actions are STSM (short term scientific missions). Combining my interests in writing processes and multiword expressions (which I follow in the COST action PARSEME (PARSing and Multi-word Expressions) Towards linguistic precision and computational efficiency in natural language processing, I applied for a research adventure with Ioannis Dimakos from the Department of Primary Education of the University of Patras. He heads the Laboratory of Cognitive Analysis of Learning, Language and Dyslexia. Under Constantin Porpodas this lab participated in the COST action A8 "Learning disorders as a a barrier to human development."

For this STSM we work on a multi-lingual study on multi-word expression (MWE) production. A great part of natural language (either spoken or written) consists of MWE (i.e., sequences of words with special meaning and/or syntactic properties). Those units have to be learned, the use and meaning cannot be deduced from a simple combination of the words involved. MWEs are rather fixed units and they are typically stored as complete units in dictionaries. It has been shown widely that knowledge of such units plays a key role in reading and listening. However, there is very little research on the production of multi-word expressions. In a pilot study, I could show that MWEs of various kinds (idiomatic phrases, terminology, grammatical constructions, etc.) are produced with significantly shorter pauses between the words involved than when producing any other sequence of words. This study was based on texts produced by German university students who wrote short argumentative essays, the writing was recorded using Inputlog. It has been shown in great detail that use, structure, and semantics of MWEs are similar across European languages and European cultures. In this STSM we will investigate whether this holds also for the production of MWEs in German and Greek.

So, in the second week of April 2016, I travelled to Patras, found a really nice hotel by the sea with a great view, and we started our small project.

Professor for one year (week 48): Who does profit from MOOCs?

Actually, this blog post was scheduled for the first week of March. However, the topic is still relevant even a few weeks later. Just pretend it's early March 2014 (i.e., cold and rainy) while reading.

During our visit of US higher ed institutions last year, we met James P. Honan from Harvard's Grad School of Education. We discussed various things and also touched e-learning and MOOCs. Honan told us about his experiences as a teacher and consumer of e-learning courses and contents and then some musing started about the underlying principles of MOOCs. I will briefly follow up here.

From a didactically point of view, massive open online courses (MOOCs) are old wine in new skins. I wrote about this part in an earlier post. E-Learning courses hosted on servers of universities started around 2000, and courses supported by current technology are as old as TV. The only new aspect is the "massiveness". At a university, e-learning courses are offered to the students of a particular subject at a certain point of their studies enrolled at that specific university. So there might be several hundreds of students using the materials of a course.

Going "massive" and "open", those courses skip restrictions -- everybody can take part -- but no change in didactics might be involved. Allowing more than only a few hundred users to access the material may involve changes in server architecture, maybe clustering, but not necessarily in the general technology used for user interaction and the like.

However, someone has to run those servers and someone should be paid for maintenance. The first MOOCs were developed from scratch, not just scaled e-learning courses (there will be another post on this aspect, stay tuned) -- maybe the content providers would need some payment, too. But declaring those courses as "open" doesn't only mean everybody may join, but also means nobody should pay anything for taking part. So where should the money come from to pay development and maintenance?

Honan gave a hint when he told about the fear of teaching staff at universities: Attending a course may have two main reasons. People just are curious about a certain topic (a), or people have to acquire certain knowledge (due to job demands or the like) and that involves getting a certificate (b). For a certificate, attendees would have to do some sort of exam. And this exam would have to be assessed and graded by someone. And guess who is qualified for assessing and grading student work? Right, teaching staff.

So while in the early years of e-learning instructors feared to be replaced by machines, the advent of MOOCs makes instructors fear to be used for grading only. And in the end, to be replaced by cheap grading staff -- why should you need highly qualified academics when you can have people trained to grade certain exams only. MOOCs would not result in replacing humans, but in downgrading educators.

At the one hand, this nightmare might not become true to the extend instructors might expect -- similar to the fear of teachers being replaced be educational TV shows or e-learning courses --, but on the other hand, that's probably part of the business model of companies like Coursera, edX, or Udacity. Participation in MOOCs might be free, but to get a certificate you would have to pay -- part of this money might get down to the graders, but most of it will go to the company owners. Those certificates don't have to cost a fortune. Look at prices for apps -- as long as the audience is big enough, small fees are fine.

