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.

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 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.

Professor for one year (week 35): Should everybody know how to program?

This post is the report, I wrote for the GPP 2013.

Introduction

Being a computational linguist, I was trained in programming as well as in linguistics.  After school in the mid-nineties, I couldn't decide whether to focus on linguistics (or nowadays "humanities") or computer science/math (or nowadays "STEM").  So I was quite happy to be able to focus on both when studying computational linguistics.  I always loved algorithms, abstraction -- and yes, I also loved Latin.  Maybe that's a rare combination, but in today's world it turns out to be quite handy.

One aspect of the 2013 GPP motto "University and Society -- Meeting Expectations?" is the aspect of university as the institution to prepare students to be successfull in today's society.  In the last decade, we saw the emergence of more and more electronic devices, "digital" is one of the buzzwords in several scientific fields, technology becomes pervasive.  We speak of the "Generation Y" as being "digital natives."  However, if we look how today's students use technology, they are only users, they are not creators.  They often even don't know how to configure programs.

Douglas Rushkoff in his book Program or be Programmed argues that everybody should know how to program to understand today's technology and to be able to control it instead of becoming a slave of the electronic devices surrounding us.  So my personal focus in the GPP 2013 was to explore how universities support or enable learning to program.  Of course students in computer science (CS) and related fields (like computational linguistics) are trained in programming, but I was interested in courses for non-CS students.

Answers from US professors

During our visit in the US, I asked my question at two places explicitely and I got two different answers. 

At North Eastern University, Dr. Neenah Estrella-Luna, an assistant academic specialist,  as she described herself, argued that indeed, computer literacy would be a valuable topic to teach considering that university should empower students to deal with current challenges.  However, she admitted that there are no courses offered to all students, not to mention being required.  My question was understood as asking about "teaching students how to program." 

At swissnex in Boston, we met Dr. James Honan, senior lecturer at the John F. Kennedy School of Government at Harvard.  He understood my question differently and answered that students would keep faculty busy and push them to use more technology.  He talked about MOOCs before and probably this influenced his answer.  However, his statement made clear that there is a view of "computer literacy" as "being able to use devices", including the expectation that instructors offer digital content and e-learning material.

At this moment, I was a bit disappointed.  Either the necessity of teaching and learning how to program is not recognized, or, when it is recognized, it is impossible to offer such courses for all students.

While at the MIT, we visited the Media Lab and the "Lifelong Kindergarten" headed by Professor Mitchel Resnick.  We got an introduction into scratch, the programming language and online community intended to teach kids how to program using a game concept.  They learn abstraction, algorithmics, and data structures while they play with code snippets, interact with other kids around the world, and program their own games and worlds.  It's an advanced model of learning the concept of recursion while playing "Towers of Hanoi."  I was aware of scratch before and I really enjoyed seeing some demos and talking to the researchers involved in designing and implementing scratch.  I think using games as a vehicle for teaching important concepts is a good strategy -- the users aren't probably not even aware that they acquire valuable knowledge they will use later in school, in university, and in their jobs.

Situation in Switzerland

On the morning of the day I took my flight to Boston, I took part in a meeting of an experts panel on CS competencies of the Hasler Foundation in Berne.  The foundation is working towards a proposal for a general subject "Computer Science" at Swiss schools.  Currently, some schools in some cantons offer CS as supplementary subject (in German: Ergänzungsfach).  However, this subject is often taught as it was in the 1990s: students learn how to use certain software, they don't learn to program, they don't learn about abstraction, algorithms, and data structures.  In the publication "informatik@gymnasium", published by the Hasler foundation through NZZ Libro (note that the German version of this book is already sold out!), the authors argue that CS and using software are two differnt things and that school should teach students the basics of CS to prepare future citizens to cope with everyday life.  It is probably a long way to achieve this goal, but it's a goal worth all the effort.

However, here we talk about serious teaching, not about fun instruction as in the case of scratch.

Answers from the Web

After coming home, I searched the web for comments about computer literacy and opinions or activities on teaching programming.  Bill Gates, in a questions session at Microsoft's Faculty Summit, confirmed that there is indeed a "gap between how computer scientists use computers to automate their lives and how most people don't really know how to use them effectively."

Larry Hardesty talks about the "programmable world" that surrounds us and that will change the world as we know it by making the distinctions between virtual and physical objects obsolete.  To make good use of the new world, we should be able to understand opportunities and challenges (and issues) and how to manage them.

