How to reward students for learning from mistakes without penalizing those who didn't make mistakes in the first place? We're thinking of implementing a policy where if a student asks a question/makes a mistake in a lab, they are required to write down what that question/mistake was and how it was resolved.  These "lab notebooks" (for want of a better word) will be evaluated based on the quality of the responses to the problems, not the problems themselves.  For instance, if the problem was relatively trivial ("We didn't turn the device on") but the response is great ("we came up with a checklist that we will follow to ensure that the basic setup is done every time") then the submission would be evaluated with a high mark.

For the majority of our students, there will be more than enough stuff in each lab to write about.   However, this isn't universally true and some groups will do the lab just fine without asking any questions.  Whether they make mistakes or not is an open question, but there really are some groups who just get what's going on, take their data and do very well without needing to interact with us at all.  And there's the problem - these students would have an empty notebook because they didn't need to go back and check things out.  Would they get a 0 on that part?  That doesn't sound fair at all.

How can I reward the students who learned from their mistakes without penalizing those who don't make any?

At face value, it seems clear that the issue is that you're trying to mix measurements of two very different kinds of performance. I think the REAL issue is deeper than this, but let's start with the two things you're mixing up:

You want to measure outcomes. Did the group end up with "correct" results from the lab?
You want to measure a specific (learning) process. Did the group learn from mistakes and questions?

The first measurement is usually the default in classroom settings because it's the most obvious. The second measurement has value, though: You're trying to prepare students for life, not just hand them a set of answers. Measuring their ability to learn, and encouraging them to be learners, is important.

That said, measuring outcomes has the advantage that it's not dependent on the process. It's easy to see if the lab result is correct or not. I don't think that's the focus of your question, so let's concentrate on the second concept - measuring the learning itself.

That brings us to the core issue with your proposed process: Measuring learning from mistakes and questions is very, very different from measuring learning in general. You hinted at this when you said,

  For the majority of our students, there will be more than enough stuff in each lab to write about. However, this isn't universally true and some groups will do the lab just fine without asking any questions.

The issue here is that you've singled out a single type of learning to measure. The students who learned by failure and questioning are correctly being evaluated by your grading approach. This is good - you're encouraging that type of student to hone their skills. So let's look at the second group - the students who "did the lab just fine without asking any questions." The important thing to remember is that they weren't born knowing how to do the lab - they learned it too, just in a different way than the question-askers and mistake-makers. The reason why your new approach lacks fairness (as you identified yourself) is that it's only attributing value to one type of learning.

So - is it a bad idea to grade your students based on the questions they ask and the mistakes they make? If you're trying to grade them on their learning process, which is what I think you're trying to do, you need to make sure you're accounting for all types of learning not just the one style. This will be really challenging - people learn in multiple ways and transition between learning styles in a fluid manner, and many learning styles don't leave behind a "paper trail" you can measure after the fact. So let's get to your specific question:

  How can I reward the students who learned from their mistakes without penalizing those who don't make any?

I may be frame-challenging your question, but if you allow me that liberty, I would rewrite it as:

  How can I encourage students to be aware of, and work on improving, their learning skills, versus just rewarding them for getting the right answer to a specific assignment?

I think the answer to this will be much broader than a single stack exchange question, but some ideas to think about:

Include a discussion/presentation during/after the lab where a selected group walks the other groups through how they approached the lab, with a focus on how they prepared or learned the material beforehand and how they dealt with issues/challenges.
When a group asks questions during the lab, let the other lab groups answer it (instead of you). When an answer is given, have the answering group provide explanation of where they got the answer from.
Include an entire lab session, or elements of a lab, that are not part of the graded solution and are deliberately "unanswerable" in that they cover material you haven't taught or are otherwise deliberately difficult. Have the students come up with their own way to resolve this. This could even be a lab where the process intentionally causes failure, which would "force" the students to use your "learning notebook" as a tool to document and explain the failure and their approach to solving it. Purposefully cause them to go off-script and respond to the failure with creativity.

Basically, you need something that accounts for multiple learning styles, and something that encourages accountability instead of just allowing students to "cheat the system" by pencil-whipping busywork in order to get a grade. Notice I left your word choice of "reward" out of my re-worded version of your question - I think awareness and demonstration are probably more important than a reward - at the least because the reward just causes students to focus on the end, rather than the means, which totally dodges your goal of getting them to focus on learning skills versus just a final result.

Edited to add:

Let me further add: I don't think the "learning notebook" is a bad idea. It actually sounds like a great way to encourage people to recognize failure and accept it as part of a (positive) learning process, versus being afraid of failure. It would be great to include along with my three suggestions above. That said, I do think it becomes cumbersome to try to include it as a graded item, and more importantly, regardless of whether you grade it or not, focusing on it (as a graded item) misses the larger opportunity to consider and encourage other learning styles.


In a mathematics publication, is it preferable to state all definitions immediately, or as they arise? I am writing a mathematics paper for publication which frequently:

Uses special functions the average reader would not be aware of.
Uses my own notation.

Within the first section of my paper (introduction/preliminaries), I define the relevant special functions. Should I also define my own notation within the preliminaries, or wait until its use naturally arises within the paper to introduce it? My notation can be understood without context, though its usefulness will obviously not be clear.

There is no general rule. Different authors have different opinions about this. To my knowledge no mathematics journal has guidelines about this sort of thing.

A rule of thumb could be that if your notation is going to be used throughout the paper, then it should be introduced at the beginning in a special section called "notations", "conventions", "background", or however you want to say this. If the notation is only going to be used locally in some section or even one proof, then I would introduce it at that point.

Another possibility is to make a categorized table of notations at the beginning or an alphabetical list of notation with references to where the object is defined, if your paper is long and complex enough to justify it. This can of course be combined with the previous rule of thumb.

I would recommend against making the "background" section as part of the introduction. The introduction is supposed to be something that can be read by almost everyone slightly interested in the paper, to know what the main results are and how they fit with the general literature, and in case of stars aligning, whether the paper is worth reading. (The abstract is to decide whether you'll even glance at the paper at all.) Keep it short and simple and avoid introducing too many things at once. In my opinion it is a mistake made by many younger, and also more experienced authors to make the introduction way too technical.


Overly long review process (>2 years) without feedback We submitted a paper to a journal more than 2 years ago. After 1 year had passed without any news, I asked the editors for an update on the status of our paper, and they mentioned they were still waiting for referee reports. After the second year of waiting, I wrote another email about a status update, without getting any response from the editors. The long process is a real nuisance for all authors involved, in particular for the junior ones who need publications on their track record. This is why I would like to see a decision (positive or negative) soon, but on the other hand I would like to not offend the editors. What would you advise us to do in this situation?

Perhaps the time for being polite is past.
Ask them for a realistic projection about a decision. Perhaps even mention the impact of the delay on the careers of junior members.
There might be reasons for a long delay, but not for a long time between communications. Don't ask for an immediate decision unless you want to risk the inevitable rejection.
But it is their responsibility to choose reliable reviewers.
But if your paper is especially esoteric, making it hard to find suitable reviewers, then starting over will also lead to long delays. If that happens, keep a tighter rein on the process.


Is publishing runnable code instead of pseudo code shunned? In Computer Science is it preferable to describe algorithms using pseudo code rather than real code? If so, why?

I've talked to a few academics who think so but I can't understand why. Some of the arguments I've heard:

"Pseudo code is shorter."  Given a modern language like Clojure or Python, the real code is often not much longer. Even if it is, (electronic) paper is cheap.
"Not everyone can code in Python."  True, but well-written Python code isn't harder to read than pseudo code. Plus, Python is standardized - someone's personal pseudo code isn't.
"In CS, we focus on algorithms - not implementations."  True, but also showing implementations doesn't take away from that focus.
"It's favoring one language over another."  True, but that is a right I as an author have, isn't it?
"It looks amateurish."  That's subjective.

The main advantage with real code is that you can run it and see for yourself if it works or not. You don't have to wonder whether you have made a mistake in implementing the pseudo code.

So what are the arguments for pseudo code?

To make it clear what I mean by real and runnable code, see the code in the following articles:

An Effective Implementation of the Lin-Kernighan Traveling Salesman Heuristic
Fast Minimum Storage Ray/Triangle Intersection
Watertight Ray/Triangle Intersection

The first and second contains C code. Helsgaun claims it is "c-style pseudocode" but it is to my eyes almost indistinguishable from real C code. The difference I can see is that it uses α, β and ∞ as variable names. The third contains C++ code.

There are cases where real code is preferable, and cases where pseudocode is preferable. You shouldn't rely on a simple iron rule, but rather on judgement of what is appropriate to the situation.

Some things to consider:

Programming languages come and go. In the 60s, Fortran was considered a really nice and readable programming language, much easier to read than Assembly. But if you'd written an article using Fortran code samples instead of peudocode, it would be harder to read for us now. Right now, Python looks pretty good to us, but will it still look that good in the future? If I handed you a piece of Python code with the following code in it:

a = 3 / 2

What is the value of a? Is it 1 or 1.5? Because Python 2 and 3 handle integer division differently. Now, I've gone more or less native programming in Python 3, so I actually had to look up which of the division operations in Python 2 and 3 are different and which aren't. Just to show you, using real code in a paper may reduce the shelf life of your paper.

