text:
How to supervise a bad PhD student
I am a postdoc helping in the supervision of a PhD student who is finishing his 2nd year now. This is in Europe, meaning the student came in with a MSc degree and these 2 years have been of research. "Helping in the supervision" actually means here that my professor is nominally appointed as supervisor but I am doing all of the actual supervision (my professor is not familiar with the details of our project, just the "big picture").
The project is on computational physics/chemistry, and the PhD student has a background in chemistry with no prior computational knowledge. At the time of hiring we were under pressure to get the project going and complementing my physics background with someone who actually knows chemistry seemed like a good idea back then. Also the student was very motivated.
I have been spending a large amount of time teaching this student lots of physics and programming/scripting, which is needed to carry out the project. I assumed I would need to spend a lot of time at the beginning because of the background mismatch, so that was no problem. I thought things would improve with time. Unfortunately, they have not. The student is terrible at any kind of programming and has a lot of trouble learning new concepts, but what worries me the most is his attitude.
He basically is obsessed with getting results but is overlooking learning, in the form of reading books and papers and working hard on a problem for a period of time. If I tell him to "bang his head against the wall" for a couple of weeks trying to crack a problem before seeking advice from me (like we all have done during our PhDs), he gets frustrated after one or two days and starts sending me lots of desperate emails begging for me to intervene. This is a "gimme teh codez" kind of student, looking to avoid any problem which is of any real difficulty. I spend long meeting sessions explaining the theoretical and practical details of some approach, but he only seems interested when I write code that he can copy paste and use to get results (without even understanding the code, let alone the underlying physics).
Because of this I have to do lots of debugging and finding the same little (and large) mistakes that arise now and again because the student does not understand what he's doing. I have discussed many times with him that he needs to focus on understanding theory and code, instead of just getting results. But this is to no avail. I get the impression the student wants to do a technician's job rather than a scientist's job, but still get a PhD out of it.
As a result, I find myself working personally on any part of his project which has any hint of difficulty in it, spending way too many hours a week doing supervision, and getting increasingly frustrated at the lack of progress on the project. At this point, it would be fair to say that I could myself get everything he has to do (and more) done just in the time I spent meeting with him.
I have brought some of these concerns up, but the student won't accept they need to adjust how they work, instead claiming the tasks are too complicated (believe me, they're not), the professor does not help enough, the project is not well organized, etc. I don't know what to do - this student is getting easily 10 times more help than I did during my PhD (and I had a good experience).
To complicate things, I am just a postdoc so I have not a wide experience supervising different students that would tell me whether this case is common or isolated.
Am I expecting too much from my student? Is the problem I'm having a common one? How can I improve his attitude towards learning and working? How to deal with a bad research student?

Comments are not for extended discussion; this conversation has been moved to chat.

I am currently on my second PhD since I dropped out of the first one. I was the "nightmare student," and so I'd like to give you my perspective. This may not be relevant, but I started out as highly enthusiastic and motivated but got disillusioned as the level was much higher than I anticipated. Like your student I was moving into a physics field with a different background (biology) and had no real coding experience.
I experienced something that can only be described as "turbulence." I became so paranoid and insecure that I couldn't do anything by myself. My supervisor and project postdoc would send me some code, or a task they needed to do, and I would freeze. I'm an intelligent person but my brain couldn't focus. I'd read Stack Exchange and be unable to implement the solutions. If a solution varied even slightly, I wouldn't be able to implement it.
There was some underlying psychological problem. Insecurity, anxiety, stress -- whatever it was, I was a nightmare to be around. I never became independent and the more boundaries people tried to put up with me, the worse I got.
When I eventually quit, it was the greatest relief of my life. My postdoc hated me and was openly criticising me for my lack of independence. For some people that works, but for me it made me freeze even more. I'd panic, I was desperate for validation, and just couldn't concentrate.
Objectively I agree with every other comment here: set boundaries, he needs to become independent, don't exhaust yourself. But for me, those responses made me worse. Deep down I didn't think I was capable but couldn't admit it to myself so was stressed out and needy. Paradoxically, what would have helped me most would have been to be reassured that I could do it as a person. E.g., that I was fundamentally worthy and deserving of my place in the department, and that my weaknesses were not inherent parts of me, but skills that could be overcome.
Sounds so stupid!!! I'm sure many people will feel cynical about that, with good reason. But if I'd had that space to feel emotionally secure I would have felt more grounded, safe and able to solve my problems. In the end, feeling valued and secure would have saved my PhD when being criticised only made me more insecure, but that's just my experience. I know this must sound ridiculous. It was just my situation.
The only positive for me is that I now know everything to avoid on my second PhD. But it would have been preferable to get here without wasting 3 years' of public funding and annoying a lot of people.

I don't think it sounds stupid at all. Furthermore, I think this answer is at least as valuable as the other ones which do not really take into account the human side of the question.

This is a very valuable perspective. Probably worth noting that "set boundaries" and "provide positive and hopeful comments" are not necessarily mutually exclusive!

I get the impression the student wants to do a technician's, rather
  than a scientist's, job but still get a PhD out of it.

From what you've described, this seems like a very accurate assessment, and it's the crux of the matter. You need to get this message across to your student. Magicsowon's answer does a great job covering the practical aspects of how to (attempt to) get the student out of their bad habits. I think also explaining the situation to them very clearly is an important component. You have already brought up some concerns with him, but perhaps there is room to be more blunt/direct. You need to get the following points across:

The difference between a technician and a researcher, as alluded to in the quote above. A PhD is not awarded just for putting in a certain number of hours. It is a certification that you have the ability/potential to be an independent researcher. Merely working through a set of tasks with one's hand held throughout does not demonstrate that ability.
Someone who has been showing no initiative and requires that much help is going to really struggle in their thesis defence.
Difficulties such as "tasks are too complicated..., the professor does not help enough, the project is not well organized" are ones that will crop up throughout a scientific career. If the student cannot find strategies to cope with them, they cannot be a successful scientist.

No offense but many PhD students are like that "...getting PhD with technician mentality..". Great answer (+1).

Is this 'project' the student is working on really synonymous with their full capacity as a PhD student? I think it unfair of both you and the OP to confound the two competencies. If you hate your boss, your job, or programming that doesn't mean you won't be a world-class doctor of chemistry. The OP is telling us how the situation is inconvenient for him, not what the graduate student is experiencing. Rather than attacking (and then dismissing) the student's capacity the comments here should be about mediating the working relationship.

@Quixotic I take your point; sorry if my answer came across as excessively harsh. However, while you are quite right that this student may have abilities that are not coming to the fore in the current project, unfortunately for them, the current project is what they are going to be judged on.

@User2390246 And I understand your point that intellectual weakness in one area may be predictive of overall deficiency, however university employees are meant to be managers and expert educators. The OP is not demonstrating traits of either, nor are most commentators; everyone is venting and predicting student doom. Just because the OP is in the position of authority it doesn't mean that he is doing a good job or his judgment of the student is accurate.  The student deserves a structured, ethical environment.

Some people just don't get coding.  If he's one of them, he's in the wrong PhD.
With a chemistry background he probably hasn't written any real code since school (and then only if you're lucky), but might have done a "computational chemistry" module writing simple scripts for a pre-written model or commercial software.  Signing up to a computational PhD was probably an error.  I've found that people don't realise how unsuited to coding they actually are until they really have to do it.
At this point you've done what you can and it's up to the real supervisor.  You may need to document the bits you've done that the student is relying on It's possible he's heading towards a form of plagiarism if you wrote the code and he claims or implies that he did if/when he writes up, but even setting that aside if the progress he appears to be making is actually based on work you did, it's time that was made clear.
The supervisors options are rarely restricted to making him quit or you spend even more time on the project.  There's often the possibility of changing the project to something more suitable.  You could take some time to think about what skills he does have, and whether there's a role for them in your group, especially if it's not a purely computational group and there's experimental/theoretical work that would be more suitable.  You don't have to find a solution but as a responsible postdoc it doesn't hurt to look for some positive possibilities.

The project is on computational physics/chemistry, and the PhD student has a background in chemistry with no prior computational knowledge. [...]
I have been spending a large amount of time teaching this student lots of physics and programming/scripting, which is needed to carry out the project. I assumed I would need to spend a lot of time at the beginning because of the background mismatch, so that was no problem. I thought things would improve with time. Unfortunately, they have not. The student is terrible at any kind of programming and has a lot of trouble learning new concepts, but what worries me the most is his attitude.