In England, efforts are on their way to teach algortithms to primary school kids.  The government acknowledges the need to "catch up with the world's best education systems."  However, this new curriculum is still under development and the teacher's union isn't sure about when would be a good starting point to introduce it -- they object to only react to governmental decisions.  According to Sean Coughlan it will include computing defined as:

Computing will teach pupils how to write code. Pupils aged five to seven will be expected to "understand what algorithms are" and to "create and debug simple programs". By the age of 11, pupils will have to "design, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems".

It would be great if England could manage to design and actually implement this aspect.

And there are discussions going on in the emerging field of "Digital Humanities": In a twitter post, Jan Hecker-Stampehl (@heckerstampehl) asks "Should humanities scholars learn to program or trust that the programmers in DH projects will understand them?", obviously not aware of the more than 30-year old answer, Jacques Froger gave 1970 (Froger, J. (1970). La critique des textes et l'ordinateur. Vigiliae Christianae 24 (3), 210-217.), as Michael Piotrowski responds:

Il n'est pas indispensable que le philologue établisse lui-même le programme, encore que ce soit infiniment souhaitable ; il devrait au moins connaître assez le langage de programmation pour contrôler le travail du technicien ; en effet, l'expérience m'a appris qu'il ne faut pas s'en remettre les yeux fermés aux électroniciens, mal préparés par leur formation mathématique à se faire une idée juste de problèmes concrets qui se posent dans la domaine de la philologie.

(English: It is not absolutely necessary that the philologist writes the program himself, even though it would be extremely desirable; but he must at least know the programming language, so that he is able to check the work of the technician; in fact, experience has taught me that one should not blindly rely on the electronics people, whose mathematical training has hardly prepared them for fully understanding the concrete problems encountered in the domain of philology. (translation by Piotrowski))

However, even in fields where you would expect learning to program to be part of the curriculum, it is rather rare, as the blog post by Philip Guo shows.  He argues: "If you're a scientist or engineer, programming can enable you to work 10 to 100 times faster and to come up with more creative solutions than your colleagues who don't know how to program."  Students would need more concrete motivation than only arguing that programming helps them become an empowered citizen (the argument Estrella-Luna used at North Eastern).  Guo accepts that programming tools, i.e., text editors, should be improved to foster programming, but in the meantime we should focus on teaching students programming skills to support creative problem solving.

Selena Larson emphasizes the need to teach programming to students in schools already.  She supports the Hour of Code initiative during Computer Education Week 2013, following a similar strategy as scratch: Using games and fun figures, kids should understand basic principles and get an idea about what it means to program.

Conclusion

Studies by professional assocations like the ACM (Association of Computing Machinery) regularly show an increasing number of jobs requiring programming knowledge.  They also show that there is a lack of people with appropriate skills meeting these requirements.  So there is an urgent need in society. 

As I agree that school would be an appropriate place to start teaching basic concepts of CS, university should be the place to empower students to actually program.  Maybe learning to program, acquiring knowledge about algorithms and data structures should be a required course in every curriculum.  I strongly support the statement made by Steve Jobs in an interview in 1995 saying "It teaches you how to think.  I view computer science as a liberal art.  It should be something that everybody learns."

However, we are still on the way to implementing those ideas into education, be it in school or at university.  If we have the chance to support initiatives like the Hour of Code or panels and experts groups designing curricula, those of us having the respective knowledge, should take part and see this as opportunity to serve society.

Professor for one year (week 33): Coding is the new Latin

A few days ago, I tweeted this:

With a link to a short statement by Steve Jobs.  Two sentences in his statement triggered this tweet:

• "It teaches you how to think." (with "it" referring to learning how to program)
• "I view computer science as a liberal art."

And I added a follow up tweet stating that Latin and Algorithms and Data Structures should be required for all studies at a University.

Initially, both tweets are an reaction to the initiative Hour of Code, which is simply great.

However, the more I think about it -- and look at my Twitter timeline following Digital Humanities conferences over the last weeks --, the more I believe that coding actually IS the new Latin already;  it just hasn't hit universities while Latin is on it's way out of universities (fewer and fewer study programs require proper knowledge of Latin).

When you look for arguments supporting Latin for everybody, you find:

• It's fun.
• It's the basis of European languages, you learn other foreign languages much easier if you know Latin.
• It's the basis of European languages, you gain competencies in your native language.
• It teaches you how to think.
• You learn a lot about logic and abstraction.
• There's no better way to learn grammar.
• It's part of our (European) cultural heritage.
• It teaches you how the world works today.