Of course, with "certificate" a mean any piece of paper stating that you passed the exam of this course. As soon as participants actively demand official certificates of the hosting institutions, e.g., from Stanford or the MIT, another question arises: How much is such a certificate worth? As an on-campus student, you would have to pay a lot of money -- if you would ever get accepted in the first place. However, nobody would pay several thousand dollars for an on-line course offered or developed by Stanford or MIT staff.

So maybe several hundred dollars? But wouldn't that be a hard competition for those Ivy League Universities? If I could get a prestigious certificate without moving to Stanford and without enormous debts, why should I even send an application to Stanford? But here we're already touching another topic.

Professor for one year (week 47): Teaching investment and payoff

This is the 47th post in the series "Professor for one Year."  Initially, I had planned to post something every week.  However, my year is over and I still have some weeks left in the series.  The topics for the missing posts are already planned, so the only thing I need is some time to write ...

Apropos of time:  How much time do you spend on teaching, including preparation, interacting with students, assessment, grading?  As I wrote two weeks ago and also in week 24, teaching did take up a lot of my time.  I argued that the time allocated to teaching -- including preparation and grading -- should be the same as the time students have to invest to take a particular class -- i.e., the ECTS credits should describe the amount of work students and instructors have to invest.  However, for a regular seminar with 9 ECTS credits, this would mean 18 hours per semester week.  So, no more than three courses (54 hours per week) and then you would have to do some of the other work in the non-lecture time aka semester break to stay at least somewhat healthy and within the regulatory framework of labor law (41 hours per week).

Let's have a look at the workload of professors; 39 to 41 hours per week include:

• administrative work (keeping track of all the different contracts for your PhD students and PostDocs, help with finding new researchers, mentoring your PhD students, hold staff meetings, etc.)
• committee activities at your local university (attend faculty meetings, serve on appointment committees, attend senate meetings, etc.)
• committee activities in your scientific community (attend meetings of societies, have some duties there, review for conferences and workshops, review for funding agencies, etc.)
• write grant proposals (you don't get much state or university money for staff)
• teaching
• doing research
• publish about research

There are studies on professoral activities, showing that professors work more than the 40 hours they get paid for, and that they spend only little time on activities one would usually associate with "being a professor" -- teaching and doing research.

Having a social live, too, and assuming that maybe you don't want to work every weekend, but roughly 50 hours per week -- of course, you think about some issues during your non-working time and you have ideas outside you office --, the question remains: which of the activities are really important and where could you spend less time?  You cannot cut on administrative work, but you could try to delegate some tasks.  Most of the committee activities at your university are related to the status of a professor, so no chance of delegating something there.  You can delegate tasks for your scientific community like reviewing conference or workshop papers -- however, as an author, you'd rather want to get feedback from senior researchers, not from PhD students, so this is a bit tricky.  You could hire someone for writing grant proposals and you could even let your PhD students and PostDocs write most of the articles on which you appear as co-author.  Even the research you could delegate to members of your group, at least part of it.  So you are the one who has ideas and then somebody else is experimenting if they are worth to be investigated much deeper -- for computational linguistics, this means that you find someone who does the programming along the lines of your roughly sketched new approach.  So most of the activities could be delegated to other people, and maybe the quality even improves because you profit from including more people and thus more ideas and more skills than one could have oneself.

And for teaching?  Oh, that's easy: You take the slides and exercises you developed years ago (or you even borrowed from somebody) and use them term after term without changes.  You find teaching assistants doing all the tutoring and exercises with students.  You cut short on mentoring: students have to come up with topics for theses themselves, and somehow they should know by then how to write a thesis, don't they?  This way, you can drastically reduce the time spent on teaching.  And to be honest, that's the most obvious way:  I didn't have duties in committees at the university during my year as professor, but even then, I could hardly keep up with my scientific community activities, and I did have absolutely no time to write grant proposals, do research, or even publish.  In other words: I had to invest almost all of my time in teaching and I definitely couldn't afford this for a real professorship.  On the positive side, I now have quite a teaching record, from which I can benefit in the future.  But honestly, I also enjoyed mentoring and advising students even though this takes up a lot of time.  And in the end it's the only way to have someone try an idea and report some results I might be able to use for proposals or further research (eventually resulting in publications, too).