Pseudocode lets you abstract stuff away In pseudocode you can just state something like:

WHILE stopping criterion not reached DO
    (stuff)

And then later on in your paper you can argue about different possible stopping criteria for your algorithm. You could do that with actual Python code too, but the result would be basically that you're twisting your Python code to do what pseudocode does by nature.

You can be pretty standardized in pseudocode Just use the various algorithm typesetting options for LaTeX.

You can use mathematical notation in pseudocode Using mathematical set notation is a lot more universal than relying on all of your readers understanding Python set operations. Consider:

a = set([1, 2, 3])
b = set([1, 2])
c = 1 if b.issubset(a) else 0

versus

A ← {1, 2, 3}
B ← {1, 2}
C = 1 if B ⊆ A
    0 otherwise

Someone not familiar with Python looking at the first example will be wondering: is [1, 2, 3] a ... maybe a list? Well the list [1, 2, 3] isn't the same as the list [1, 2], so the set A and the set B contain different elements so B can't be a subset of A.

Algorithms vs. implementations Suppose in 2019 you write an interesting algorithm in Python 3 using some state of the art libraries. In 2025 I come up with an alternative algorithm for the same problem in Go and want to compare performance to prove that yours is better. To get a fair comparison, I'm going to have to implement my algorithm in Go or yours in Python. Suppose by then nobody uses Python for high performance stuff anymore because Go does it better. (It might, I dunno.) Now I have to go research the seven year old libraries you used to find out exactly what functions you used and which Go functions are equivalent to them. That's very hard. So quite likely, the Go implementation I make of your algorithm won't be all that good. And big surprise! My algorithm benchmarks better than yours!

Now instead if you'd used an implementation-independent description of your algorithm, things might turn out better for your publication.

So the two big disadvantages of using real code are: it limits the shelf life of your publication, and you reach a smaller audience.

So when should you use real code?

When the topic of your paper is not the algorithm, but the implementation or the programming language. Maybe you're trying to show that Python is a really good language in which to solve problem X because with libraries Y and Z you get an easy and efficient implementation.
It's absolutely encouraged to also publish your real code as an appendix or, better yet, in a repository where people can download your code and suggest improvements. A nontrivial algorithm is probably too big to copy by typing by hand or even copy-pasting out of a paper anyway. As soon as you start getting into something like a new Deep Learning algorithm, you're probably looking at multiple files or even nested packages.


How to filter data obtained with practical measurements and simulations to be concisely put into a paper? I'm a PhD candidate in Computer Science and I'm working on my first paper that should be published in a peer-reviewed journal (until now, I published only papers in conferences).
My project is a FPGA memory module, which uses a specific approach (the main idea) to obtain better performance in terms of access philosophy and capacity.

Now, the mentioned idea is naturally the bulk of the paper, with the math, proofs, algorithms, etc. behind it. But, since the module was synthesized and tested, I feel obliged to address its obtained "measures".

The process generated quite a lot data which become even more after the simulations were done. So, I'm not sure what charts/tables are appropriate to analyze and publish in a paper? (e.g. input->output delay for a range of inputs; parameters like max clock, number of logic elements; perhaps even (parts) of the schema; etc.)

As this seems a bit specific, I would like to broaden the scope of the question. Namely, the general issue here is how to filter data which does not directly contribute to the understanding of the main idea, but is the result of a finished project and can be used to reinforce the researched concept with practical measurements and simulations. This obtained data is large, so I'm asking for guidelines what could be considered concise enough to put into a paper.

Read a couple of dozen papers that are similar in nature (that is, in terms of the method and how the research question is addresssed) to yours and are in your target journal. Look at how they've supplied the sort of detailed information you're talking about.

As a rule-of-thumb, put it all into supplementary information. And then follow the advice of your edit and peer-reviewers, if they advise moving things. If there are particular numbers that contribute to your main narrative, put those numbers in the body of the paper, too.


What can a student in England do, if her Disability Office refuses PDF versions of textbooks? My sister is an undergraduate at a university in England, and emailed its Disability Office  (let me abbreviate this to DO) a letter from her physician stating that disability thwarts her from carrying books (like her many heavy textbooks), because a publisher suggested PDF Accessible Formats of books that she must request through her university's DO.

But the Head of DO informed my sister that he doesn't consider her to have 'print disability' or to qualify for PDFs, probably (we are guessing) because he doesn't want to spend time obtaining the PDFs for her. He has offered nothing else useful for her.

We think that he's wrong, but how can she acquire the PDFs?

I have an alternative take on the circumstances.

From Wikipedia, a Print disability refers to an impediment that prevents the reading of printed material. Presumably, the PDFs the disability office has are intended for students that need to use some other method, for example: 1) a text-to-speech program, which can't operate on the printed books, or 2) a digital display for a student with a disability that prevents them from turning pages.

It sounds like your sister has trouble carrying physically demanding loads, like a heavy textbook, but that doesn't mean she can't read a textbook that is sitting in front of her on a table. If this is correct, indeed your sister may not meet the definition of "print disability" that the DO is using.

Although the publisher sent your sister to the DO to obtain PDFs, they probably want no part in assessing whether the accommodations are necessary, they are punting it to the DO. The DO, on the other hand, might have a specific licensing agreement with the publisher that only allows them to provide digital copies if certain criteria are met under their definition of "print disability." It is possible that difficulty with the weight of a textbook doesn't fall under their terms.

I would suggest meeting again with the DO before escalating this to other offices, explain the nature of the disability, and see what accommodations can be made. Personally, when I was an undergraduate student carrying around heavy textbooks just wasn't a good idea for anybody. They were rarely actually needed in a course, they were used at home. It might be that the DO would rather provide a physical textbook copy in courses where a physical copy is needed in-class on a course-by-course basis, rather than providing PDFs (which they may not have a legal right to distribute).

Alternatively, as others have suggested, your sister could obtain digital copies on her own if possible.

(just to add: I think it's unfortunate, to say the least, that the DO didn't directly suggest other accommodations. You have a right to be frustrated by that, but I would try to be polite in the short term and try to assume that the DO head is simply busy rather than negligent. DO's could very easily be understaffed. Maybe they truly are just a jerk, but if they aren't and you assume they are, it will make the situation more difficult for everyone. Assume the best, try to be proactive, try to be assertive without being demanding, etc.)


How to focus only on the problem at hand? I am a PhD student designing algorithms for mathematical problems. I have the very bad habit of loosing my track on research problems. Suppose I am working on problem X, then, in the next week, I will start looking at Y,Z and other problems. Due to this, I feel tired at the end of the day with very less productivity with respect to X. I have always fear in my mind of failing, and that's why I try to look at other problems simultaneously. I don't know if it is okay for a PhD student (after mid-stage) to do this kind of things.

Question: How to focus only on the problem at hand?

You are "guilty" of a particular type of procrastination (in turn a type of  avoidance behavior), in which you avoid working on your (self-assigned) task by working on some other task. Ergo, the usual strategies to counter procrastination apply. There are several examples on this site and elsewhere online; my preferred method is to combine the pomodoro technique with a progess tracker.

Pomodoro technique : Work on your task during limited and manageable time periods by setting a timer, e.g. 25 minutes, then force yourself to take a break of at least five minutes even if it completely disrupts your flow. This limits fear and thus avoidance, and it creates motivation to get back to work after the break. Don't allow yourself to work on your side-gigs until four (or so) pomodori are completed, but then go ahead and indulge.
Progess tracker: Use a 'done' list, an online habit tracker, a spreadsheet or whatever works to track each completed pomodoro. This creates motivation, by seeing your success, in particular if you accomplish not  to break a streak, and facilitates habituation.

If your problem to focus disappears, you may want to be more liberal with the breaks between pomodori, so they don't disturb your flow, use a single pomodoro just to get you started, or do away with them all together.

As a side remark, it's good to work on several projects, but you should prioritize and stick to priorities.


First postdoc as "the big filter": myth or fact? I am currently halfway through my first postdoc (a two-year position). I have a Ph.D. in physics, published 3 papers during it, and since this postdoc is in a slightly different field, I still haven't published anything.
I am moderately comfortable in my original field, meaning that if I work for some months on something, it will most likely render some sort of publication (usually in Journal of Physics A/B or Physical Review A). However, my current situation at work is very stressful. I have less freedom than I thought I'd have to work on my own ideas, and the research environment is very dull. I find myself working on problems that might be simply too hard, and perhaps I can't solve them.
I started to wonder what will happen if I turn out not publishing anything during these two years. When I was struggling in my Ph.D., thinking about dropping everything and becoming a monk, I heard from many experienced researchers that it was OK if my Ph.D. was crappy. That the Ph.D. is not the "big filter" in the life of a researcher... And that this is the function of the first postdoc. They told me this is a decisive moment because people will not hire you afterward if you do poorly. In your Ph.D., the fact that you're still more of a student than a researcher usually saves you some explanations, but for the first postdoc, the lack of publications looks really bad when applying to a follow-up position. This is naturally very simplistic (a postdoc might not publish anything simply because his/her research group is non-existent, the boss is crazy, family problems, etc). However, if I were myself on the board analyzing applications, I cannot promise I wouldn't consider the lack of publications as a red flag.
If this filter is true, it makes the situation of someone who just happened to be unlucky in his/her first postdoc quite depressing. Maybe they will have to give up academia and go to industry, even if they don't want to, just because no one accepts them. As I really don't want to move away from academic research, I'm starting to get very anxious about this topic...
Well then... Is my stress justified? Will I be able to find another postdoc after this one even if I don't publish?