As someone with a master's in computer science who works as an industry programmer, red flags went off for me as soon as I read this in your question - but not for the student. For the way you're managing him.
Programming is an exceptionally difficult skill to learn. I would say it is equally as difficult as learning how to do research - but it is also a complete skill in and of itself. In order to get to the point I am now, as a professional programmer, I took four years of programming classes in high school, four years of a CS program in undergraduate, over 12 months in total of internships, and two years of grad school. To get to the point you want your student to be at, where I could teach myself what I needed to to write my own non-trivial programs, took me at least through high school and an entire summer spent teaching myself how to write Android apps. I know that you've gone through a similar process yourself, so this is just a reminder to you, someone who is now on the other side, that programming is very difficult to someone who doesn't have much experience.
If you want your student to be successful, you need to step back and give him a chance to actually learn how to program. The good news is that, since he is a grad student, he is both smart enough and at the level where it should only take a semester or two before he starts being productive. (Emphasis on starts - he won't be at the same level you are for years, for better or for worse.)
From your question, you've been teaching your student how to program for the last two years. I'm very glad to have read that. On my first read, it seemed to me that you had largely just thrown him into the deep end and asked him to swim, and there's absolutely no way that could have worked. However, to be honest, I still believe that programming is a difficult enough skill that you're not going to be able to teach it to him yourself. This isn't a failing on your part at all. It's just an acknowledgement that you're busy with your own research and the other aspects of managing this student, so your capacity to teach is limited, and your student is also taking classes, learning to read papers, learning how to carry out research - his capacity to learn programming on top of all of that is also limited.
To make sure he has an opportunity to learn programming in a way that overcomes those limitations, I strongly recommend that you ask him to take introductory computer science classes. He can probably skip some of the earliest courses and jump into 150 level courses. Then, while he is taking those, please stop giving him as many tasks that require him to be a productive programmer for a while. Is there other work he can do that exercises more of the background in chemistry you originally hired him for?

I certainly understand being frustrated that your student isn't being productive and doesn't seem to be willing to learn on his own. My point isn't that you should tolerate any student who isn't being productive. My point is that, in the specific situation you've described, I strongly suspect that the core issue is you're expecting a bit too much out of someone who has not had the time to learn a very difficult skill.
To emphasize, I think the reason you're expecting too much is because you're asking someone who has no background in programming to be an effective programmer on top of doing research. Either one of those presses an individual to their intellectual limits. There are many other things it is reasonable to ask a student to learn on their own - how to carry out an experiment, how to use the correct statistical analysis, etc. Programming is sufficiently difficult that it's in its own category, though.
If, after taking programming courses, your student is still not being productive, then I think your case that the problem is with the student's attitude is much, much stronger.

ImportanceOfBringErnest commented on my answer:

I think there is a big discrepancy between what a physicists call programming and what a programmer call programming. Mostly, if you show a code some physicist has written to a real programmer they start crying and run out of the room. So I would interprete the skills the questions talks about as really really basic and the real problem seems to be rather the attitude of the student.

I absolutely agree that the student does not need to be at the level of a professional programmer. He only needs to be able to bang out a script in a few hours that calculates some value and is done with it. Even so, I still strongly believe that it is unreasonable to expect a grad student to reach that late beginner level of programming ability on their own.
In grad school, I was a TA and often helped students with introductory CS homework. I have seen students starting out their programming careers take 10-15 minutes doing things like determining which arguments to pass into a function or writing a for loop that iterates over an array - and that only after banging their head against the problem until they realized they weren't going to get it on their own, sought me out for help, and got some hints from me. This stuff is truly very, very difficult to pick up, and it takes time and patience on the part of everyone involved for a new programmer to be successful.
Introductory programming courses are designed to guide students past the true beginner level to the point that they can write basic programs on their own. This is what the OP wants. The OP's student doesn't need to get a full computer science degree or be able to architect an enterprise system. But I still strongly believe that the only reasonable way to get the OP's student to the level the OP wants him at is to give him a chance to go through at least one introductory CS class.

I think there is a big discrepancy between what a physicists call programming and what a programmer call programming. Mostly, if you show a code some physicist has written to a real programmer they start crying and run out of the room. So I would interprete the skills the questions talks about as really really basic and the real problem seems to be rather the attitude of the student.

I definitely agree that the OP's student doesn't need to be at the same level as an experienced programmer to carry out the research asked of him effectively. But I think getting to the point where you can figure out how to write a non-trivial program on your own, even if it is ugly code, is still more difficult than most of these answers assume. It takes time to wrap one's head around how a computer "understands" a programming language, how to break a problem down into what it's possible for a computer to do, and to really grok ideas like functions and working with arrays/classes.

Yes, but there is a problem with expectations. In the US, you aren't expected to have ever written code before, but if you're going for a PhD in computational XYZ, and you don't know beforehand that there will be scripting and/or development that's bad. Worse when you are unwilling to sharpen the proverbial saw in order to improve your productivity because it interferes with doing research every waking minute. That is the attitude a lot of people have. It's not that they can't, it's that they don't want to.

@Kevin That depends on the student. I did a degree in chemical engineering where the only formal computer "training" was a 2 week course in Fortran. I went on to become a professional programmer having taught myself Assembler, C, C++ and Java mostly from just reading textbooks and writing programs in my spare time while I was learning.

@ImportanceOfBeingErnest You are more right than you should be about programmers running screaming from physicist's code. I have become a very firm believer in reading a style guide

@ImportanceOfBeingErnest Considering the fields and we talk about computations, they may have to learn specific stuff, like "how to do when Matlab "" is not enough". And that is damn hard. As said Kevin, I though totally the same when reading OP's post. The student couldn't know it would be that hard, OP and his supervisor should know it. Furthermore lerning programming don't require only to read, it requires a hell lot of practices with failures in order to be able to understand throughly how to do things right, basically, it's very time consuming.

@ImportanceOfBeingErnest I edited my answer to respond to your comment in more detail.

I have been a PhD student, and then an advisor.  I had an excellent advisor, and learned a lot from him.  The key to being a successful advisor has two major components:

Know when to kick ass
Know when to provide encouragent/comfort

Those are your only responsibilities.  Basic skills are the responsibility of the student.  You can discuss ideas with the student, help with insights, and set expectations.  If the student fails to meet expectations, he is on his way to being a former student long before he hits thesis mode.  You are not responsible for saving him by doing his work.
What is critical now has already been suggested: meet once a week, set goals, in writing, and demand progress reports in writing.  Email works very well for this.  It also provides a CYA audit trail, protects you from being diagnosed as failing because you are doing his work as well as yours, and makes it very clear that his failure is his own problem, not yours.  So when decision time comes, he can be let go because there is a written record of his failures.  This is not unlike the requirements for employee evaluation that you will face if you end up in industry.
It is also worthwhile to keep time sheets on how you spend your day.  I kept a simple text file.  This includes time you have spent “doing his job”, that is, writing code that is critical to you but he was supposed to have written.
The “virtual paper trail” is important.  As observed, a PhD student is expected to work well on his own.  Not have you do all his work for him.
I once had a student who was not even capable of basic freshman tasks and apparently earned their undergraduate degree by letting others do their work for them. I went to their supervisor and explained this, but nothing came of it. It took another year of wasted salary before they fired by a new supervisor.
You are not doing your grad student any favors by doing all his work, you are not doing your school any favors by covering up his failures, and you are not doing your profession any favors by churning out someone deeply unqualified to be a professional.  And you are not doing yourself any favors by doing two jobs.  It is time to fledge this bird.
You can’t save everyone.  Some people cannot be saved because they do not understand (or refuse to admit) how unqualified they are.

Yup I totally agreed with the last paragraph.  Some people just cannot be saved.

You say you are based in Europe, but the PhD system of mainland Europe is much different to the UK, where I live. For example I completed my PhD in 3 years, whereas my European colleagues generally tend to spend (at least) 6 years on a PhD, if this is the case then this student is still just starting out and could probably afford to take some time out to catch-up on the basics. Even if you are in the UK, 3 years to complete is an absolute minimum it would benefit him to learn these skills even if it means delaying thesis submission.
Perhaps there are some external courses for the student to attend on the more technical skills like programming, I had £3k per year to spend on training during my PhD. I would also imagine there are undergraduate courses at your institution which the student can 'sit-in' on the lectures in physics and programming, I would encourage him to attend.
Rather than giving him direct answers to his questions and coding demands, point him in the direction of published work (papers/textbooks) on the subject, be ready to discuss these with him after he has had time to absorb them (not just to read them, but to really understand the authors message). Make clear that it is not your job to do his work for him, and that he will become a much stronger scientist if he perfects these skills now.
What happens when he starts writing his thesis and he still doesn't fully understand what he is doing, the method used, and as an extension the results. He will either misrepresent results or simply miss important conclusions, unless you also write his thesis for him.
Don't get me wrong, I am all for supporting the student, especially if it is a difficult or technically challenging project, but babying them will just result in more pain in the future.

I am not sure which Europe you are talking about, most PhD in Europe are 3-4 years. Italy is 3 years, Switzerland 4 years, France 3-4 years, Germany 3-4 years.