However, having a teaching load as high as 9 SWS must result in reduced teaching effort, and thus in  lower-quality teaching as professors cannot afford investing most of their working hours in teaching. So one solution would be to value teaching more, or to reduce teaching load -- students then could expect good-quality teaching and mentoring.

Professor for one year (week 46): Writing research across borders

After teaching is over, I packed my suitcase and took the TGV to Paris.  I attended the Third Writing Research Across Borders (WRAB) conference which took place at the University of Paris-Ouest Nanterre la Defénse.

WRAB takes place every third year, 2011 it was at the Georg Mason University in Washington, D.C., and 2008 at was hosted by the University of California Santa Barbara.  It is the biggest and most international conference on writing research I'm aware of.  The conferences organized by EARLI's SIG Writing (which take place every other year) are also international (i.e., not only European), but much smaller.

The number of participants, number of submitted and accepted proposals, and the number of concurrent sessions is constantly growing.  They actually had 26 parallel sessions!  It was almost impossible to find out which of the talks/presentations would be the most suitable one depending on your own interests.  There was a bit of Twitter traffic going on, so I could see that related topics would be discussed at various sessions all taking place at the same time.  The program was so dense and there were so many people, I draw the comparison to LREC (the International Conference on Language Resources and Evaluation), taking place every other year.  LREC is growing still and you can be sure to meet almost everybody from the NLP community there.  If LREC is the conference to be for NLP, then WRAB is the conference to be for writing research.

And WRAB shares another not so nice feature with LREC: Although you know that everybody is there (or you even searched the program for the name of some colleagues), you cannot meet someone during a coffee break or over lunch unless you actively make an appointment.  I like small- or mid-sized conferences better.

As I had no time to submit papers to NLP conferences during this year, those writing research conferences (and also conferences/workshops on linguistics) will be the only conferences I actively attend in 2014 -- you only have to submit a very short abstract, not a full paper.  At WRAB, I presented ongoing work on a systematic analysis of complex writing errors.  I argued to go a step further than current error analysis in writing research, NLP, or (second) language acquisition -- we have to consider the process that caused an error when classifying writing errors.  This way, we could on the one hand distinguish competence errors and performance errors and we could on the other hand come up with actual proposals on how to automatically prevent or correct certain types of errors.  Fortunately, I found a possibility to actually publish this -- I will give the details once the publication is a available.

I could meet colleagues from Europe and The Americas, we exchanged ideas and made loose appointments for SIG Writing's Conference on Writing Research in August.  So yes, the conference was successful.  As I already new that I would start at IMS in Stuttgart in April, I could tell people about my new affiliation and I made some loose collaboration and cooperation agreements.  I hope I can actually work on that in Stuttgart.

Professor for one year (week 45): Last week of teaching

Finally it's mid February and the last week of teaching is over.  This semester (Winter term 2013/2014), I taught four courses: on Computational Morphology aka XFST, on Computational Semantics aka Prolog, on Natural Language Processing, and on logging writing processes and exploring those data.

Except for the NLP course, I had to prepare everything from scratch.  There was material I could use and it definitely helped to "borrow" ideas and exercises for the two programming courses, but I spent all time with preparing slides (for all courses) and data (especially for the writing processes course) or assessing student solutions.

I also realized that I have to work on my elocution: When I teach two classes back-to-back, my voice is almost gone at the end of the day although I regularly sip some water.  Of course, one solution would be not to talk that much myself, but to let students contribute more.  However, staying focused for 90 minutes and trying to be louder than the 30 computers plus keyboarding noise and to keep students awake is stress to my voice.