Like many other things in academia, this depends.
Having an unproductive postdoc is a point that will definitely come up with any future hiring committee. I’ve sat on a few myself (CS departments) and productivity is considered at every period of your academic life. Of course, if a candidate had a stellar PhD it will make up for it to an extent, but it’s entirely possible that such a candidate will be passed over in favor of a candidate with a less impressive PhD but a more consistent performance overall.
Such “performance gaps” always raise questions - does this indicate an issue with working independently? Working in a group?
Another point to consider is reference letters. Your postdoc host is not likely to write you a glowing letter if you are unproductive, which will hurt your chances of landing a position later on.
That being said, research does not always result in publications. If you made progress on an important problem and have at least a preprint/workshop publication, this may be enough. Hence, your focus should be on productivity rather than on the end product.
A postdoc is also an excellent time to show your commitment to the research community. Organizing workshops, taking on higher-ranking roles in program committees, setting up tutorials, writing a useful blog... All excellent ways of putting yourself out there and making yourself more marketable. Of course - research should absolutely come first, as it is the main evaluation criterion.
Remember - you successfully completed your PhD, you can (at least according to empirical evidence) succeed in this as well! Good luck!


How to define author order of a collaborative paper where all authors have contributed equally? I am finishing writing a manuscript in collaboration with a dozen of colleagues. The paper is mainly theoretical, does not involved experimentation of any kind and is born from numerous discussions between all the collaborators (it is actually hard to tell when the project started). No one really took the lead in the project, at least not during the whole time (for meeting setup and so someone had to take the lead, but only temporary). In addition, the ten collaborators are coming from 6-7 different research groups and universities.

In such case, when everyone as more or less the exact same contribution, what is the best way to determine the author order on the paper, without making any diplomacy misstep?

PS: in my field , alphabetical order is really not common.

PS2: we do not have time for a 25 games croquet tournament...

Since your field does not normally do standard alphabetical ordering, then its use here might signify that something unusual is going on. So you could in principle list everybody in alphabetical order, identify the corresponding author, and then insert a note in the acknowledgments that everyone contributed more or less equally to the paper.

Alternatively, in some fields, papers can be authored by "teams" instead of individuals—although all the individuals participating in the team are cited somewhere. This is more typical when the collaborations run to the hundreds of authors rather than a few to a dozen. If the journal you submit to allows this, it would probably be the fairest option of all.


Is it acceptable to submit a postdoc fellowship application (same lab, same project) to two funding agencies at the same time? I've already applied for one post-doc fellowship with one project with the consent of my possible future supervisor. I will know whether I've been succesfull in 6 months.

Now I realised that there is another fellowship to which I could apply - my potential host supervisor agreed that she would support me for both fellowships.

From my view I see no obstacles to ask for both, nevertheless I want to ask, what would be the opinion of a potential reviewer if he/she will get the same project for review twice from different agencies? If I will apply for both fellowships, both the review process and the results anouncement would run more or less in the same time period.

It's quite common to submit proposals to multiple funding agencies at the same time.  Most grant applications ask you if you are submitting the proposal to multiple funding agencies.  You should note this in your application.  If both agencies are interested in funding your proposal then they'll coordinate awards so that you don't get funded twice.


Should an arXiv paper indicate the conference it was rejected from? We have had a couple of papers rejected from conferences quite a while ago, and we now would like to post them in arXiv. We are considering indicating on the paper which conference it was rejected from, mostly to state when the paper was actually written (we are also writing a new paper with additional results). Is this an acceptable practice?

No, you should not do that. Rejections are not to be advertised. Everyone who will read the paper will be prejudiced against it and you simply do not want that. After all, many papers are initially rejected before being accepted at a suitable venue. You want other people to know when your work got finally accepted and not the other way around.

If you want to establish the initial date of the paper, next time I would suggest to upload immediately after rejection and after addressing the reviewers' comment that lead to your rejection. This way, you have a better paper uploaded, you establish priority before submitting to the next conference and you can cite your work in subsequent publications.


What would a good written policy for scaling exam grades look like? I'm not in the habit of scaling exams, although I'm aware that the practice is widespread and highly recommended in some circles (such as grading on a curve). To my recollection, none of my college instructors scaled exams or wrote any policy about such on their syllabi. I'm wondering if one did commit to an official policy of scaling exams, what would a reasonable written policy look like?

The goal here would (hypothetically) be similar to Krantz in How to Teach Mathematics (Sec. 2.10): "My main goal in formulating my grading policies is to make the greatest number of students feel that they have been treated fairly (and, not incidentally, to reduce student complaints)." Secondarily (and again hypothetically), to defend against possible future administrative complaints that the instructor is not matching some recommended grade proportions (same section by Krantz).

Please assume that the individual exams are adequately fair assessments themselves (not broken, vague, or overly-hard questions, cover same topics covered in class and homeworks, sufficient time permitted, significant advance time taken assessing questions and grading rubric, etc.).

Background: I'm at an open-admissions community college where high failure rates are historically common, often half or more of many math or computing courses. Among the things I'm worried about if I started doing that are: Would there be any lower bound to what might wind up being passing work?

For example, I had a colleague at a different university (top-20 in U.S.) who got in trouble a number of years ago in that, when pressed by students on his scaling policy, said that if every single student got a zero on a exam, then the scaling process would turn all of them into 100% marks. Surprisingly, the students successfully organized a total boycott of the final exam, and my friend followed through and gave 100% marks to everyone as per his word. (This turned out to be quite embarrassing for him.)

So I'm wondering what kind of formal, mechanical policy for scaling would prevent no-lower-bound situations similar to this one?

In general scaling is a mistake as it introduces uncertainty into the student's calculations. But some adjustments are proper and you can publish statements about them.

First, you can agree that no one will "miss" the next grade by a small percentage. This is better done overall, than on a per exam/paper basis. But if it takes 90% (overall) to get an "A", then, at the end of the day give the A to someone with 89. This merely recognizes that your grading scheme isn't perfect and may disadvantage people in small ways occasionally.

Second, agree that your grading rubric is clear that you are allowed to adjust upward when you think it justified. Mine would say something like, "If you get 80% you will get at least a B". This is just a reinforcement of the first point.

Third, permit people to do work over if they have fallen short. Regrade it for "most of" the lost points. My policy was 90%. If you lost 30 points on an assignment you could get 27 points (max) back if you re did the assignment properly. My policy was generous, permitting several attempts. The repetition was good for the students and worked to assure learning.

Fourth, at the end of a course, look at how the students did overall and compare it mentally with what you think they really learned in the course. If you think the learning was actually better than the distribution shows, bump it a bit. This will push a few students up to the next partial grade, say from B to B+.

Finally, avoid marking using only a few high-risk exams, but spread the marks over a large number of tasks. All or nothing final exams eventually leads to trouble, even in less extreme cases than the one you mentioned. This leads to a practice of continuous study and learning rather than "cramming" for the big one. Cramming results in more memorization (short term learning) and less deep learning.

My students all knew everything about the above policies. I seldom got complaints about grading. I could be as demanding a professor as I felt necessary (students viewed me as very demanding). At the end of the course surprises were always happy ones and students felt good about themselves and encouraged to continue.

Finally, let me note that strict scaling, making the course distribution into something like a normal curve is, IMO, always a mistake. It turns the course into a zero-sum game for students who can only win if someone else loses. In theory it should be possible for everyone to do well, even full marks, based only on their own work. Such scaling is also unjustified as it assume that a given sample (your students) perfectly match a population (all students). Statistically that is a serious error to make unless your scale is huge.


Contacting a Professor about a possible Ph.D. During my Masters I wrote and published a series of three research papers, which I presented at 3 different international conferences.

After my last presentation/talk at the 3rd conference, a Dean from an Australian university approached me and after talking for some time he gave me his business card and told me to send him an email if I'm interested in attending a Ph.D. programm at his university.

Now (4-5 months later): I will start my master's thesis in the summer and after talking to a lot of people (friends, parents, etc) I decided to contact him and ask about specifics. Turns out, I don't even know what to ask. (I havent sent the email yet btw).

In my eMail I start off with how we met (so he might remember me?) and then go on to say that he mentioned the possibility of the Ph.D. program at his university and gave me his business card.
I then mention that I will presumably finish my studies Nov/Dec 2018.  (so he knows a possible timeframe)

I would like to ask him questions about the possible research fields, finance possibilities, etc - but our conversation was so short back then, I'm guessing I can't just ambush him like this.