@user4050: many places are in practice minimum 5 years. (dont forget 20% "institutional duties" which are supposed to pay for the employment, and which often can accidentally take up a bit more than that percentage.)

@mathreadler I don't see where you got this 5 years information, I have never seen that and I am familiar with different systems.

@user4050 : Where I got that information? You must be kidding. I have seen many students on that five year journey. 5 is the minimum, quite many who end up professors take 7,8 or even up to 10 years.

@mathreadler Are you referring to phd students with 5-10 years of funding and/or a scholarship? Typically phd scholarships/fellowships lasts 3-4 years, what department would fund a student for 10 years?! Unless the student teaches a good number of hours. I would never pay a PhD student for 10 years!

@user4050 In Belgium, a PhD can take 6 years if you are hired by the university as teaching assistant.

@user4050 : There are many different variants to fund it. For example like Arnaud writes, formally taking a "pause", working as teacher for an institution at the university or at a local company or something third. Also, why wouldn't you? Rising quickly does not have to mean he's good at research. Could mean anything from being well connected, socially smart, good at office politics or management.

@mathreadler I'm not sure you understood my comment. To clarify, I meant that for PhD students in Belgium, 6 years is the norm if their funding comes from the university (which is the case for the majority of PhD). There are other source of fundings, where you have less or no teaching to do and then it takes 4 years.

@ArnaudD : I understand your comment. It is the same where I went and in many other places in Europe. For an industrial PhD candidate employed by a private company with no teaching duties 4 years is quite usual. The fifth year is if you teach or help the institution do other things for the employment which in that case is by the university and in my experience constitutes the vast majority of all PhD students.

@DSVA : There is no concrete ECTS range or time requirement for doctoral level in the Bologna system. Also PhD is "third cycle" where one would be supposed to have finished a second cycle (for example masters of science) to be allowed to start it, but we all know some places where that is not a requirement.

@mathreadler Depends on the implementation of Bologna. In my country (Serbia), for example, a doctorate in sciences is exactly 180 ECTS and requires 300 ECTS worth of academic studies. Medical ones have a bit more ECTS. Even worse, your studies are limited to 2 times the regular length, so if you can't make it in 6 years, you're in trouble.

@ArnaudD. it must be field dependent then, at the university of gent in the faculty of science all phds are 4 years.

Swedish PhD can take 5 years including 20% institutional duties, so it's supposed to be 4 years for research+courses.

Yep you are right, but full professors who have taken 8 or so years completing theirs is not particularly uncommon @gerrit.

As someone who is a PhD student in computational chemistry who was new to programming when I started the PhD: Please try and be aware how alien and different coding, computer science, computer theory, etc is to someone who is used to being a physical scientist. It was a whole new world when I started and it was very difficult to motivate myself to put all my focus into learning coding and programming, when what I really felt I should be doing (and what I wanted to do) was learn about atoms and molecules. It's difficult to get over the shock at being in a position of serious research, and yet knowing basically no more than a high school senior (about coding). Most grad students I know in my friend were in similar positions as me; very, very few were CS people beforehand or had done anything more than screw around in perl or python for a few hours. I don't know how long your student has been with you, but there's a huge barrier to entry in this kind of situation, so don't set your expectations too high in regards to coding learning speed + enthusiasm, especially if they seem genuinely interested and motivated to learn in other areas.

You had a lot of great a "kind" answers which may work for most of the people, but success isn't for everyone. It's a fact you've gotta admit.

I get the impression the student wants to do a technician's, rather than a scientist's, job but still get a PhD out of it.

As sad as this would be for him, he may not be built to be anything else than a technician, and if that's the case you need to let him fail. He'll need that lesson the sooner possible.

If his field requires programming, how would being a technician change that?

@JAB I don't think his field necessarily requires programming, although in more and more technical jobs it's a very helpful side-qualification; neither is programming the main problem OP identifies. It's the fact that the student lacks the endurance to delve deep into a topic where he needs to learn a lot on his own before he can apply his knowledge and get some practical results. That's why he seems to much better fit a "technical" job, where tasks are typically smaller and following proven paths.

@JAB He's coming from the chemistry field and is trying to go further. To go further he needs to learn to get out of his confort zone, and learn new things even if they're not directly related to his field.
Here, when the OP says "I get the impression the student wants to do a technician's job" he's speaking about a chemistry technician, aka doing lab stuff.
So this is my point, maybe he's just not built to be more than a chem-lab dude, or simply not ready yet.

Your best option is probably to guide him in a way that both helps him go forward with his work and helps him develop the resilience and perseverance that are part of a good scientist.
The best way I see to do this is to link him a set of papers containing what is needed for him to reach the solution he asked you for, maybe even in the form of a query on one of your field`s research archives.
This way you avoid being "milked for teh codez" while still being a competent supervisor, teaching him how to approach problems in a correct manner.

My professor is nominally appointed as supervisor but I am doing all of the actual supervision (my professor is not familiar with the details of our project, just the "big picture").

No one is benefiting from this supervisory relationship.  It's time to pull the plug.  Tell your professor that it's not a good fit and that you can't supervise this student any longer.  If he wants more detail, tell him that the student

does not have prior experience with programming, is not applying himself to learn programming, and doesn't have a natural aptitude for it
doesn't understand the underlying theoretical underpinnings of the project
is not able to work independently

If at some point your professor assigns you a different student to supervise, please don't wait so long to report back about how things are going.

Most of the 'answers' here suggest that the post-doc "save himself," but they do not provide constructive manager-employee conflict resolutions.
I want to outline some suggestions. Try to honestly, objectively assess the following:

Does your employee have a learning/language barrier? If yes, seek specialist assistance for clarifying instructions to the employee.
Does the employee have adequate resources to understand and execute instructions? (Consistent computer, software, textbook, tutorial access).
Is the employee failing to perform due to poor scheduling? If yes, is it due to his own choices (procrastination, under-estimating time needed), or because management has failed to allocate appropriate planning stages for the project?
When confronted with a new problem to solve, is there a consistent stage at which he is failing? Consider requiring documentation or a lab journal for employees so that they can demonstrate what steps they took to resolve issues.
Is the employee/manager conflict emotional? An employee that dislike his boss may engage in active sabotage, while a manager who personally dislikes his employee may engage in exaggerated fault finding.
Does the employee have adequate pre-requisite training/skills to perform at the level required? Consider having a third party participate in a meeting to determine if you are being unreasonable in expectations. If employee has skill inadequacies have a skill-set expert advice on a learning plan, do not invent one on your own. Refer employee to appropriate tutoring, courses, or online tutorials where he can be held accountable for completing a training session.

Motivating and correcting the student should come, once a week, in timed, structured meetings. The student should also be given time to state his own concerns, ask questions or offer solutions without interruption or ridicule. Consider the following:

If this is a chemist, perhaps the tutorials closer to their area of expertise.
If the programming is unrewarding consider offering short-term incentives, for example allow him to present a successful model or GUI to undergraduates and get a round of applause for it.
Find bait. If he is publication motivated, use it. If he enjoys educating others, have him write a tutorial, if he likes editing graphics, assign those.
Consider partnering the graduate student with a successful undergraduate who has programming expertise. Ask the graduate student to provide chemistry insight in exchange for the undergraduate learning/practicing programming at a more advanced level. If possible, hire an undergraduate to collaborate with the graduate student. Having an ally or a position of authority may invigorate both.
Praise what can be praised. When the student does solve a problem or does something that demonstrates initiative signal gratitude and do not be sarcastic or condescending.
Network the student, if possible, with someone who could have experienced a similar learning curve and motivation challenges, but who is now successful.

When positive reinforcement fails, or falters, here are some appropriate negative reinforcement techniques:

Document your weekly expectation and give instruction on what the employee should do to "self troubleshoot" before the next meeting. When reviewing work consider if step 1 and 2 were taken, if applicable; if 0/2, report to overhead supervisor.
Ask employee to be responsible for correcting his own errors. Provide annotated code, not corrected code such that he has to go in and implement corrections directly.
Provide task specific deadlines and meeting agendas (even if one-sentence long e-mail). If your employee is failing to respond to milestone requests through procrastination or disrespectful attitude to your position, document it and show this to the professor and/or department chair. Harassment or personal attacks are not okay.
Ask the student to present/summarize the purpose of individual experiments or coding that is being performed. If there is a failure to comprehend the project, ask a (qualified, objective) third party or additional participant to provide feedback. If the third party agrees there is a comprehension gap, report this to the professor.

I am not doubting that you are in a difficult, time consuming situation, but I do think you might be failing as a manager and educator if your two solutions are "do his work for him" or "fire him." Learn to manage, motivate, and document your efforts. After documenting the experience, allow the professor to make the hiring/firing decision.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
What is an "un-reproducible result"?
In order to publish a research paper in a journal, one of the requirement may be the reproducibility of results.
I'm wondering, in academia, how does an un-reproducible result even exist?
Is there any classical example for it, in any branch of academia research?

