Supervision families

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

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

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

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

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

Back where I started

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

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

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

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

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

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

However, I grew older.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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