So what can I write between mentioning that I will finish my studies and writing

"I am looking forward to hearing from you, Best Regards,xxx"

Also:
What would be something a professor might want to know about me (should I include my CV already?), and what questions show that I am interested, without ambushing him completely? Should I write an "apology-sentence" that I am contacting him 4 months later and not in the following weeks?

Any suggestions are welcome! Thanks in advance!

PS: I am from Germany and currently also study at a German University.

I formulated my mail as a "letter of motivation" - Just in the same way you would write an application in Germany. You could start with something like

  "Thank you for the nice discussion about the open PhD position in your
  research group at the XYZ conference in October 2017..."

Then, you should mention that you had a very close look to his/her research and that this perfectly fits your interests because of "this" and "that".

In any case I would already attach the CV with all certificates and required documents.


Summer Internship - A very well-known Professor I want to send an e-mail to a very well-known professor in the field of my interest. I have read most of his papers and I am 90% sure that I like to work under his supervision at least for some months. I am an undergraduate student but being realistic, I hope to be considered a good student in my field and recently, I published a paper in JHEP. My question is:

  The professor has a research group in a French university. I want to let him know I will pay for my costs and I just want to do a 3-month or even more research under his supervision. Since I guess I have good accomplishments in my CV, how I can ask him politely for a summer internship (or a visit, I am not familiar with these names) and potentially guarantee to receive a reply message from him?

Thanks!

I want to let him know I will pay for my costs

This is not how things are supposed to work. It is normal that young researchers visit other institutions, but these visits are normally paid either by their current institution, or by the accepting one. What you're doing is explicitly proposing to do unpaid work, which is a bad practice and you don't want to work with someone who agrees to that.

What you can do instead is find 5-10 places where you'd like to go for an 'internship' or 'placement', etc and send emails with inquiries. It is fine to ask a professor whether they can hire you as an intern, but I don't think you should propose unreasonable arrangements.


How honest should one be in a PhD exit interview? I will be graduating with a PhD and so I have one coming up soon. It seems like a new trend in academia. It is usually for all graduate students who enter the program when they leave it, with or without finishing. For example: ece.rice.edu/~suman/grad_council/exit.html. It is not to be confused with the thesis defense and will have no impact on my graduation. The interview is going to be with only the graduate program coordinator in my case. I assume, though, he will share the input with others including my advisor.

I searched the web and found answers for exit interviews in the corporate workplace instead. Most of the websites seem to suggest not to be completely honest to avoid burning bridges. These websites also suggest that HR people don't make any structural changes anyways to accommodate your concern. Can I extrapolate these ideas to a PhD exit interview? Is there anything to be gained by being honest in this interview? In particular, should I be honest about the advice and guidance I received from my advisor especially if I feel I didn't receive enough attention and advice from them? I know this is a common complaint among graduate students, should I be honest nonetheless?

I had an exit interview when I left my graduate program and I have also had exit interviews in industry.  My feeling is that exit interviews in Academia have the potential to be productive, whereas in industry, it's mostly just HR covering the company from liability.

There is probably no perfect answer, but I would say that you should be honest while remaining professional and constructive.  If you have criticisms of your degree program, try to keep them general (don't focus on your PI or your lab unless they ask about it).  My program director was very defensive when anyone criticized the program, so I was careful and tried to provide solutions to my criticisms.  I do think that he took some of the suggestions to heart, such as moving the qualifying exam so that it did not overlap with certification board exams.

In my opinion, the exit exam is not the time to bring up anything along the lines of harassment, mostly because I don't think it would be recorded properly, as random_non_bot_person mentioned.

Industry is different.  Exit exams seem to be only there to cover the company.  If you were sexually harassed at the company, HR wants to know so that they can prepare for a legal defense (I'm guessing, I'm no lawyer).  They want to know if you were fired, they want you to understand the implications of any NDAs that you signed, and they want to know that you aren't going to sue them.

Summary: Academia exit interviews have some potential to change the program, but is highly dependent on the program itself.  Industry exit exams will almost definitely not change a thing.


Publishing paper using an open-source software Can someone publish a paper on the mathematical model of the open source software i.e. parameter analysis of the model (i.e relevant to specific area) which is the backbone of the software.

Is it possible to publish paper on model analysis?
Apart from citing the software, what kind of credit I should include in the paper?

Yes, it is possible to publish a paper on model analysis. I would treat this like any other situation in which I wanted to use someone else's "data". Generally, it would be the owner of the data (in this case the developer of the model) that would do the analysis. In the absence of an analysis that I need (or want, or think would be interesting), I would contact the owner(s)/developer(s) and see if they have plans for a similar analysis or would like to collaborate.


Who is responsible for "blinding" a paper for double-blind review? Is it my responsibility as author to remove author and affiliation before submitting the paper to a journal?

In my opinion it is, but then I read this blog article by an Editor-in-Chief of some Elsevier journal who lists several reasons for rejecting a paper, including:

  The manuscript is not complete; it may be lacking key elements such as the title, authors, affiliations, [..].

So my paper can be rejected if I prepared it for double-blind review myself? But how can I make sure that the paper is properly reviewed without my name and affiliation?

It will depend on the journal, and if the journal follows double-blind review, they will tell you how to properly format your paper. For example, the American Journal of Epidemiology:

Manuscripts must be submitted online in a double-spaced, blinded format, with pages numbered, in at least 12-point type, do not insert line numbers.
When uploading the manuscript to the Web site, please delete any text reference to the paper's authors, affiliations, and acknowledgments.


Should I translate my Dissertation I finished my M.Sc. dissertation a while back but I wrote it in Portuguese. I am applying for postdocs now and in some they ask for my research documents. Should I translate my Dissertation or is it wrong? And if yes, what notes and where should I write to identify it as a translation?

No, you don't need to translate the entire dissertation just for an application.  That would be an unreasonable amount of work.

However, you should at least translate the abstract.

Better yet, write an extended abstract in English.  This would be a 1-2 page summary of the contents of your thesis (brief background, methods, results, future work, etc).

Submit this together with the original Portuguese text of your thesis.

Commenters have suggested writing and submitting a paper (in English) based on your thesis.  That might be a good idea in the long run, but it might not be practical to do before your applications are due.  And you shouldn't rush the process if it would hurt the paper's quality.  So I wouldn't focus on that as a solution to this particular situation.


Whether/how to list a failed student on my CV I'm a postdoctoral researcher.  My professor recently asked me to co-supervise one of her doctoral students, whom she also employed full-time as a research scientist.  Since I had no prior experience advising at the doctoral level, I gladly accepted.  Unfortunately, the guy turned out to be a phenomenally poor researcher.  He was habitually late for work, took absences without leave, lacked subject-matter knowledge that we expect even of undergraduate students, and had extremely poor oral and written communication skills.  I worked my best to help him, and we even managed to get a paper published (though I had to completely rewrite his draft, many parts of which were completely nonsensical or even plagiarised).  However, his bad work habits continued, and despite my tutelage he remained unwilling or unable to fill the gaps in his knowledge.  After about six months of my co-supervision, my professor decided to dismiss him from the doctoral programme and from employment.

I'm currently seeking employment, including tenure-track positions.  My question is whether and how I should list this failed student on my CV.  On the one hand, I think it would be helpful to show that I have at least some experience advising at the doctoral level.  But I don't want it to reflect poorly on me that the only such experience I had was ultimately a failure.  Should I list him or not?  And if so, what explanation, if any, should I include in the CV as to why the supervision didn't continue past six months?

One thing to keep in mind is that tenure-track faculty generally do not list failed PhD students on their CVs.  One might argue that they should actually, since -- unlike a non-academic resume -- a CV is supposed to be a factual, comprehensive record of one's academic career, but nevertheless academic culture doesn't cut this way.

The next thing I will say is that in my experience at least, "co-advisor" refers to a member of a group of more than one faculty member -- usually, though not absolutely always, tenure-track faculty members -- who jointly share the role of advising.  You seem to be using the term as a kind of "lieutenant advisor," but that could be misunderstood.  When the student's other advisor is your PI, it is likely that your advising duties are less official and more subsidiary.  In particular, you write "[M]y professor decided to dismiss him from the doctoral programme and from employment," not we decided...Nor did you hire the student, fund him and so forth.  However, from the sound of it, you played a large intellectual role in supervising the student.

So: I would not recommend you put a line on your cv which says "co-supervised a PhD student."  If potential employers are interested in that, they are likely to ask you about it, and when they find out that the student didn't graduate, I think that sounds bad...in particular, it sounds not completely forthcoming.  On the other hand, "co-supervised a PhD student who did not finish [or however you want to put it]" also sounds bad, as I mentioned above.

I would suggest instead that you put a line on your CV saying that you mentored a PhD student and that mentoring relationship resulted in a joint paper.  That's a successful mentoring relationship.  The student was not ultimately successful in the PhD program, but I claim that you're not on the hook for that: you didn't hire him and you didn't fire him.  If they ask for more information about the PhD student, you should of course be completely forthcoming about the fact that he was dismissed after six months, as well as all the help you gave him.  If/when this additional information comes to light, it does not undermine what you said before in any way.  Since you are not claiming the PhD student as your own, his fate is not your responsibility (which is true!), and so your involvement in the situation looks (and was, based on your description) entirely positive.