You do some science, you see a result, you can't repeat it: Because something was different. In biology, warmer/cooler room/fridge could be the cause, for instance.

Please define what you mean by "reproducible," people have different understandings

See "Journal of Irreproducible Results" ;-)

https://en.m.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False

I'm reminded of Adam Ruins Everything: "One research team just replicated 100 famous psychology studies, but found they couldn't reproduce about 60% of them... Another revisited 67 major drug studies, and found that about 75% didn't match their results... Another team zeroed in on 53 recent cancer studies, and couldn't reproduce 47 of them... One estimate suggests that, in the U.S., we spend $28 billion a year on biomedical research that can't be reproduced."

This reminds of the paper: https://cpb-us-e1.wpmucdn.com/sites.usc.edu/dist/b/364/files/2019/05/EE650_incredibles.pdf

The question is too broad.  You may read Ioannidis PLOS article and use many of the publications in PLOS under «reproducible research» to learn about this, e.g., by this. Even if eventually aiming for a text editor, the first 28 min of Misshula's talk at NYC's Emacs user group over some general overview, too.

@Laurel Your possibly refer to Wicherts et al. (open access), or this, and / or this mentioned in Misshula's talk mentioned above.

It's important to define what reproducibility actually means and in what context it is used. Science deals with things that can be reproduced in principle: If you managed to re-create the exact same situation, you would be able to get the same result.
But in practice, that doesn't always mean that you can re-create the situation: You might have measured the seismic waves of a very large earthquake in Indonesia. Or you might have seen photons of a nearby supernova. Neither of these conditions can be created by humans, and so the experiment can not be repeated in practice, though in principle it could be. A related situation happens if it is impractical to do so: If the original experiment was done with a ten-billion $ machine (say, a particle accelerator, a nuclear fusion reaction), then yes you could repeat the experiment, but you probably find yourself in financial trouble if you tried. There are also valid research results that should not be reproduced, even if they could: Say, whatever we may have learned from the Tuskeegee syphilis study or the Stanford prison experiment might be scientifically correct, even repeatable, but one can only hope that nobody will ever try to repeat these studies.
Finally, there are often practical constraints: If you take a picture of turbulence in a pipe, you will not be able to recreate the same picture because turbulence is a chaotic process; similarly, if you try to do experiments on a single cell and count the number of molecules of a specific kind, you're likely going to find that it depends sensitively on temperature, time of day, etc. That doesn't mean that the science is wrong: In both cases, statistical assessments of the results may still be valid, even if you can't recreate the specific numbers.
Of course, there are also experiments that really can't be reproduced: Someone published the results of an experiment that seemed reasonable to them and to the reviewers, but the measuring device had a mechanical defect and consequently every number in the publication is just wrong and the measured effect does not actually exist. This of course shouldn't happen, but it does happen in practice. There are also common statistical problems in studies that involve a small number of human subjects where the random, involuntary, or voluntary choice of subjects suggested an effect that, if repeated on a larger and more random cohort does not actually exist.

This answer could be improved by expanding a bit on when results are unreproducible (i.e. what the question asks). A mechanical defect isn't the only reason results might be unreproducible. I would imagine it's also one of the less likely cause of unreproducibility. Also, where does OP "confuse what reproducibility actually means"? I can't see that anywhere. In the question there are mostly just questions asking what it means without any assertions or implications about what it means.

@uhoh Fair point -- how's this?

Looks great, thanks!

Interesting answer, but it is not why in practice results are not reproducible: of course a particular supernova explosion cannot be studied twice, and no one expects to be able to do so. It should therefore not be the first answer.

Another possibility leading to a result being unreproducible is insufficient documentation, logging, or provenance and version tracking.

Also, a fabricated result would be unreproducible, and in some cases "unreproducible" may be a euphemism for fraudulent.

@Jake : If we are positing fabricated results, then why do we reject fabricated reproduction?

@Jake: Right. In practice, a very substantial subset of scientific "results" "can not be reproduced in actuality". I took the question about "un-reproducible" to mean the theoretical distinction between "reproducible research" and "non-reproducible research", not whether a specific result can be repeated. So I'm not disagreeing with you, I just understood the question differently.

I'm analytical chemist, and we're in a somewhat more fortunate situation. In our case, reproducibility means: another lab can take our paper, collect samples measure and analyze as described and arrive at results that support the conclusions we drew in our paper. (If we can do it again, that is called repeatability)

I like this answer, but should not we add ethical constraints.  Certain activities like the Hiroshima bombing, the Holocaust, and COVID-19 have tremendous effects and teach us a lot but should not be reproduced.

@emory Yes, great point. I've added a couple of examples of experiments that should not be repeated, even if they could.

Wikipedia has an excellent article on the current Replication Crisis (or Reproducibility Crisis), and I can't hope to improve on it. I would recommend that you start by reading that article, the many examples included, and the other referenced links therein.

The replication crisis (or replicability crisis or reproducibility
crisis) is, as of 2020, an ongoing methodological crisis in which it
has been found that many scientific studies are difficult or
impossible to replicate or reproduce. The replication crisis affects
the social sciences and medicine most severely. The crisis has
long-standing roots; the phrase was coined in the early 2010s as part
of a growing awareness of the problem. The replication crisis
represents an important body of research in the field of
metascience...
Glenn Begley and John Ioannidis proposed these causes:

Generation of new data/publications at an unprecedented rate.
Majority of these discoveries will not stand the test of time.
Failure to adhere to good scientific practice and the desperation to publish or perish.
Multiple varied stakeholders

They conclude that no party is solely responsible, and no single
solution will suffice. In fact, some predictions of an impending
crisis in the quality control mechanism of science can be traced back
several decades...
Philosopher and historian of science Jerome R. Ravetz predicted in his
1971 book Scientific Knowledge and Its Social Problems that science –
in its progression from "little" science composed of isolated
communities of researchers, to "big" science or "techno-science" –
would suffer major problems in its internal system of quality control.
Ravetz recognized that the incentive structure for modern scientists
could become dysfunctional, now known as the present 'publish or
perish' challenge, creating perverse incentives to publish any
findings, however dubious. According to Ravetz, quality in science is
maintained only when there is a community of scholars linked by a set
of shared norms and standards, all of whom are willing and able to
hold one another accountable.

Keep in mind that modern scientific studies have a probabilistic aspect in how their random sample has been determined. It's the goal of statistical inference to express and clarify this fact. For example: Say there's a deadly disease, a researcher creates a medication that in truth does nothing, but just by fortune happens to give it to the only 10 people in the world who will recover on their own. Then that study will appear to be an amazing success and surely get published, even though no one will ever be able to recreate the effect. This is called Publication Bias and with more and more scientific studies done over time, it's theorized that a majority of published papers may now be in this category.

I was about to post an answer that focuses on too small or non-representative samples (esp. in social sciences) as well as p-hacking & related issues when I realized that all this is covered in the article you cite.Maybe you could summarize/highlight some of these points directly in your answer because I think these issues receive less attention than they should.Aspects covered in other answers are important (cost of reproducing results, one-off experiments like a supernova, computational power, lack of information, etc), but IMO these statistical aspects are key for (non-)reproducibility.

@CL.: I agree the statistical aspects are key. Initially I avoided getting into that to keep the answer brief, and because in the linked article the statistical discussion is field-dependent. I added a final paragraph trying to very briefly scope out the issue, thanks for the suggestion.

Enough information to reproduce
If we look at a journal requirement for the results to be reproducible, it's not about the result being reproducible in principle, but about the possibility to reproduce it based on the contents of your paper.
For example, your paper may describe a result that you can reproduce but that is effectively not reproducible by omitting key details of the experimental setup or by relying on data that's classified or otherwise unavailable to others. If so, you're effectively asking the journal, reviewers and the wider community (who might actually attempt to reproduce the results later) to accept your results as true based on pure faith and goodwill - and they may refuse to do so. This limitation generally is not strictly applied to things that are very difficult or very expensive to reproduce (e.g. you need the equivalent of the Large Hadron Collider to replicate LHC results, but physics still wants to publish results coming out of LHC even if noone else has a comparable particle accelerator), but for situations where practical reproduction is plausible, it makes sense to mandate that the authors include in the paper the information required to do so.