If you look closely, a lot of those arguments are valid for programming, too:

• It's fun.
• It teaches you how the world works today.
• It teaches you how to think.
• You learn a lot about logic and abstraction.

And that's basically, what Steve Jobs was referring to.  And I agree, although there is a learning curve, you can do a lot with programming, even as a beginner.  It is a kind of craft, you have to practise a lot; but it's also a kind of art, you can enjoy it while doing it and you can enjoy the final product.

Usually, it's "critical thinking" what we want to trigger in students, but I think "being able to abstract" is a more appropriate goal.  And probably a prerequisite to critical thinking -- you have to discover large lines of arguments, you have to abstract from singular examples, you should see commonalities and general discrepancies before making your own arguments and expressing pros and cons.  Being trained in logical thinking and abstraction helps you to do so.  And there is no better way to learn those two than via learning Latin, math, or programming. 

Actually, there might be another link between Latin and programming -- it's a kind of closed community of those who know it.  Therefore a programming language could be seen as a kind of "code" -- i.e., a "cipher" --, and you have to be an insider to decipher it and grasp the true meaning.  It's a kind of jargon spoken and understand in a closed group.  Maybe people referring to "programming" as "coding" are not aware of the ambiguity of "code," but I think the sense of "speaking an encrypted language" is a valuable one.

There's already a lot of research literature with respect to "code literacy," also emphasizing that you should learn how to program to become part of "the club of the privy."  And of course discourse following the book Program or be Programmed by Douglas Rushkoff supports this view.  In this regard, the term "hacker" -- often used as a derogatory term by society, but as a badge of honor for expert programmers in hacker culture (see the definition of "hacker" in the Jargon File) -- could even become a synonym for "member of the new elite."

Professor for one year (week 25): Dress properly

When you look for advice on how to dress professionally, you always hear: "Dress appropriately," and "You should feel comfortable with what you wear."  OK, the second part is easy: It depends on the season how many pieces you wear, and maybe after Christmas-New-Year-holidays you'll have to adjust your clothes a bit.  But "appropriately"?

When you have a job interview, this is a critical question.  For people in medicine or law it's easy, you wear a suit as a man and a pantsuit or deux-pièces (skirt suit) as a woman.  You want to look like "one of them," you want to look like the other professors.  So what do professors in Computational Linguistics look like?

The old men wearing three-piece suits are retiring these days.

When do you see professors wearing formal clothes?  Maybe on conferences.  Only a few men wear suits regularly, most wear pants and coats, some even wear jeans and pullovers or fleece jackets.  Women wear pants and coats, some even wear jeans and fleece jackets (oh, no gender difference?  OK, some women also wear skirts or dresses, but I'm not into skirts.)  So maybe for very official occasions, I get a bit more formal with pants and a blazer.

But what do you wear on a day-to-day basis?  You won't look overdressed and feel comfortable at the same time, so jeans and fleece jacket would be OK?  But then you make experiences like I did on my first days in Konstanz, people treat you like another student.

How could I show that I'm a bit more mature than my students?  Maybe getting grey hair would help.  But that's not the best option, you simply feel old -- not mature or professional -- discovering the first grey hair in the mirror.  As a man, I could grow a full beard.  But wait, I already had bachelor students with full beard -- so no significant difference again.

I guess I'll have to live with being mistaken for a student from time to time, a deux-pièces simply doesn't go with my EDC -- Kitchensink and Motörizers.

Professor for one year (week 18): Don't call us, we call you

Over the last months, I've applied for several positions as assistant professor or research group leader at different universities in different countries.  All applications were rejected.

No, wait, that's not quite true:  

My application at UT Austin, TX, was rejected three days after submission -- that was fast!  

I applied for two positions at Saarland University, the rejection for the second application was sent out two weeks after submission; for the first application, I asked about the procedure three months after submission -- the answer was something like "Oh, we did send you the rejection some weeks ago, you should have received it."  No, I hadn't.

I applied for a position at the University of Tübingen, they sent a detailed schedule when to expect which action and which week was planned for interviews.  When I hadn't received any information the Friday before this week, I called and was told: "Oh, we sent out the invitations three weeks ago, if you haven't received one, your application probably has been rejected."  Aha.

I applied for a position at the University of Zurich.  Two weeks after submission, I saw the list of invited candidates to give a public talk in a newsletter.  No, I hadn't been informed officially that I was not on this list -- I could see it in the newletter, after all.  When I asked about the formal rejection, the contact person appologized and made clear that she hadn't liked this procedure anyway, but the professors in the commission had told her to not send rejections to the non-invited applicants and that she was not aware that I subscribed to the newsletter.