PhD intellectual property The Australian government pays for the research study fees.

Who will, in such case, have the intellectual property rights for the research: the PhD student or the university?

Laws vary and I am neither a lawyer nor Australian. But in general, unless the grant or university rules say otherwise, things you create are (or at least should be) your own. Some universities try to make a claim (various places in the world) and you may have signed away some of your rights previously, but in the absence of that the IP is yours.

Check locally for the correct answer, of course. If the rules seem arbitrary or unfair, you can explore, locally, what it would take to counter them.


Accepted for Masters but Can Only Attend if I Have a Scholarship I'm not a US citizen, and I was accepted recently for an MS Degree in CS. After asking the Graduate Office, I was told so far they don't have a scholarship for me (only 15 out of the 115 accepted got one).

I'm proceeding with the papers and such, since if someone drops, it will pass the scholarship for the next one, but due to currency exchange and the high price of US education, I can't go without the funding. Is there any way I can prove myself now or is it better to refuse the offer right now?

Ask if you are eligible for student loan.

Scholarships are often not available for undergraduate and Master's degree. It is extremely common in the US (and UK) that students borrow money to pay tuition fee and stipend. My former flatmate didn't paid all the loan until he was 28 years old (his father paid his tuition fee, he only borrowed for stipend). You can consider this as an investment if you believe the degree could bring you a well-paid job in the future.

On the other hand, you don't have to go the US to do a Master's. Many EU countries, such as Germany, provide free education for foreigners, and many provide scholarships based on the household income.


A single definitive paper, or an initial manuscript and a followup? Suppose that someone wants to publish his idea and his first successful experiments in a venue and then perform some extra experiments (for a more powerful proof) and publish it in another venue.

Submitting the first to a conference hope to be accepted soon and then submit the second one after a while to a journal, referencing the first.
Submitting both of them to journals and write them in different manners.
Waiting until future experiments finish and combining them to be published as one journal paper.

I count, here, some negative and positive points of the ideas

In the 1st idea, the first paper proves that your innovation has improved a method in the state of the art and the second one refers to the first and says that this innovation, even, is able to improve other more-powerful methods in state of the art. Of course both of them are contributions. But, lacking the powerful contribution of combining them in one paper.
In the 1st idea, you are able to present your work in an international conference and take advantage of global feedback.
In the 2nd idea, you should suffer side effects of deleting some parts from each paper, to avoid plagiarism.
In the 2nd idea, you will have two journal papers which normally are more creditable than having one conference and one journal paper.
In the 3rd idea, you should wait for finishing the experiments.
In the 3rd idea, though you have one journal paper instead of two, this paper is published in a more creditable journal than others.

However, please let me know the optimal choice.

Regardless of your elaborate schemes, you will always need to take into consideration that you cannot "sell something" twice.

If the only novelty that you present in a second paper are additional experiments (and deriving conclusions from these), then you must never ever state something different as a contribution of the paper. And in many fields, this would be quite a weak contribution, basically only leaving your schemes 1 (conference+plus extended journal version) and 3 (single publication) as realistic, unless you want to settle for a very weak venue for the second paper.

Slicing your work is also frowned upon in academia, for several reasons. First of all it looks as if you are trying to inflate your CV. If it makes grasping your overall contribution harder, it should also be avoided. Also, there should always be some scientific reason for splitting up your work. For example, if the target audiences for the two parts of your work have a very very limited overlap, then splitting up your work may yield a better service to the community. If the parts of your work are mostly interesting in isolation, and actually only of interest to few people in their combination, then splitting up the work is also sometimes the best way to go. And finally, if the underlying techniques of the two parts of your work are so different that publishing the two parts together would lead to a paper that is only readable by the very few experts that have knowledge about all techniques, then the split may also be justified. In all cases, you have to reference your other work and explain the differences in contribution.

In some fields with published conference proceedings with page limits, it also became customary to to leave out some additional information in a conference paper and writing longer journal version later. This is ethically OK because the journal paper in some sense replaces the older paper. This seems to be precisely your "scheme 1".


My department promised graduate students a particular teaching load, but has found a loophole to increase it I am a second year STEM PhD student at a large R1 public university in the United States. When I chose to come to this program, the department told me that I would have a teaching load of 3 classes per year. That is, I would be expected to teach two classes in one semester, and one class in the other semester per academic year. The language in our contracts is not in terms of classes taught or credits taught, but a vague statement to the effect of "teaching xx hours per week". Since a statement in terms of hours per week is obviously vague, I inquired about this when I choose to come to this program, it was explained to me that it was simply 3 classes per year and that the load is not as high as it seems on paper. Other graduate students had the same experience.

Now, they have changed their promise and are now expecting me, and some other graduate students as well, to teach four classes per year. We are upset about this because we feel as if the department intentionally misled us with the language of the contract. Since the department is not increasing our stipends, we also feel they are exploiting our cheap labor. This has also led to resentment among graduate students since some did not get chosen to teach extra classes, and it was essentially random who got stuck with a higher teaching load.

My question is: Is it reasonable to ask the department to increase our stipend or compensation for this additional work? If it isn't, what is reasonable to ask of them? Should we just get over it?

I think this is unfair, but there is probably nothing that you can do about it.  In my STEM department, TAs all nominally are responsible for the same number of work hours (twenty hours per week), but with different courses and different grading responsibilities, it is inevitable that there will be some iniquities—including some students teaching more sections that others.  Sometimes students unfortunately get saddled with extra work unexpectedly.  Mostly, the students grin and bear it, knowing that we try to have the extra work average out over time (see below).

As a practical matter, funding for teaching assistantships at an R1 institution mostly comes from outside the department (probably from the dean's office).  There is a set number of fully-supported TA slots available, and the department has a certain number of courses that it needs to have covered.  There may be a bit of extra money in the departmental budget, but if the department suddenly and unexpectedly increased the teaching load for some of its TAs, that suggests that the department may be in a precarious financial position, with little to no extra money available to support the TAs who are working harder.

It will not hurt to ask for additional money.  However, even if there was enough money to pay the overloaded TAs a bit extra, the department would probably say no.  If they did start paying some TAs more than others, that could provoke even more ill will and dissension among the graduate students; the ones who were not assigned extra sections would be (quite reasonably) angry that they were not getting the same opportunity to make extra money as some of their fellows.

So, I think it is quite unlikely that you will get any extra funding for teaching one extra class section per year.  What you can realistically ask for is to have the extra teaching load spread around between different TAs; this year, maybe half the TAs have to duty extra duty, but next year, those additional courses will fall on the shoulders of the other half.  The department may already have it in mind to do this, but it would not be a bad idea to get it stated as an official policy goal to handle things this way.

One final thing that might be relevant.  The amount of teaching that faculty members do in R1 STEM departments is far from uniform.  In biology, chemistry, and physics, full load for professors is typically teaching one or one-and-a-half courses per semester.  In other STEM areas (geology, mathematics, neuroscience, etc.), the course load for faculty can be about twice that.  That means that different kinds of STEM departments have different amounts of slack in what the faculty are available to teach.  Depending on your field (and the local departmental culture), it might or might not be viable to have professors take up some of the extra course responsibilities.  It is too late for that to change for this semester and maybe the next semester too, but things might be different next academic year.  (Again, however, this is probably something that the departmental faculty have already thought about and discussed, so any complaint you make is unlikely to make a great deal of difference in what ultimately happens.)


Using native language for research discussion in an English-speaking country My university is in an English-speaking country, hence most of people use English for communication. However, it is possible to have a supervisor and her student that are from the same country, which is not an English-speaking country.

When that happens, is it acceptable if they use their native language in discussion regarding the PhD research while at school?

Yes. I see this all the time in my school. Although some professors avoid doing so. I believe it can loosen some faculty/student boundaries. Also, it is advisable to use English language when accompanied by other students/faculty who can not speak that native language.


how to apply The Feynman technique efficiently to effectively improve my learning in math and physics? I have known the Feynman technique two years ago, I tried to use it in my studies. I usually read a text about some topic(usually math, physics) and wrote in some paper what I understood. But I had a bad experience with that, I spend a lot of time, I didn't learn and it caused frustration what makes me leave this technique.
I'm doing electrical engineer graduation so all of my studying is about math and physics, I wanna learn more effectively and efficiently, in other words, well and as little time as possible, just spend the necessary time and no more no less.
Recently I watched a video about this technique and I'm thinking in apply it somehow, but I wanna know to do it in a way that works, and then I wanna ask about your experiences and for advice on how to implement the Feynman technique to get improvements in my learning.