I think this is the best answer, even if it does not technically answer the question as asked

To add what others have already written, reproducibility in academic publishing mainly deals with the extent of how you have described your research methodology.
UPDATE: Un-reproducible results are largely frowned upon and a result of not describing the study properly. There is a place in science for experience reports and conjectures, however these should be framed as such.
Say, you are dealing with something unique like a supernova exploding. There is no way to recreate it in any practical sense. However, you can describe the star (type, size, distance, composition, environment, details of the explosion, etc etc) to provide the reader with enough details to understand what you are studying.
Similarly, you describe what kind of tools (telescopes, detectors etc.) you have used to collect data, what kind of methods you used to analyze the data, and so on. Thus, the reader can trace back your conclusions to data and to the circumstances of how the data was collected, and to the phenomenon being investigated.
Thus, the whole study may not be reproducible but parts of it can. Like, using the same equipment in the same way, applying the same data analysis methods, apply your reasoning to draw conclusions.

In order to publish a research paper in a journal, one of the requirement may be the reproducibility of results.

Your assumption is false.  Only certain kinds of journals require reproducibility.
Classic examples of unreproducible results include:

Case studies of unique circumstances.  For rare diseases, these are very valuable, but they apply to many non-medical fields of research too.
All observational astronomy (there is only one universe).
Statistical fluctuations.
Expensive experiments.
Errors.

Only the errors should not be published.

There is the word "may" in the quote. It makes it strange to continue with "your assumption is false" and next verifying the quote as true.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

sort: votes


text:
An existing paper has better results than me, but its published code does not reproduce those good results. How to proceed?
I am performing research on a particular architecture in deep learning. Initially, the state-of-the-art research paper in Arxiv related to architecture is showing a score of (say) 9. Later the value is updated to 10 in the research paper. In the corresponding GitHub account, the code for the architecture is the one that is capable of giving 9 only and the authors did not update the code or other contents in the research paper. Anyone can able to get the value 9 if they execute on their system. The tickets are open and the authors didn't respond to any of the tickets that are asking for the code that is capable of giving the value 10.
It may be the reason that the research may be undergoing and authors are not willing to publish either the technique or their code.
Although the paper got citations, it is not published in any Peer review journal
yet.  The citations are, I think, for the architecture that is capable of generating 9 since no one can able to get 10.
Suppose I got the value 9.5, can I send it to any Peer review journal for Publication since the technique or code is not available in public and the authors are just claiming the value of 10?

Can I know the reason for the downvote?

It's not an answer, but as a reviewer I don't really care whether someone reports 9, 9.5, or 10 -- for me, it's the same ballpark, with the result contingent on the current phase of the Moon and minor deviations in code & data. Thus, I'd position my approach as having some other merits rather than providing marginally better scores in some synthetic tests.

Rather than publishing this paper, I feel it would be better if you have a preprint for it. If your technique is completely different from the one they have used, you could try expanding your study to a different dataset which they have not tested on.

If I understand correctly, this group has published an algorithm and claims to achieve a score of 10 in numerical experiments. The presumption will be that this result is correct, even if the publicly-available source code does not reproduce that number. Now it's true that one could write their own paper saying "this group's results are not reproducible; I did exactly what they said (using their own code!) and got a score of 9." But doing this might be inadvisable, since the other group will likely be able to defend their claim of 10, and then you will look bad for making a false accusation.
It certainly is annoying when groups publish buggy source code that is not aligned with their own results. But remember, most journals do not require source code at all. Even some major AI/ML results from Google use proprietary datasets, so no one can reproduce their results; we just have to trust them that the results would be reproducible if we had a similar dataset. This is really not so unusual in science; the same is true for results from large telescopes or particle colliders, for example.

Suppose I got the value 9.5, can I send it to any Peer review journal for Publication since the technique or code is not available in public and the authors are just claiming the value of 10?

You can send whatever you want to a journal. Certainly you can "make the case" that a reproducible 9.5 is better than an irreproducible 10. And perhaps your algorithm has other differences/advantages that make it interesting. Nonetheless, the presence of this previous paper that claims to get better results will be a major adverse factor when the reviewers decide whether to accept your paper.

I am curious, how do mistakes (or even deception) get caught if everyone assumes that all claimed results are correct? It is understandable for the Google-scale experiments and proprietary datasets, since almost no one can even try to reproduce them, but for smaller scales, when code is unavailable, shouldn't it suffice to reimplement the methods described in the existing paper and compare with your own? This is what quite a few papers I have read seem to do. After all, the results will be comparable only if the settings (dataset, architecture, etc.) in both are identical.

@GoodDeeds Short answer: they commonly don't in AI/ML. The field is moving so quickly barely anyone has time for those; if the result is not particularly interesting/promising, it's going to be abandoned in an year tops, much to the demise of those seeking to reimplement it thinking it'd help with some SOTA-related part or with a non-mainstream dataset. The reason for this is that it's lot more industry-leaning rather than science mindset: one doesn't care whether the results are correct, only how much effort would it take to make it work for them and whether it's applicable to their problem

@GoodDeeds - yeah, if there is a significant claim (e.g., we set a new record on ImageNet), the sort of tests you describe will normally be done by the authors prior to publication, and there will be lots of interest in reproducing the results. But for less significant work (i.e., most papers), someone might personally conclude that the article is bogus, but due to the low attention any given paper receives, it is highly unlikely they would go through the process to formally challenge a paper.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Anonymous Student Feedback: Moving forward from abusive comments
As a relatively new lecturer, I'm always looking for ways to improve my teaching. After doing a bit of research around how different lecturers etc approach student feedback, a number had stated that using anonymous feedback solicited during the semester can be really helpful.
So following on from that, I put on my learning site an anonymous feedback form for students that could let me know how they think the unit is going and what could be improved. The idea behind this was to get real-time feedback that could be implemented throughout the course, instead of for the following year. This portal is only accessible to students, and the comments are submitted to me and my TA privately.
I left this open since week 1, and perhaps that was my mistake. Despite barely being into week 2 (in which week 1 was just a basic introductory lecture to the course) one student has provided some anonymous feedback, which isn't feedback at all. Rather, it is just a string of abusive, sexist and foul language comments.
Due the alarming nature of the feedback, I approached a mentor/supervisor about what I should do. They were conflicted, because on the one hand, the nature of the comments were highly inappropriate, abusive, and raise concerns about the safety of my person in my teaching space. However, no specific threat at this time was made. On the other, having anonymous informal feedback throughout the unit is a good way of getting students to reflect on their own learning experiences without the fear of being reprimanded re assessments, and help me improve my teaching in the process.
I'm caught between continuing to allow anonymous informal feedback, or taking this option away and informing students of the reason. No other student has provided any feedback. Only myself and my teaching assistant have access to the feedback. Only students enrolled in the unit have access to the form.
How would you handle this situation?

It sounds like you've encountered the well-known downside of online disinhibition effect, more colloquially known as GIFT.  There is simply nothing that can be done to prevent some people from being horrible when offered anonymity.
So, let's parse out two different questions about how you should react: 1) what actions should you take to protect yourself and other students? and 2) should you continue to use the anonymous feedback form?
Let's start with the first question: given that you know there is a horrible person in your class, what should you do to protect yourself and other students?
I see three basic possibilities here, either a) the person is insincere in their statements and is trolling you to try to get a reaction, or b) the person is genuine but not a threat, or c) the person is an actual threat to the safety of yourself or others.  If you are concerned that it might possibility c), then if there is any way for your IT staff to penetrate the anonymity and investigate, it's reasonable to ask them to do so, just as if somebody had made a serious electronic threat through some other channel (your institutions likely will have a process for reporting serious threats of harm, though, which may mean you shouldn't start by going directly to IT).  Otherwise, it's probably best to ignore it and move on, because otherwise you're just giving them the reaction and power over your actions that they are looking for.
Now, turning to the second: should you continue to use the anonymous feedback form?  The one problem with true anonymity is that if you get more nasty comments, you can't tell whether they came from the same person or not. I would recommend looking into whether you can get pseudonymous information from your system, i.e., so that Student X's comments are all collected together and Student Y's comments are all collected together separately.  That way, you can separate out and discard feedback from the horrible person (essentially marking them with a "troll" filter), while still getting meaningful feedback from other students who have more positive disinhibition from the anonymity.
It's a pity that some people are horrible and try to screw things up for everybody else; the rest of us need to try to figure out how to appropriately protect ourselves and our institutions while keeping them from profiting from this behavior.

There are a whole lot of whingers out there. The advice given "it's probably best to ignore it and move on" has worked for me. I just filter it out, and concentrate on any good ideas that can help me do my job better - they do crop up from time to time.

"then if there is any way for your IT staff to penetrate the anonymity and investigate, it's reasonable to ask them to do so, just as if somebody had made a serious electronic threat through some other channel"  As an IT professional, I can tell you it is not reasonable for OP to make this request at all.  The request should be made through your institution's security office and accompanied by a formal complaint: this is either a threat, or it's not.  Being hurt and offended is no reason to ask someone from a position of power to violate policy or possibly the law.

@ClintonPierce Your point is well taken that in many institutions there may be a process that means you shouldn't talk directly to the IT staff, and I will modify the answer accordingly. My key point is that a genuine threat of harm in an anonymous feedback form should be treated just like any other genuine threat.