As for the exams, I did different things:

• For the Prolog course, I had an exam similar to the Perl course last semester.  The grade is made up by points earned during the semester by submitting solutions for three exercises and then there is a final exam consisting of a more theoretical part to be answered on paper and a more practical part where students actually program.  I could assess the theoretical part while students worked on the programming tasks, nice multitasking.
• For the XFST course, students earned some points by solving three assignments during the semester, too.  And then they will submit small projects including documentation within 4 weeks.
• For the NLP course, students earned some points during the semester by submitting solutions for three assignments and then I had a classic written exam at the very last session.  Students had to answer one question per topic.  Looks like the handwriting of most students is more or less readable.
• For the writing process course, students had to work on a project during the second half of the semester.  They defined a small research question to investigate in groups of two, recorded a writing session for each person, and then explored the logged data and wrote a small report.  I will report on this experimental didactic setting at the next Conference on Writing Research in August. 

I could time the assessments for the three NLP-related courses so I had to come up with an assignment every week and I had to grade an assignment every week.  I preferred a constant but moderate workload instead of giving assignments to all three courses in the same week.  In every course, students had to read research papers (classics and recent ones) in groups and present the content to their colleagues.  Students also had to relate the paper they presented to the material presented or discussed before and they had to comment on whether or not they agree with the authors based on their prior knowledge and being almost full-fledged and approved linguists themselves (they where all master students except for the XFST course).

So teaching is over, I wait for the project submissions of two courses and I have to assess the written exam for NLP and the Prolog programs.  I hope to submit all grades by the end of March to actually finish all teaching stuff when my appointment in Konstanz ends.

Professor for one year (week 44): Publication speed

When you read a paper about "current research" or "recent findings" of a project, are you sure this project is still ongoing or finished only recently (considering the publication date and the date of reading)?  Are you really reading about current research, about something that the authors are currently working on?

If the paper appears as part of proceedings of a computer science or computational linguistics conference, it is fair to draw this conclusion. A paper published in fall 2005 most probably describes research from late 2004 or early 2005. The proceedings appear at the date of the conference at the very latest. Of course authors have to submit their final papers a few weeks in advance -- publishing with Springer, as we do for the Workshop on Systems and Frameworks for Computational Morphology (SFCM), requires editors to submit everything to eight weeks before the conference date, so authors have to submit their final paper roughly three months before the conference (and thus before the presentation of their work). That's pretty fast for an actually printed publication. It could be even faster for electronic publication only, reducing the time span to maybe one month.

However, if you look into other disciplines, publication speed is much slower. The last project at the University of Basel was somewhat interdisciplinary, involving linguistics and computational linguistics. So we went to conferences/workshops in both fields and we also published in both fields. As there are rarely proceedings for linguistic conferences that actually appear at the date of the conference, we usually submitted a paper to a call after the conference. Mostly, those papers appeared in edited volumes as part of a book series.

Due to different publication speed, the very first article we wrote (corresponding to the very first talk we gave during the project -- there had been talks on the topic before the project started) appeared after the project was finished. So everything we said about how to tackle various challenges and what we would like to achieve was published only when we already had those results. Which in general isn't that big of an issue if we would have published all other papers in a similar way: articles on single aspects of the project or on the outcome would appear later.

However, some of the more technical or NLP-related aspects we published at NLP-related conferences.  So we now have the strange situation, that the somewhat "starting" paper presented at a conference in April 2011 is published much later (mid 2013) than papers on the infrastructure we developed and used (fall 2012). Someone trying to follow the project thus has a hard timer figuring out what to conclude from which publication.

Late publications are often due to slow processes during submission (extension of deadlines on request of other authors), during review, during revision and resubmission, during editing, and then during actual printing or putting it online.  Together with Robert Dale I wrote a handbook chapter which finally appeared now, in February 2014.  The whole book project started in early 2011 (probably even earlier as there probably had been negotiations with the publisher first).  We submitted our chapter on time, received two reviews and submitted a revised version (i.e., the final version!) in October 2012.  And now, one and a half years later, we finally got the printed book.  In the meantime I changed universities twice (from Basel to Konstanz to Stuttgart).  So I had to report a change in affiliation, author bio, e-mail and postal address twice.  If I wouldn't have reported those changes, the editors wouldn't have been able to reach me to ask me look at the galley proofs. (In the end, the publisher send the book to a totally strange address I never reported, anyway ...)