I applied for a position at the University of Amsterdam.  Half a year later I asked about the process and got a message saying: "Oh, we did send you the rejection some months ago, you should have received it."  No, I hadn't.

I applied for a position at the University of Bern.  Some months after submission, I got an invitation to send my best publications.  Some weeks later there was a message that the whole process was on hold and that I would be informed about the next steps -- in the meantime, I could see on their website that I was not on the list of invited candidates for a talk that already had taken place.  No message came, but there is now a welcome note for the new professor on the institute's website.  When I asked about the formal rejection, they appologized and admitted that it was a bad idea to not send rejections to the non-invited applicants.

I don't get it: in the job descriptions for these positions, they ask for excellent people, they look for the elite.  And then they treat you like a beggar.

And of course all the rejections had nothing to do with me as person and I would be perfectly qualified for these positions and they wish me all the best for further applications.

Addendum September 30: Oh, and I almost forgot that I applied at the TU Braunschweig -- no comment until today, but their website lists the invited talks to be given by August 26 and 27 this year (i.e., one month ago). Thank you very much for not informing me!

Professor for one year (week 16): What is excellence?

After one semester of teaching at the University of Konstanz, it's time for some retrospection.  Konstanz is one of those "excellent" German universities -- which also means they have a bit more money than non-excellent universities.  (By the way, how are these universities labeled: "ordinary", "miserable", "amateurish"?)

So far, I spent several years at the University of Zurich (UZH), at the University of Basel (I taught at both), and at the University of Applied Sciences Northwestern Switzerland (although I didn't teach there).  As researcher and lecturer, I had contact to departments concerned with study affairs and teaching in the broadest sense (e.g., teaching evaluation, examination authorities, e-learning consultants), to people concerned with facilitating research activities (e.g., publication database managers, third-party funding offices), and to the general administration (e.g., human resources, secretaries, and the "Abwart" or "Hausmeister" [caretaker] -- nowadays called "facility manager" in German).

For general issues like getting keys and a badge, German bureaucracy meets all the known prejudices perfectly.  You have to go from pillar to post just to realize that today nothing will happen, you will not even get the form you have to fill before being eligible to get a key.  The scene where Asterix and Obelix have to get permit A38 is a perfect match.  You have to find out by yourself about all the things you might need.  I was told, I even had to fill a form and get the signature of the department confirming that they would pay the costs for a -- trash bin!  I didn't even try to get a pencil, yet -- I rather bought one myself.  Whereas at Swiss institutions (not only in higher education) everything is prepared beforehand and you will get an invitation when to come where and collect your keys and your badge.  You are taken care of.  You just go and grab a new pencil.

At all Swiss institutions I worked at, offices were cleaned twice a week, restrooms were cleaned twice a day (around noon and in the evening, so when you come the next day, everything is clean).  In Konstanz, offices are cleaned twice a month, restrooms once a day (around 16:00; of course, people use them after this, so when you come the next day, everything looks messy again).

Swiss offices and lecture halls are equipped with high-value furniture from USM or Vitra, and desks and chairs are renewed regularly.  There is a design concept defining which furniture goes in which kind of working space (there are different chairs for offices, lecture halls, cafeterias, and waiting areas).  It gives a good atmosphere, you feel valued.  Someone invested in getting you a great office to create a positive and creative working ambiance.  Chairs meet ergonomic standards.  Oh, and you just order a new shelf, another chair for visitors, or a desk lamp by e-mail or online -- some days later you find it in your office.  In Konstanz, desks and chairs look very used and accidentally assembled.

I was lucky that someone wanted to get rid of an old orange desk lamp with a proper light bulb and I could use it; I don't like neon light coming from the ceiling.  And yes, this red chair is a victim of the ravages of time.

The only design furniture I am aware of are original Eames Side Chairs on stretchers in front of some professors' offices (a kind of waiting area for consulting hours). 

But this is probably due to the time the university was build: In the 1960s, the Eames furniture served -- as intended -- as mass produced chairs; you can still find the fiber chairs in older stadiums, concert halls, or universities.  Today's Eames chairs are rather expensive (at the University of Freiburg im Breisgau, several dozens of original Eames DSS had been stolen this Spring) -- but still very nice for large seating in cafeterias or concert halls.