@ALANWARD agreed. :)

@ClintonPierce I agree. There was no specific threat so I wouldn't get IT involved since it breaches the anonymity aspect. Had there been a specific threat (i.e. I will come to your house to do X etc) that's when I'd get IT involved.

@jakebeal I think you make some good points. I don't think I have the option to filter out the trolls like you suggested, but might continue with the anon feedback anyways and just see how it goes.

You must die! You're so cool! These are, at the two ends of the spectrum, example comments that I have received from students along the years.
You will find students who love you, those who like you, those for whom you are indifferent, those who dislike you, and those who plainly hate you. This regardless of what you do as a teacher, that is, even from week one of the class.
Those who love you will deliver overly enthusiastic comments; those who hate you, protected by the anonimity, will insult you in the worst possible ways and will wish you all sort of bad things.
My advice is: don't be too pleased by the former, and don't get sinked by the latter. In particular, for what concerns the abusive comments, unless they hide a real threat, brush them off and move on.

Agreed. I'm not sure if it's just a disgruntled student who doesn't like the approach of the unit or what the issue is, but I think this is the best way forward as others have said.

The only solution is to stop anonymous feedback comments. One can have anonymous polls for specific questions. But comments should not be allowed. This is simply mean.

A short answer, but I believe that it answers your question:
Threats of violence correlate with, but don't cause, violence. As such, eliminating the medium that enabled that student to anonymously communicate threats of violence, as you have proposed to do, does not reduce the probability that one of them will act violently. However, it does ensure that no student can anonymously communicate useful insights about the course to you.

THAT. Someone won't change their ideas, thoughts, or intentions merely because they no longer can rant via anonymous form. Conversely, there may be a (probably, small) chance that an option to ran anonymously would cause someone with a grief to be less of a threat (since they didn't bottle up their negative feelings; and feel a sense of "accomplishment" over having expressed themselves).

I wanted to chime in with a few other possible routes for getting student feedback throughout the semester in a way that may reduce unproductive/abusive comments.
If you have a teaching center at your institution, take advantage of their resources. I have been at a number of institutions where you can arrange for a member of the teaching resource center to come observe one of your classes. They will then have you leave the class about 15 minutes early to obtain feedback from your students; they will present to you later a summary of useful recommendations based both on their observations and their conversation with your students. This is an excellent way to get feedback during the semester about your teaching.
I have also found that it is useful to get anonymous feedback from my students during the semester but to do so in a more structured fashion. In other words, every 2 or 3 weeks, present them with several targeted questions (so, for example, about the balance of lecture versus discussion, or the content or length of problem sets, etc.).
After each round of feedback, spend a short period at the beginning of class discussing the overall themes of the feedback with the class, and speak concretely to both 1) what you will do to address the comments and 2) what they need to do to contribute to a positive experience. I have found that making sure the students know that you are taking their comments seriously helps them to take providing feedback a bit more seriously.

We don't have a teaching centre but I will get a colleague to come in and provide a review. I think your second option might also be useful, but will be difficult to do with a class of over 100 with only 2 hours per week. When soliciting the feedback, did you just ask them these questions in the class? How did you approach this? Because I did do the anonymous option but this has instead left me open to a string of abuse and wasn't particularly helpful.

Assuming that no direct threats were made and that you do not personally feel threatened, I would attempt to show that this attempt to troll you and make you feel bad was unsuccessful and that you actually found it amusing. Don't show weakness to students who want to hurt you.
I would show the comments on a PowerPoint slide, at the start of a lecture, and say something like "Hey everyone, we've got an anonymous feedback form, let's see how that's going.' Then present the feedback (I'd leave the profanity, etc, in there, but you might want to 'beep' it out."
"Hmmm... We've clearly got some strong opinions. Indeed, it appears that I am a ******** ******** *******. That's kind of a broad description, which isn't so helpful. How about we try to make things more directly relevant, for example, you might write 'If the PowerPoint slides were different colors, then you would not be such a *********** ******** *******."

I quite like that approach.

Anonymous feedback is useful, but doesn't take the place of gaining enough trust from your students to get direct feedback.  I've found the best way to get that trust is to tell them what's on your mind.  If you're straight with people and let them know that you care, they'll usually let you know if they're unhappy.
I was teaching a beginners' programming class a number of years ago, and when midterm came around I got all sorts of negative feedback.  Some was very helpful; practically everyone told me to stop telling them how much time they had left every half hour.  (Sorry, I really did that.)  They nearly all complained that the test was too difficult and they were worried about their grades.  I told them I would grade it on a curve, but I also told them them that I didn't think that they were there to get a good grade so much as a job, and if they couldn't handle the material in the test I wouldn't be willing to hire them.  Funny thing, they did a lot better in the final!  :)
The point is that it's important to make it clear that students have a say in their education, and that if they have something that they want to say they will be listened to.  That way, they will often listen to you as well.  If you keep fast to your goal of seeing that they learn the material as well as possible, and keep in mind that liking you is secondary to that goal, then you'll probably find that most of the students like you anyway.

Anonymous, open-ended comments are one of the most effective forms of feedback as it regularly produces honest and useful information. Don't stop using it just because one or two individuals misuse it.
I know work is being done to automatically censor personal attacks in student feedback. If you're collecting responses to start/stop/continue-questions you should check out hubert.ai They have a solution that prevents foul comments to reach through.

Since your ego is involved, I think you need to have someone in between you and the comment submitters to do some screening.  One way would be to ask if someone in your department administration would like to be the one to be be checking the comments.  Another way (but this is not an online method) would be to hand out feedback (paper) forms once a month or so in your class.  If it looks sort of official, and if you take class time for this, I doubt people would write silly things.

"it needs to be carefully moderated" - No it doesn't.  If the feedback is not displayed to anyone else (and is only shown to the instructors), there is no need for it to be moderated.  I think your answer starts from a hidden premise.  Many instructors provide channels for anonymous feedback that won't be shown to anyone other than the instructor, and in those cases, this answer will be inaccurate or unhelpful.

Only I and my teaching assistant have access to the feedback, so there's no commenting etc.

Thank you for the clarification.  I have edited my answer.

@aparente001: alas, you probably underestimate the willingness of certain people to write silly things. The examples I gave in my answer were written exactly in the kind of setting you are proposing: the feedback forms were officially prepared by the faculty and handed out in class (now we have online forms, instead).

@MassimoOrtolan - All the more reason to set up a go-between, so that you don't have to read the nonsense and abuse.  Perhaps you could work with a buddy faculty member, and screen his or hers, while your buddy screens yours.  (I'm suggesting this in response to what you said in your title, about "moving forward.")

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
Who cares about your PhD thesis?
I'm pursuing a Ph.D. in a computer science-related field (more particularly, in Artificial Intelligence). During my studies, I was involved in several projects, some of them got published in top-tier conferences. I'm ought to submit my Ph.D. thesis this year and was wondering what's the role of theses these days in CS.
Generating a polished thesis would take at least two months. However, collecting the work we've already published into a huge Latex file with a shared (newly written) introduction and discussion would take one week. I believe I can use the spare time to advance my research even further or start new collaborations.
If that matters, my career plans are to start a postdoc position, followed by seeking an academic position.
So, is there any significant downside to hastily constructing a "sandwich thesis" as opposed to carefully polishing a cohesive document? Assume that my committee would accept both.

If I am right, you are asking the choice between "generating a polished thesis" and "collecting the work we've already published into a huge Latex file". I think the answer depends on your institution’s regulations, the exact contents of your work or your personal values, thus off-topic on our site.

OP (original poster), your seem to be describing the difference between a "cumulative PhD thesis" and a classical "book-like PhD thesis". Could you clarify whether this is the case in your question and mention whether your PhD examination reguations allow cumulative PhD theses and whether you satisfied the formala requirements to writing one already (there may be a minimum number of peer-reviewed papers for this route)?

You might be interested: https://academia.stackexchange.com/questions/124689/whats-the-point-of-phd-theses-if-nobody-reads-them

Does this answer your question? What is a “sandwich thesis”?

I think the underlying question ("is a hastily-written thesis likely to negatively affect my career?") could be on topic. Edited the question to reflect this.

Also, you may want to specify your country -- some countries give "grades" to PhDs, while in the US, there is no such formal distinction (we instead rely on letters and publication counts).

Well, my committee chair cared a lot.

This depends entirely on the culture of the field and the department. In my department, sandwich theses were the completely accepted norm, and no one was expected to spend more than a couple weeks writing their thesis (mostly just the introduction and maybe some short transition material between chapters).

@tparker Well that is pretty horrific to hear. "Sandwich" theses written in a "couple of weeks" would not be looked upon very favorably in pretty much any other area of Math, Science, or Engineering that I am aware of, but CS folk here seem to be fine with it. Another data point for my long-held hypothesis that "CS is a different country".

