Saturday, March 9, 2019
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.
Friday, March 8, 2019
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?!
Sunday, December 30, 2018
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.
Sunday, December 9, 2018
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:
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!
Wednesday, May 30, 2018
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?
Saturday, April 14, 2018
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
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.
Saturday, April 16, 2016
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.