For most of the delays in various processes, explanations can be found.  Sometimes someone gets sick, but most of the time it is due to poor production processes.  Submitting articles in MS word format with graphics and tables as separate files forces the editor/copy editor to spend a lot of time actually producing appropriately running text including figures and tables.  Marking keywords manually on paper slows down indexing extremely.  And so it goes on and on.

Given the electronic tools we have today in document processing and document engineering, there is no real reason for slow publication speed.  Apart from the discussion on open access and how authors can make an impact by preferring open access publishers, authors can influence the speed of publication by choosing publishers or publishing methods with reasonable processing speed.

Professor for one year (week 43): Will there be research after your PhD?

This week, the new Rektor of the University of Zurich started his job.  There were several articles and interviews in various newspapers, where he announced what he wants to do next and what his long-term goals are.  In one article in the NZZ am Sonntag, one sentence struck me: "Wirklich geforscht habe er zuletzt als Doktorand" (The last time he did serious research was as a doctoral student).  And he apparently regrets that.

This statement made me wonder: What's your task as postdoc or as professor? When it's not research,  is it about writing grant and project proposals for other people only?  Is it mainly about teaching (looking at the workload, you could think so, I will comment on that in a later post)?  Or are your days filled with more administrative stuff, the higher your professional rank is?

There are several indicators that in fact, postdocs and professors acquire the money to then hire some doctoral student(s) to carry out the research the applicants have a genuine interest in.  For example, with the Swiss National Science Foundation, postdocs cannot submit proposals where they actually would carry out the research themselves (except for the Ambizione program, but that has specific requirements and is part of the career track, not of the project track).  You can submit a proposal and then hire someone -- but the proposal will be evaluated against the applicant's research profile.  The  DFG (German Research Foundation) recently introduced an instrument where you can apply for a grant for your own position as postdoc.  So this looks a bit better.

On the other hand: As a doctoral student, you are not eligible to submit proposals, so you have to find a postdoc or a professor who submits a project proposal you can then carry out.

Given that proposal writing is a serious but tedious task, there is of course less time to actually do some research.  Some universities in Germany have decided that some professors (rank W1 and sometimes even rank W2) cannot negotiate about academic personnel -- there simply will be no academic personnel, you have to write proposals to hopefully acquire third-party money to hire a teaching assistant or a doctoral student.  Isn't that weird?  Public universities are funded by tax payers, so shouldn't that cover all costs to run a university including all personnel needed?  Third-party funding today makes up almost a quarter of the budget of German universities.  On the one hand, it's a good sign: researchers find people who think the proposed research is worth funding it.  But it's also a bad sign: The state is only able (or willing) to fund three quarters of universities' budgets.  But that's more of a political discussion, I think.

However, there seems to be the general perception, that after your doctoral studies, there will be no time for serious research.  I recently heard the conversation of two PhD students: A just submitted his thesis and told B that he would have liked to investigate a slightly different topic, but his supervisor told him not to do so because of the risk of failure -- his research could have produced negative results.  They both agreed that this would have been more interesting and even more fun than to do something one could somehow even predict the results.  And then B concluded: "Only doctoral students do real science, so why don't they let us do risky things?  After your dissertation, you will not be able to really investigate something anymore."  Isn't that weird?  It seems to be widely accepted that your scientific life won't include research after you defended your dissertation; at the same time, it is assumed that you can only do "real" research if you've obtained a PhD, i.e., submit research proposals ...

I really have some research interests in computational morphology, computational phraseology, and writing technology where I would appreciate the help of master students or doctoral students, but where I would also like to explore some things myself -- even if this includes tedious annotation or hacking.  That's fun and only this way you can really discover something new.  I definitely aim to facilitate research by coordinating and managing projects, but I would still like to be part of the actual investigation.