@user_of_math My thesis was in physics, not CS.

@user_of_math Looking at the comments to the question linked above, it seems that this practice is common in many fields and countries.

@user_of_math That does not make it any more palatable than practices like University Presidents earning more than a million dollars per annum or any of the numerous other abominations that plague academia. Anyhow, I should get off my soapbox now.

A well written thesis is one of my favorite things to read. They are a good way to get an idea of a subfield you are not directly working in.

If your work that was "published in top-tier conferences" was indeed published (and not merely presented at event), you might no longer hold copyright for that work, so you can't legally put it in large LaTeX file with introduction slapped on top. This is one of multiple things to consider in that situation.

I think the OP greatly underestimates the time it takes to even write a decent "sandwich" thesis. The person you need to ask is your supervisor. They will care, as finding examiners is not easy and if they get a reputation for sending out badly written theses for examinations, no-one will want to burden themselves with that.

Now that this question has been edited to have an actual answerable question at the bottom, maybe someone can [edit] it to give it a less cantankerous title?

If you feel that taking a week out of your research time will significantly delay your progress, then you must be the future Erdős or something for AI. My guess is that your entire life is going to be without a family, friends, vacations, or leaving campus on unrelated things all that much. But thanks for advancing science! In case these things do interest you in some way (having friends and family, etc.), then you can surely take a week out of your time to finish reworking your thesis.

I am a material scientist. My PhD dissertation is a combination of my journal publications. I know nobody is really interested in reading my dissertation, not even my committee members had carefully read it because they knew each chapter was published expect spending a bit time to read the literature review and told me some grammar issues to revise.

Who cares about your PhD thesis?

At least your supervisor and your examiners, and potentially anybody seeking to evaluate your work in the future.
The primary reason to be careful with your PhD thesis is to avoid giving a bad impression to your PhD committee. Even if the content is solid, it would be unfortunate to be given major corrections or a lower "cum laude" honor (depending on the local usage) just because a committee member felt that you didn't do much effort with the dissertation.
It is true that most dissertations are not going to be read by many people, but your PhD dissertation is going to be a quite important part of your profile for your whole academic career. A reviewer for a hiring committee might have a cursory look at it, a student considering working with you might be curious about it, etc. Of course it's unlikely to be a major issue, but is it really worth the risk? Even simply for yourself, isn't it worth spending a bit more time in order to have a document you can be proud of in the future?

Your answer contradicts the more practiced policy among universities to allow and foster "cumulative dissertations".I don't know for which reason they do, maybe because in publish or perish times much more papers per PhD student are published than in times in which a traditional thesis was mandatory. But the contradiction has somehow to be resolved, when students are offered the option to submit a cumulative dissertation.The ideal picture you give and I subscribe and underline also in my answer seems not be anymore the favored or necessary one among universities?!? Only academic track PhD's...

@user48953094 I've heard about it but this option is not offered in my institution (at least not in my school) so I didn't want to talk about something I don't really know. In any case I think that the PhD student should ask their supervisor what they think is best.

@user48953094 cumulative thesis and polished thesis do not contradict each other. Even if you build your thesis from your papers there is typical a lot polishing you can do. Likely your papers will introduce similar concepts each, so one big polishing issue would be to a) merge their introduction, their background theory, notations and definitions, examples etc. such that there it's consistent in usage of introduced terms and notation. Then there is b) to wrap them if possible in a common story and explain why they matter in the grander scheme of things and how / if they are connected.

Also, while theses may not be read as often as papers (not sure about that even), my impression is they often are read a lot more often by the same person and with an interest for detail compared to papers. I've used a few PhD theses like you would use books - as reference introductions to certain topics. Many papers are just skimmed for the essentials, PhD theses provide all the nitty gritty details.

"Cumulative dissertation" is not a synonym for "stapler thesis".  The latter is only one species of of the former.

@Frank Hopkins: one big polishing issue and they are often read a lot more often --- This "summary thesis" is a nice example, one that I have found very useful for some topics I'm interested in. This is another excellent such example.

Nowadays some universities offer a "cumulative dissertation" (putting your papers together in the journal style with a introduction at the beginning of the dissertation) additionally to the traditional thesis publication format. I would ask your supervisor, if he allows and recommends this based on your publication track (number, extent, content, depth, siginificance, originality of your publications), as only he can likely make a good judgement here.
Apart from this, if you really think/know not much people will care about the  context, background, methods of your research, this might be a reason to favor the cumulative dissertation. But I read during my PhD many PhD thesis of predecessors in the field to grasp above three points, as in papers details often have to be left out. Some thesis get also published as book by springer & co.
I don't think it can be a downer pursuing professorship and the institutions you apply for also allow a cumulative dissertation.
The PhD thesis gives you also a training effect to publish books or review articles, because of more similar format style like a journal article or letter. And to become a professor such publication contributions to the community can be very advantegeous to get tenured.
A PhD thesis also gives people an inlook how you were/are working, how did you approach a scientific question. As many papers are the work nowadays of many co-authors and collaborators and many PhD's are produced, a non-comulative PhD thesis might become very "trendy" and advantageous again to have over competitors.

Nowadays some universities offer a "cumulative dissertation" — At least in computer science, which is OP's field of study, I believe "some universities" actually includes every university in North America and Europe. (Perhaps the UK would be an exception, if not for recent political events.) I don't think I've seen a PhD thesis in computer science that wasn't a cumulative dissertation in decades.

@JeffE Why would "recent political events" influence whether universities allow cumulative dissertations? UK universities tend toward a full thesis, at least in the disciplines I know.

@awjlogan the UK might (no idea) not be as much on board with cumulative theses. If that's the case then one would need to mention them as an exception. Given recent events, one might follow the English approach* to not consider the UK part of Europe and just not mention them as an exception when going with "all of Europe". To be clear and less excluding one might say EU, but alas...
*In England I've indeed sometimes heard the separation between 'us' vs 'Europe' or 'us' vs. 'the continent'.

@FrankHopkins - Degrees are not granted by the government, they are granted by individual universities. It is entirely up to them to decide on what basis they award a degree. Universities are overwhelmingly opposed to Brexit. "One might follow..." is an illogical statement, and has no basis in reality. To be clear, I in no way support Brexit, but please stop making things up about it - this is a topic that has nothing to do with it.

@awjlogan neither did I claim that the government would grant phd theses nor did I try to reason whether the UK is or isn't part of Europe (geographically it is), but you are right that this is off-topic.

@FrankHopkins: You're mixing UK and England, but AFAICT higher education isn't even a national responsibility in the UK. Scotland has devolved powers so the Scottish and English systems are distinct.

@MSalters Yes, strictly speaking I should have said Great Britain or the individual countries on the European continent, though I merely kept on using the same rough reference that JeffE started with.

The main issue I see with this plan is that the papers you already have worked on have been collaborative efforts with other people (your use of 'involved in' and 'we've'). The PhD needs to be your own work, to demonstrate that you personally can work to a level required to attain a Doctor of Philosophy in your field. This means producing innovative research independently of others, despite working in a team environment.
In Australia at least, there is a growing trend for PhD by Publication. I suggest you speak to your supervisor about this (or find out what the local version of the term is; you didn't specify your country of origin). The logic behind it is that if you have a number (typically three) publications which have been written by you, and independently peer reviewed by reputable reviewers, then this amounts to the PhD. This typically means publication in Q1-rated journals; conference papers typically are insufficient without a formal peer-review.
Students like the PhD by Publication approach because its hard for the examiners to refute anything which has been peer reviewed already. Some supervisors also like it, although others like mine see it as the lazy route, both for the student and for the examiners, as some of the latter may not even read those peer reviewed parts. Personally I tend to agree with the latter: in a 3 year PhD, it means getting a fully peer reviewed publication produced every 12 months. Typically the first 12 months are spent reviewing literature and deciding where your project will fit within the greater field of research, so it's hard to produce good, PhD quality research in the first year, although it sounds like you are nearing the end, so this may not be relevant.
In a traditional thesis (at least in Australia), you are required to include a List of Publications which you have worked on (this is independent from the usual bibliography section). The administrative staff at my university have recently made it a requirement that you must include the percentage of your contribution within this list, which needs to be quite detailed: you must list that you contributed 10% of the introduction, 20% of the content, 5% of the results, 10% of the conclusions, etc. The point is that they really want to make sure that what you have included in your thesis is 'your' work. This can also have consequences for the bulk of your thesis. In my case I'd written a paper on process control, and collaborated with another author who'd added some computer simulations. Although the system itself was entirely my work, I had to completely rewrite it and rearrange the model for the thesis so that it was distinct from the paper, to remove any question that I'd included research that wasn't my own in the thesis, or plagiarized a published paper (even one I'd collaborated on). The thesis certification page at the top of the submitted thesis includes the words 'This thesis is the work of authorname except where otherwise acknowledged. The work is original and has not previously been submitted for any other award, except where acknowledged.'
So in short, simply collecting your team's work into one huge latex file is unlikely to qualify as a PhD thesis.