What I do like in Konstanz are some rather "facilitating" aspects:  As lecturer you get a link to configure evaluation questionnaires for your lectures, then you receive the printed copies to use them in your class.  Only two hours after submitting the filled-in questionnaires, I got an elaborate analysis via e-mail.  That's great!  At the UZH I had to evaluate the questionnaires myself manually.  Part of getting the excellence label for Konstanz meant  investing in support for students -- for example, they have a very active writing center -- and for researchers.  They provide a regular newsletter pointing to open calls for grants and awards sorted by topics -- i.e., calls for linguists, for chemists, etc. -- and by career level.  That's very convenient!

And then there are some simple things when you realize that this excellence label means something else than the word "excellence" -- Swiss universities don't label themselves as "excellent", but they offer more convenience for scientists.  For example, let's consider getting reimbursed for expenses related with attending conferences.  As researcher you go to conferences and workshops to present and discuss recent findings and to do networking.  In some areas like computer science or computational linguistics, where publications are mainly conference proceedings, authors have to attend the conference and give a talk to get their articles published.  Depending on the location and the duration of a conference, this can get rather expensive.  Since the university has an interest in publications, they should pay for it.  At UZH and in Basel, researchers could get reimbursement up to CHF 1000 per year.  Swiss researchers can also get funding from academies.  So maybe going to two European conferences per year is possible.  If there is additional money at the general budget of the institute or as part of third-party funded projects, you can even publish more.  In Konstanz, the amount of money available is very limited -- less than 2000 € per year to be split between all junior researchers (i.e., non-professors) of one rather large department.  Oh, and don't forget the messy forms you have to fill in before and after the conference!  And of course you have to submit two copies of the form, both signed and filled manually (as always, the forms cannot be filled properly by using a PDF viewer).  If you don't have third-party money, you will have to pay everything yourself -- which is feasible with a professor's salary, but not with the usual 50% or less assistant salary.  In the end, this contributes to statements like "in engineering, regular money is less than 20% of the general budget" (see here) -- everything else researchers acquire themselves.  The time you spend writing grant proposals is time you cannot spend to do research, to publish, or to teach. 

Comparing just single elements (reimbursement of conference travels, furniture) at Swiss and German universities shows that indeed Swiss universities are much more attractive without labeling themselves as "excellent" -- of course not to do so is part of the Swiss culture, but then: what does this say about German culture and the German self-perception? But of course these conclusions are based on a very small sample.  

Professor for one year (week 11): What does University contribute to Society?

Last week, I participated in the Global Perspectives Programme, a joint program from University of Basel and Virginia Tech to foster academic exchange about Higher Education.  This year, the topic of the program was "University and Society: Meeting Expectations?"  We explored various aspects of "Society," "University," and "Expectations."  There are so many definitions and views of these broad concepts, that one could discuss hours and hours.  One aspect, however, is what university is expected to contribute to society.  Is it about providing solutions to current or future problems?  Is it about foreseeing future problems?  Is it about developing resources to be used for society's needs?  Is university urged to serve society and provide what society explicitly wants or to provide what society unconsciously needs?  So far, we wondered, what kind of solution universities would produce.

Most of the time, faculty and administration talked just about the questions we were exploring.  During our one-week trip in the US, the Basel group also visited Virginia Tech and had a vivid conversation with faculty of the Virginia Bioinformatics Institute (VBI).  Christopher Barrett, Scientific Director of the VBI, argued that universities would provide methods and tools to be used by society, i.e., policy-makers, to solve problems.  He emphasized that universities do not contribute solutions for current or future problems.

This statement made me wonder: At the one hand, with an attitude like this -- universities provide resources and tools to be used by others -- there is much room for basic research, i.e., research with no urgent application but that could be useful in the future.  Researchers are freed from the pressure to explicitly show usefulness in today's society.  And it makes clear that society is responsible for solving problems and for making use of the provided resources and tools.  A very comfortable statement for research, I think.

On the other hand, it reminded me a bit of the drama "Die Physiker" (The Physicists) by Friedrich Dürrenmatt from 1961 and the Manhattan Project (Einstein later regretted having signed the letter to Roosevelt in 1939 recommending that atom bombs be made).  And more so as Barrett told us that he had worked at Los Alamos before coming to VBI.  When universities -- or more precisely: researchers -- say that they only provide tools to be used by whomever, researchers implicitly say that they are not responsible for any outcome.  A researcher invents something, hands it over to the public and then doesn't care about how and by whom it is used. 

Although I appreciate the attitude to provide resources and tools rather than tailored solutions, I think universities should carefully state how to make use of their tools and emphasize the intended use.   Researchers should always take into account possible use of their findings -- the affordances -- and how to prevent criminal, inhuman, or warlike use.