There are two key parts of the Feynman method: teaching and keeping a record.
The basic insight behind the first is that if you can teach something effectively then you know it well. Many professors have had the insight that "I didn't understand X until I had to teach it". But if you are going to teach something complex to another person who has (much) less understanding, then you will probably need to resort to analogy and metaphor to get them into the correct mind set.
So, the application to the Feynman method is to form analogies about what you are studying, where the analogies are common (enough) and easy to recall.
But the recording part, using notebooks, is also essential, since writing down your insights engages the brain in a somewhat different way and that reinforces the physical changes (synapsis and such) that make deep learning possible.
So, one suggestion for using the method is to imagine someone, like one of your parents or siblings (unless they are also field experts) and imagine teaching some complex topic to them. Jargon and technical figures probably won't work, so you appeal to what they already know and come up with some analogies that will help them get started. But you don't just do this as a mental exercise. Write it down, as a lecture outline.
The other suggestion is that when you read and take notes (or listen to lectures and take notes) that you follow it up by summarizing those notes in a simpler form, say using analogies that will remind you of what you have just read. In fact, I'll guess that when you read something your mind is already working out some analogies. Record those. Having a "faithful" record (copy) of what you have read/heard isn't the same as learning. You need to engage the brain. Forming analogies and writing summaries are both good ways to make that happen.

There is more to learning that just the Feynman method, of course. This is why instructors give assignments and exercises.  Exercises help engage the brain in another way that is even more effective. It is also why cheating on assignments is counterproductive to learning. Reinforcement and feedback are keys to learning. The feedback is needed so that you don't reinforce incorrect things.


What does "Reviews Completed" status mean in Springer? I submitted a paper to one of the Springer Journals through Editorial Manager almost 5 months back. The status changed from Editor Assigned to Under Review and now it is showing "Reviews Completed" since past five days. Can anybody suggest what does it actually mean and is there any other stage before the decision is made?
Does it give any indication about the reviewers not rejecting my submission?

It's waiting for the editor to read the reviews and make a decision. This could mean one of the following: 'reject', 'submit as new', 'major revision, 'minor revision' or 'accept' (Depending on the journal, options may change).

Any decision above 'major revision' is good, meaning you get a chance to improve the paper and hopefully get to full accept.  Of course the paper can still be rejected at any stage until it is fully accepted.

Best of luck!


How much do math professors get paid in the United States? I have been researching professorship (full tenure) and I have been wondering what these full time professors make?

I am not talking about professors are Harvard, but middle-good universities?

In the United States.

There are several sources for this data. There are general salary surveys, such as the one done by the Chronicle of Higher Education.  These often classify only by rank and type of institution, not by discipline.

There is a more informative discipline-specific survey done by the American Mathematical Society: their Faculty Salary Report. This lags behind by a year or two while they process the data; the most recently released report when I write this is from 2013. The AMS divides mathematics departments into several groups depending on specialty and whether the department offers a doctorate, master's, or bachelors degree as its highest degree.

For future reference, here are the mean (average) reported salaries for new hire Assistant Professors in the 2013 AMS report. The meaning of each category is described in the report, which has much more detailed information.

Public, PhD-granting Large: $83,242
Public, PhD-granting Medium: $72,284
Public, PhD-granting Small: $67,043
Private, PhD-granting Large: $88,080
Private, PhD-granting Small: $75,722
Applied Math Department, PhD-granting: $81,234
Statistics Department, PhD-granting: $77,759
Biostatistics Department, PhD-granting: $88,362
Master's degree granting: $60,355
Bachelor's only: $56,217

There is also data available from CUPA-HR (e.g. 2014 Executive Summary). This organization has much more detailed data, but most of the data is not publicly released (university administrations pay for the data, so they can compare themselves to peers). They do release a summary by field, including mathematics, applied mathematics, and statistics.


Plagiarism of Lecture Slides Original Post:

I recently attended a lecture with highly technical content.  One of the middle slides in the deck had a particular phrase that reminded me of something that I had read several years before.

It took me a few minutes, but I found the original source (another slide deck) and am shocked to find that the professor lifted 35 slides verbatim from the original source.  The University holds students accountable for plagiarism and strictly defines plagiarism such that this clearly fits.  The slides were used verbatim and no credit or reference was provided.  Further, logos were added and color schemes were changed to make make them look slightly more like original content.

What should I do?  This professor has a professional full time job and I can imagine this getting swept under the rug, but this is clearly plagiarism and is spelled out very clearly in the student conduct code.

Is this an overreaction or should I pursue this?  This is confounded by the fact that I am very disappointed by the rigor of the course and feel like the professor lacks adequate technical background to teach the course (I guess my suspicions came true).  I'm really busy at work so don't want to deal with a big todo, but believe this really discredits the academic integrity of the course.

Additional content in response to comments:

The University defines plagiarism as "Using another writer's words without proper citation."  There is no proper citation given here, and these are not educational materials.  This is 35 slides from someone else's tutorial presentation.

I do not believe that the professor and the author of the original content have a relationship.  I have emailed the author to enquire about any permissions or relationship.  Still, even with permission, this is still plagiarism as defined be most people and as defined by the University.

All original works in the US are implicitly copyright protected, so there is also a copyright issue, but this is not currently my concern, though it could be of interest to the original author.  I have emailed him, so this is up to him.

This is a US institution and is a top tier highly respected school.  I am also not a lay student, but a Ph.D. with a degree from a top tier university who has served as a faculty member at two top tier universities (including this one).  I have also taught undergraduate courses in the past.  I have never engaged in this type of plagiarism, have never known colleagues to do so, and am shocked that so many people suggest this is par for the course in teaching.  My doctorate is from a school that prides itself on its honor code and takes honor code violations very seriously and I have served on the honor council and deliberated on a case of plagiarism in the past.  I really am shocked by this incident.

Response from Original Author of the Slides:

I have received a response from the author of the slides and he states that the work is his original work, that there are no additional authors of the work, that no one has been given permission to present the work, and that he believes his copyright on the work has been infringed upon.  He has requested additional information to assert his claim on the copyright.

If the author of the copied slides gave his/her permission for your professor to use the slides, we could reasonably have a debate about whether this is plagiarism. I would be on the side of those arguing that it is at least a mild  form of plagiarism if not worse, and in particular is setting a very bad example for the students attending the lectures, who are exactly the population that professors spend inordinate amounts of time and energy trying to educate about the wrongs of plagiarism and related forms of academic dishonesty. But as I said, I can understand if some people may disagree and argue that the culture of teaching in some places might look with some forgiveness on such practices.

On the other hand, if the author of the copied slides did not give permission to use the slides, then this is as clear-cut an example of plagiarism as I can imagine. To see this, try to imagine yourself as the author of the slides, who put in a large amount of time and creative energy creating something of value and will now be getting an email from the OP informing them that someone else has casually taken their creative output and put it to work for their personal gain, for free and without permission or attribution. How do you think you would feel? I do not need to imagine it; it so happens that a week ago I discovered that someone has plagiarized a work of mine. The details do not matter, and that incident involves plagiarism that cannot hurt me in any tangible way (though it is harmful to others), but I can assure you that it doesn't feel good. So to @ff524 and others advocating a tolerant attitude on this question, I hope you never find out how this feels from personal experience, and perhaps without this knowledge you cannot appreciate my argument. Nonetheless, I urge you to at least try the thought experiment I proposed above and reconsider your position.

EDIT: The premise of those arguing that this isn't plagiarism is that originality isn't expected in a teaching context. I'm afraid this argument doesn't stand up in the face of scrutiny. Let's say I were to show up to my class and instead of giving a lecture that I myself prepared, I were to recite word for word a lecture from a popular MIT OpenCourseWare class that I found on the web and transcribed, but without acknowledging the source or letting on that I was delivering someone else's lecture. If criticized, using the logic of @ff524's comments I will simply point out that teachers are in the habit of reusing each other's material, so it's no big deal. I will also claim that I am doing my students a favor since the author of the lecture is an extremely eloquent and charismatic lecturer whose lectures have been viewed millions of times by people from all over the world, so their lecture is obviously more compelling and useful than anything I can prepare myself.

Now, is there anyone here who seriously suggests that this can be acceptable behavior for a lecturer? Regardless of what you want to call it, there is serious dishonesty being committed. If I really believed reciting someone else's lecture serves the best interests of the students, why not acknowledge the source? My behavior shows a clear intent to deceive the students, to claim credit for the intellectual fruits of another person's labor, and to avoid criticism for not putting in the hard work involved in teaching a class, something for which I am paid. The situation described by the OP is 100% analogous to this, except my example is slightly more extreme. So again, before we discuss whether this is plagiarism I would like us all to agree that the OP's professor's behavior is 1. wrong, and 2. dishonest.

Now that we've agreed, the question is why anyone would be resistant to calling this behavior plagiarism. Okay, so maybe in a teaching context there is a bit less of an expectation of originality, but come on - copying 35 slides verbatim? Moreover, the dishonesty and intent to deceive that I described above are exactly the characteristic features of why people commit the more usual kind of plagiarism: they are lazy, don't want to work hard or aren't smart enough to do good work, and they want to get credit for doing something, so they choose the easy solution of taking someone else's work and passing it off as their own. Why the reluctance to call a spade a spade? I just don't get it.