A thesis is a whole different document than a collection of papers.
Over the course of your Ph.D., you have arguably worked (and published) on a number of different projects and topics that are likely to be only sparsely related to each other. While your narrow academic community will certainly draw more benefits from your papers than from your thesis, you will learn a lot by writing a thesis.
Writing a cogent thesis that seamlessly connects the dots between the different topics on which you worked is hard. But it helps you immensely in rationalizing about your specific contributions, how it affects your field, and more importantly, what you have accomplished and how you can move on with your life. Is this field worth exploring? Were the techniques you used useful? Are there other promising approaches? Is the problem still relevant? What did you personally gain from everything you did? Are your multiple projects consistent with each other? Would you recommend people continue working on what you worked on? How? Are you contributing to society in a meaningful way? Are you advancing knowledge? How? Are there specific avenues that you didn't pursue (perhaps for the lack of time or previous knowledge) that look promising? Thinking rationally about these issues will train you in skills that will be relevant to your future, especially if you want to continue being a researcher. A great way to think about these things is by writing them on paper, which is what you will likely do for the rest of your life (as grant proposals, executive reports, white papers, etc).
Remember that working in the lab and writing papers is only a very small part of your training as a Ph.D. student. In fact, in a few years you are, statistically speaking, likely to never work in the lab, write copious amount of code or write papers in full again for the rest of your life. But you will have to continue thinking rationally about your's and everyone else's reseach and work, and that's what you learn by writing a thesis.
PS: There are also additional benefits to writing a good thesis, such as providing additional methodological details that might be useful to the community and (most importantly) to your labmates and to the people who will use the same equipment/techniques as you did in the future.

I would recommend the latter option, but taking the introduction a bit more seriously.  One week seems awful short to do a good job.  Think about it carefully and spend two or three weeks to write a good introduction for a broad audience which includes more background.

I, for one, agree with this answer and think it’s sufficiently verbose. A single week to write a good introduction and conclusion is awfully short.

Depending on field, there can be substantial downsides, because academic papers and PhD theses are fundamentally different beasts.
There are certain things that people just don't publish in academic journals, but that is nevertheless useful information. Experimental or theoretical failures is the obvious one that everyone knows about - it's much harder to publish an article about "I tried this and it didn't work", than "I tried this and discovered something cool". Further, journal papers (which are not called "letters for nothing") often have stringent word, page, figure, and even citation number limits, forcing academics to pick and chose only the most critical information to include. That excluded information is often to be found in PhD theses, where those limits don't apply. If it isn't, then it's lost forever, pretty much.
Speaking for my own field (experimental atomic physics), I go looking for academic papers when I want to study advanced scientific topics - because there is nowhere else to get most of that information - but when it comes to technical details, long-form PhD theses are second only to informal, personal discussions with other people doing nearly the exact same thing as you.
"Technical details" is, itself, quite a loose term, but for me that has included things like:

Specific part numbers - "Part X from company Y achieves the best performance of all 18 options we tried"
Unexpected failure modes -"this type of glass suffers from thermal lensing, so you have to have windows made from this other type of glass"
Undocumented specifications - "Part A from company B is specified to do P, but can also do Q"
Unknown suppliers - "samples from "
Techniques that didn't work and so were not published
Techniques that did work, but weren't published - you'd be surprised how many of these there are.
Alternative solutions to expensive hardware - "you could buy the all-singing-all-dancing computer controlled widget from company X (that you don't have the budget for), or you can build your own with an Arduino, this code, and half a day's soldering"
Simple solutions to problems that nevertheless would take a considerable amount of time and error to identify yourself - "you need a 4th order high-pass filter for this signal because a 2nd order can't solve interactions with this other effect"

Off the top of my head, my own thesis cites something like 40 other PhD theses for useful information that saved me time, energy, and my supervisor's funding. That was out of 250-300 references in total, so 15%; the vast majority of the rest would have been journal papers.
Nobody likes writing a PhD thesis - and it's true, very few people will ever read them compared to, say, Lord of the Rings or Harry Potter - but it is a valuable source of information which never makes it into published papers. Think about all the things that you have learned over the course of your PhD - if you never write it down, you may as well have never learned them.

I agree with other commenters that, in general, not a lot of people will care about your thesis, per se.  However, you need to also consider what job you are wanting in the future.
If you are applying for academic jobs, it will likely be under much greater scrutiny, particularly by your potential postdoc advisor (I always read the thesis of postdoc applicants to my group and I know many other professors do the same).  For one, it will demonstrate whether you have the tenacity to conduct long, well thought out research. Second, it will demonstrate whether you can effectively communicate that research.  Third, it will demonstrate if you know how to write a thesis and, therefore, have a chance of mentoring your future students through the process.  Note that even postdocs are asked to mentor students through the process, particularly in larger research groups.  If you have not gone through the long, meticulous process of writing out the thesis, you will not be qualified to do this.
Your statement "Generating a polished thesis would take at least two months," gave me pause.  Yes, that is true.  Probably even a bit longer than that.  But this is what a PhD actually requires.  Getting the degree demonstrates that you had the tenacity to go through it.  And there is nothing wrong with NOT wanting to go through it!  But if you don't want to put in those few months, then maybe you might want to consider going to work in industry with the master's degree?
You might find universities that accept stacking several research papers together into one thesis to be acceptable.  My university does not, however where I got my PhD did.  At the same time, while it was an accepted format, I knew someone who used it and their committee tore it apart and they basically had to rewrite the entire thing after their defense.  The reason for this is that most professors wrote their dissertations in a time before universities considered it an acceptable format.  So they didn't consider it a worth while option, even if the university did choose to accept it.  I would make time with your PI and committee to discuss the format, if it is an option for you, before you make this decision.

Short version : no one. How much you care for it is something, but the supervisor or the examiners couldn't care less. I'm a math and computer science major from Romania if this info is worth anything to you.
I say all of this because I've experienced it . So , I've been working on my application for the thesis for a year , a hole year. The application wasn't some website like the majority of my schoolmates did, it was an app that could read a barcode with the help of a usb scanner. I've built everything from scratch with libraries, made it so there were almost no bugs, worked like a charm and did more than half the app before finding a supervisor. He didn't look through the code, not even a glance. I other words he didn't care about the code.
On the written part I've worked about 2 months . About this one too i was pretty proud. All the parts fitted together nicely and didn't have filler info. The supervisor read through this, several times.
On the presentation day, the examiners flipped through the written part just to see if i got any interesting images , asked something about the part of the app that involved that USB scanner and the code of the reports and that was it. Next. All the work i've done over that year was reduced to them to just two chunks of code. Nobody cared that the app was nearly perfect, nobody cared about the written part.
So my advice is don't put too much work in it if you intend to leave the project just as the thesis, ultimately nobody will appreciate for what it truly is

Hi and welcome to Academia SE. Please note that the OP is talking about a PhD dissertation: what you describe, instead, seems the situation of a bachelor's or master's thesis, which is quite different.

@MassimoOrtolano thank you for your observation. Unfortunately the attitude still persists to the dissertation either

Let me answer by focusing on the purpose of the thesis, derived by the purpose of a PhD.
Disclaimer: This is an opinionated answer, I haven't done research in CS, and I don't have supervised PhD students — so take my word  with caution.
I believe (p. xiii) that:

The purpose of a PhD is to train a research student as an autonomous scientist and a good researcher — i.e. as someone deserving the grade of Doctor.
The objective of a PhD thesis is hence to demonstrate to one’s peers that its author can be considered in this way by reporting the successful completion of a high-quality piece of research.

Side note: don't get me wrong. “Successful” is here about the completion of the project, and not the research itself: I do believe that if the research question was relevant regarding the literature review and the experiment well designed and implemented, the project is successful even despite null/non-significant results.
So is a sandwich thesis OK?
If it consists in multiple papers about research you have done (i.e. you are not simply the co-author that have crunched the numbers, or written the paper without doing the experiments — the point here is to demonstrate that you master all the various aspects of being a researcher: crunching the numbers is one of them, but writing is another, as well as formulating a research question, designing an experiment, etc.), I would consider so.
In such case, I would stress the importance of the global introduction and conclusion, which should highlight how your separate papers contribute to an homogeneous research body (i.e. you're not only grabbing low-hanging fruits in minor papers, but you are able to have a coherent research agenda on the medium/long term).
But as others have answered, your thesis will be one of the key elements used to assess your qualities/skills as researcher in the first years of your career. So delivering something half-baked might not give the best impression.