Finally, let me elaborate on something I mentioned at the beginning. We in academia spend a huge amount of time and energy trying to inculcate in our students the moral ethos of doing original work and never acting dishonestly, especially in connection with claiming credit for someone else's work, and we agonize endlessly over why some students don't seem to be getting the message. I witnessed this on many occasions when I was serving recently on my university's Campus Judicial Board and sat on disciplinary hearing panels for students accused of many kinds of academic misconduct, including plagiarism. You cannot imagine the silly excuses and rationalizations people come up with for why they copied someone else's work or happened to be glancing directly towards the exam notebook of another student for extended periods of time during an exam, and you cannot imagine the frustration of the instructors who caught them. So, my point here is that if we refuse to label this professor's behavior as plagiarism, this makes us look like total hypocrites. If we are to have any hope of getting the message across to students that one must do original work and attribute any usage of someone else's, we have to be firm and consistent in our standards. I chose to focus on the scenario when permission for using the slides was not given by the author since there it is a bit clearer that a strict moral boundary had been crossed, but as I said my opinion applies also to the scenario where the author did give permission, although in that case it would be a milder offense since the author at least would not have been harmed.


What to do if a team member does not work but they have to answer committee questions? I am a master student in a French university and the university states that for a project students must form a team. However, I ended up doing the whole work by myself (They always come up with an excuse to avoid working). We have to present the project in a French, the committee will ask us questions.

My French level is at best A2 on CEFR. I am afraid that when they ask questions I won't be able to answer them due to my language skills also my colleagues probably would not be able to answer since they have not done any work which would result in a poor grading of our project.

Usually, the committee members are able to distinguish between difficulties which are due to language issues and incompetence. Don't hesitate to ask for clarification if you don't understand a question and maybe you have a chance to make som drawings on paper or on a board / flip-chart which show the relevant concepts.


Where should you mention the name of potential mentor in research statement when applying for a post doc? I am going to apply for a postdoc position. I exchanged emails with one of the  professors as potential mentor and he told me to mention his name in my research statement. In which part of the research statement his name must be written? Is it enough to write it in the first paragraph?

Your research statement should probably mention the new potential mentor's name in the context in which his research interests align with yours. So give a bit more than just the statement that he has agreed to mentor you. In other words, say why it would be a good plan for you to work with him.

As to having your previous advisor give you advice on your research statement, there is no ethical concern at all. It is natural for you to continue to seek professional advice from your doctoral advisor. Your relationship doesn't break when you finish your degree. It changes a bit, as you become his colleague in the field, rather than his student.


Can you do a PhD solely on repeating experiments of others? In here, they say there is a problem with results not being replicated. I know a PhD is supposed to be "original" research. But could you do a PhD solely on replicating the results of others? Especially given that there is a problem with results not being replicated, I believe you would be contributing a lot to science. Even if it is not "original research".

I would say it depends:

a) If in medicine, you are the first one to replicate an effect in an independent study, then you actually did something original, namely confirming the effect

b) If in physics, you refine an experimental design to verify an uncertain outcome, or clean up a measurement to exclude artifacts, and make the effect more clear, it is also something original

The rest is in the shades of what passed in the journals as "original". But I guess as long as the experiment was set up freshly (i.e. not in the same group) and had any small improvement/change (analysis method, amount of data, statistical uncertainty, control experiments) over the original it would pass in many universities for a PhD (and I will not give my personal opinion on the level or originality required for a PhD in an average university here).


Choosing one's best papers for job applications I'm a postdoc in the physical sciences, currently writing some job applications.

I have noticed that sometimes, particularly for research oriented faculty positions but also some postdoc positions, you may be asked to list your three best/most significant papers. Presumably the goal here is to enable a meaningful evaluation of your research, without the need to read all of your papers.

My question is: what is the optimal strategy for choosing which papers to put forward as your best three?

Should one simply choose the ones which are in the highest impact journals?

I imagine that long papers might be off-putting to busy people with lots of applications to evaluate, so is it best to only choose short papers? On the other hand maybe throwing one long paper into the mix can, if it's good, demonstrate a capacity for deep scholarly research?

How heavily does first authorship weigh? Does it look bad if one of your best three papers is one where you aren't the first author?

In my specific case, I'm submitting a faculty application where I have to choose three papers and I'm fairly sure of two of my choices. For the third, it comes down to a choice between a well-cited paper in a fairly high impact journal, but where I am second author versus a paper where I am first author, and which I think is a good paper, but it's in a less high profile journal and hasn't attracted very much attention in the field. Are there some general principles for deciding which is a better choice?

If your first two choices are sufficiently strong (as they appear to be), then for sure go for the third with the paper where you are a lead author. As a general principle, lead authorship is considered to be more relevant than co-authorship (unless it is associated with a stellar journal of the highest IF in the field.)


Letter of Recommendation from English Teacher for Math Ph.D. program? I am a Japanese undergraduate junior student who is thinking of applying to graduate programs in Mathematics in the U.S. and the biggest problem for me is the Letter of Recommendation. I am planning to ask a teacher of my English class for one of my LOR because my senior who went for a grad school(Engineering) in the U.S. did so.
My intention is that she can prove that I have sufficient English skills to study in the U.S.

However, from what I have seen on the Internet, no one seemed to do so and all of LORs have to be from people in relevant fields. Is getting a LOR from an English teacher for Math Ph.D. program good or bad?

Knowledge of the English language is often a hit-or-miss requirement, i.e., either you have a recognized English qualification (TOEFL, IELTS...) and you can study there, or you do not have it and you cannot study there. In many institutions, this is checked by administrative personnel, independently of the selection committee.

Having better English knowledge than other applicants who also have the qualification will in most cases not give you any advantage over them. The selection committee (or the prospective PhD professor, depending on the location) will almost exclusively consider your qualifications in your research field when deciding whether to admit you.

I would recommend trying to obtain recommendation letters from people that can say good things about your skills in your field.


Do cheats profit from their dishonesty? How widespread is it? Are there any studies of this? I'm retired and this doesn't affect me personally but I'm curious. I don't remember cheating with an exam or an assignment even once, nor was I aware of others doing it. Going by what I read here and elsewhere, cheating is now something of an industry.
I can't help wondering how people who need to cheat get on when they reap the benefits of their dishonesty. Can they do the job that they are supposed to be qualified for?
Question
Do such people get jobs? Are they easily filtered on interview? Are they easily detected once in post, and what happens to them eventually?
Is there any research on this, formal or informal and, if not, does anyone have any anecdotal evidence?

Personal observation
If I qualified for the Olympics by cheating, as soon as I started performing (at my age I would have to walk the 100m sprint), it would be obvious to everyone that I was an impostor. Surely it's the same for academic cheats.

Yes, people profit from cheating, perhaps for a long while. But the jobs they enter aren't as cut-and-dried as claiming you are an olympic athlete and it can take a long time for incompetence to be noticed. And incompetence can arise from other things than cheating.
I don't have evidence about studies but would probably question them unless very well designed. What criteria would you use? Would correspondents honestly admit to it? Could you run a longitudinal study and still be able to check back for prior behaviors.
Some people who cheat actually wind up to be competent, also. They might have actually learned something, or taken jobs for which they knew "enough". Some people cheat once or twice out of panic, but are otherwise honorable.
It is a very complex question. But I doubt that a lot, even a majority, of cheating is caught at the time and those who are clever enough or misbehave seldom enough will get through any gates we might erect. Our processes are designed to discourage the practice, but they aren't perfect.
And, no, I can't prove people profit, but the imperfect nature of the systems and the possible rewards and the panic among students, etc. etc. certainly imply that cheating happens without consequence.

And if you want to assure that cheating doesn't happen, design the course in such a way that it is irrelevant. In particular, in such a way that it isn't necessary to feel that you need to watch people carefully so that they don't. Of course, your evaluation methods are a bit harder to manage in such a system and the things the students do need to require real understanding. Oral exams are probably ideal, but scale poorly, of course. But even in a large, say, calculus course it only takes a few minutes to determine whether a student understands. My old advisor did this, with a line outside his door and a question or two for each student.
My technique was to use projects rather than exams for the bulk of the grade.
It is probably more difficult in the pandemic age, but also more necessary to think harder about evaluation.


What are some typical questions one might be asked in a Computer Science PhD Viva? I know that you are likely to suggest "Ask your supervisor. He/She will know best.".

While that is obviously true, I would like to know from your personal experience either as the PhD candidate or as the examiner: What should I expect in my viva?

For example:

Should I expect to be asked about the history of my topic?
About the research cited in my thesis (i.e references - will they ask me about them? I have over 100...)?
About methods used?
About justifications for my conclusions?
etc

What should one expect, in general, to be asked? I am PhD student in the UK.

There is one critical question that is asked in every Phd viva, be it either directly or indirectly, that is "what is the original contribution of your thesis to knowledge". To be awarded a Phd the result of the research must be to contribute something new to the planetary or disciplines store of knowledge. You must have done something new and know what that thing is.

It is the very purpose of a viva to discover if you know that. Saying "I do not know" to this question can result in a Phd failure.

Other questions will seek to discover if you really understand the works you are citing in your bibliography; have you actually read and understood them and do you know how they relate to your work.

The third aspect of a viva is to resolve any ambiguity that the examiners read into your writing. Which of the possible interpretations of the words and conclusions did you actually mean. You might not have realised that your thesis contained ambiguity but the examiners will certainly find it. They will also seek to expand on or explore elements that are not clearly explained and get you to clarify them.

The answers to all these questions will determine if you have to make corrections to the thesis to resolve these ambiguities or make clarification, or even conduct more research to resolve those matters.

This is written from the perspective of a Phd examiner, both internal and external and experience as an independent chair of such vivas.


Travel Budget for Faculty Members in USA How much budget do faculty members in US  have to travel to conferences every year?

I am looking for the average travel support in CS or Engineetring  departments (not out of my own contracts) across US top 100 schools, for tenured professors with accepted papers at conferences. Consider places like University of Washington.

The background of the this question is, I like to find out, once my tenure track funding package is finished, would there be a minimum research support from the department, or I am on my own. It makes sense that if my paper is accepted, the university pays for it (I present my work under the university banner). Before asking my department, I like to equip myself with prior information.

Although I am a mathematician and not a CS or engineering professor, I would overwhelmingly expect that at a place like the University of Washington tenure track faculty members would be expected to secure external grant funding (for example from the NSF) and use this to cover their travel expenses.

Universities often provide startup funds which you can use for this purpose during the first couple of years -- but these don't get renewed when you run out.

In the long run, if you don't succeed in obtaining external funding, it is unlikely that your department would rush in to fill the gap. My department (less prestigious than Washington) provides a little bit of money to faculty members without grant support, but it is only enough to attend to one or maybe two conferences a year.


ScholarOne Manuscripts File Designations When uploading files in SCHOLARONE Manuscripts (IEEE journals), I can pick an appropriate file designation for each. What is the file designations 'Summary of changes' and 'Summary of differences'?

Summary of Differences: If you are submitting an extended version of a previously published work, you may be required to submit a "Summary of Differences" between the new work and the original.

Summary of Changes: If you have made revisions and are re-submitting a manuscript that has already been reviewed by this journal, you may upload a file in which you respond to the reviewers' questions and comments as a "Summary of Changes."


Author list mismatch; should I recommend rejection? I am reviewing a paper for a journal. However, the author list in the submitted pdf file of the paper is different from the author list in the submission website. One of the authors is not appearing in the online submission website. Though, it is possible that some of the conflicts have been ignored (I am not in conflict with any of the authors). Should I recommend the paper to be rejected or just inform the editor about this (or any other solutions)?

Inform the editors as soon as possible. They will decide what to do. This is probably a minor issue that can be resolved in communication with the authors directly.


When applying to grad school with a mediocre past how can you make yourself stand out So the title question is not real clear so let me try to elaborate. I am currently 30 and a senior in college. I will be graduating this spring with a BS in Computer Engineering. The way my school is set up the Comp-Sci and Computer Engineering departments are together and the Electrical Engineering department is separate. I expect to graduate with over a 3.5 gpa (not wonderful) but have no research or intern/on the job experiences. I am looking at continuing on at this same institution with their phd program. My gre scores verbal 165, Quantitative 158. Their cut off is 146 verbal and 156 quantitative. I still need to talk with them and see how they utilize the gre scores and see if I need to retake the gre and up my quantitative score.

So from that mediocre beginnings I come to the grand finale. When talking with professors and otherwise looking/learning in my field. How do you find those areas that are new and pushing into the frontier. When I write a letter of intent and they want me to tell them what I want to be doing. Where do I go read up on the new stuff for my major. What are some all around objectives and methods that people use and then specific to computer engineering what are people reading or procuring their resources from.

What other things could I do in this last year that may help my chances of being picked?

As an additional question, Letters of recommendation would be coming from professors from within the department that will be doing the selection. Some of these Professors would be the ones heading my thesis committee(i think that's right). Does anyone have any experience they can relate where they went to grad school at the same institution that they go their BS degree from.  I will have one outside reference as a peer mentor for our freshman engineering program but otherwise most of the professors have seen me go over the top in my work for their classes.

Why do you think you should be admitted?

The admissions committee will ask themselves: if this applicant has mediocre qualifications, why should we admit them?  Why should we believe they will be successful?  So, you should be asking yourself that question, and thinking about what your answer would be.

If you have a good answer to that question, that might tell you how to make yourself stand out: emphasize the parts that make you think you are well-qualified to succeed.  Your application is a chance to tell your story and answer that question for the admissions committee.


Why do academics prefer Mac/Linux? In the commercial world, Windows utterly dominates. As of time of writing its market share is somewhere around 85%.

I don't have similar statistics for academia, but my personal observation is that most professors seem to prefer Macs, especially on personal devices. Linux is also a lot more common than in the public at large. Windows computers are still around, but much less common - maybe around 1/3 of computers run Windows, a substantial fraction of which seem to be because they're preinstalled with Windows.

Why the preference for Macs / Linux?

It's not entirely accurate to state that Windows 'dominates' in the commercial world. While this is generally accurate in Desktop and Laptop computers, keep in mind that the vast majority of web (and other) servers are Linux-based. Keep in mind that 100% of the TOP500, a list of the 500 most powerful computer systems in the world, are on Linux. Keep in mind that Linux, especially if you include Android, is almost definitely the most prevalent operating system (kernel) in the world in terms of number of devices.

A lot of academic institutions, especially in more computing-related fields, are big supports of open-source/FLOSS communities, and Linux is obviously a very big underlying part of such communities.

And, most key in my experience, for academic/scientific environments, Mac and Linux have enormous computational advantages over Windows - the underlying Unix architecture model and the inherent support of very useful shells like bash, very useful device handles (eg. /dev/random, or device IO handles), etc. These things make the development of custom code, especially for scientific, computational, or instrument-interactive purposes, a lot easier than on Windows.

Even if you're not using custom code, I'd venture to guess that absolutely any computing cluster worth its salt is being run on some kind of Linux. If you want to just use that cluster, it's far less hassle to SSH in with a Mac or Linux computer with SSH built-in, than having to use something like Putty and fiddle around on Windows.

In all, science is very heavily dependent on computing. And high-level computing is very dependent on Linux. In my view this is the biggest reason that very compatible Unix-like systems like Mac and Linux are far more prevalent.


A good motivation letter I am writing a Motivation letter to be used in applying for masters from German or Finland Universities. I have following points explained in my motivation letter:

How I see this technical world
Why I chose Computer Science in my under-gradute
What I learned in under-gradute
What are my interests
Why I want to pursue Masters
Why I chose their University

What could be the format of a good motivation letter?

What other points could be included to make it more explaining about myself? And how to arrange those points? How to connect a motivation letter to your CV?

Well, just typing "motivation letter" or "cover letter" into Google seems to spew up links to loads of hints, tips, and how-to-s, so I guess it's not so hard to find materials outside of here.
So, instead, I'll focus on the advice that was given to me last year when I had to write one, since it worked for me :)
An important thing to realize is that a cover letter is supposed to complement your resume, not duplicate the same information. Thus, your qualifications and achievements should mostly stay in the CV.
Also, you should focus mostly on the position you are applying for, what makes you want it and what makes you think you would be good for it, which means that "how I see this technical world" probably does not deserve much elaboration (one sentence should be okay in my opinion). You are trying to convince them that you are the One and show them what makes you different from the bulk of people applying. What makes you more suited, more likely to succeed, or a better fit than other applications they're considering. (I would guess it's easier for a Master studies application since they will be admitting more than one/a handful of students).
Finally, the structure that I used, and will probably use the next time I will have to write something like that, is dividing the letter in to three parts:

you: here's where you'll put information on you. While you're not supposed to list all your achievements here, you should emphasize or elaborate on specific achievements, skills and motivations that make you especially suited for the position you're applying for

them: it is important to "prove" that you're not sending out generic application letters. You demonstrate that you're familiar with the work done in the university/lab/research group you are applying for.
If you're applying as a PhD student to a lab/professor, you might want to mention a specific paper or project and write an intelligible sentence or two about that. If you're applying for a University, you might want to pick some specific of their programme that especially caught your eye.

you and them together: first section is about how awesome you are. The second, how awesome they are. Now (to conclude), you have to show why you thing you would be awesome with them. Why do you think you're a good fit, right there? This section is not only about what they can do for you, you also have to try and convince then of how they can benefit from you.

Finally, the letter should not be longer than one page. Somewhere, you can even find this as a hard limit on an application. In my experience, you should also not have more than 3 sections. I managed to put an introductory sentence and an finishing sentence just fine in the "sections" I mentioned. Also, your writing should be impeccable. If you can, get a native English speaker to check your language. Use a spell checker. Pay attention to details. Getting an address wrong, or a letter in a name, shows sloppiness. Maybe it does not consciously affect the decision, but it might on some level.
And the last advice I got, which I did follow, but you don't have to take it as a hard rule: avoid negative statements. It's supposed to give a better tone to your letter. So, instead of saying, e.g. "Unfortunately, I couldn't find and internship during my last summer", you should write something like "Having a free summer before my last year, it allowed me to spend more time reading upon the areas of my choosing". The positive statements should help you present the facts in a more positive light.