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Validity of results recorded in electronic vs bound vs unbound notebooks?
How do we distinguish between the validity of electronic, recorded bound, and recorded unbound notebooks for gauging whether research results are publishable?

Can you [edit] your question to clarify the connection between "lab notebook" and "research results are publishable"? In particular, use of lab notebooks is highly field specific, so please indicate your field in the post?

Yes, this question makes no sense to me (although I'm admittedly not in a relevant field, so my lack of understanding is probably partly me and partly how the question is written).  Are there really fields where publishability could depend on whether you used an electronic or paper lab notebook, with everything else remaining the same?  That sounds bizarre.

@Anonymous For patentability, records in bound lab notebooks can serve as evidence of being first to invent (See Wikipedia). I've never heard of anything like this for publishability, though.

@ff524 although this has recently changed and the only thing relevant nowadays (in the USA) is the date at which the patent application was filed. See: http://techtransfer.jhu.edu/faculty/patents/aia/aia_5changes.html#First-Invent

Where I have been involved with publications and lab notebooks, in biology, this question has never arisen.  Electronic, bound, unbound---I don't usually have any idea what my experimental collaborators are using, and it's not considered relevant.  They are responsible for the integrity of their data, and no journal has ever done any sort of "lab notebook check" on us before publication.  The only place where I can even imagine this would become relevant would be in attempting to resolve an accusation of academic dishonesty.


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Is it necessary to double major with math to be considered for theor. physics grad school?
So here is my situation.
I'm an undergraduate physics major right now (second-year) at a moderately-good university. I've experienced a bit of analysis and algebra (and almost nothing in topology, which I should change), and I like it (I do study maths on my own time - I like [complex] analysis best). However, my style and preference gears me more towards physics than anything. I've experienced working under an experimental nuclear physics group for some time now, and I love it, but it has shown me that I'm probably better suited with theoretical physics. I'm not saying that because I've proven to some people to be a genius at mathematical physics [maybe I could become one :-) ], but rather because that's where my interests seem to lie.
But I've noticed something. A lot of smart students here and theoretical physics professors at various universities come from a double math & physics backgrounds (a few just from math), and I was wondering whether I should go down that same path. I've avoided it so far because I felt that with the topics I've learned on my own, I've gone more in depth and personal as compared with my classmates, and so I feel that it is in my best interest to continue to do so with my math courses. Also, I feel that getting the math degree will only take up time and my full-attention from my normally planned physics track.
On the other hand, I feel that I could probably learn quite a few things from my math classes that I wouldn't learn otherwise. Professors already know the material they're teaching, so they typically know what to teach you. On top of that, if I wanted to enter a theoretical physics program for graduate school, I might have more of a chance of being considered with that extra degree.
Does getting a double degree in mathematics and physics better increase your chances of being admitted to a graduate program in theoretical/mathematical physics?

How about combining formal lectures and self-study together and then you get the best of the both worlds? If you are already capable of learning higher mathematics on your own then taking a course will probably not add any considerable extra workload for you.

If you get a 2nd degree in math, you'd probably have to do research for a thesis and perhaps that's where your self studying skills could really show so yeah I would say with and undergrad mathdegree, you would have demonstrated ability to do basic research. I'd like to believe math research is different from physics research. Disclaimer: math grad student here with no research experience in undergrad

Then again if your undergrad thesis is on theoretical/mathematical physics, what's really the point of getting a 2nd degree? You'll learn the math you need in physics BS or MS. Maybe a few extra courses will help but a 2nd degree seems kind of over doing. However, if you are passionate about math as you are physics,want a 2nd degree and can handle the work load and it doesn't conflict with your passion for physics, go ahead!

The quesiton asks about a double major.  In the US, this is not a second degree.

As an experimentalist, I have not found the analysis I studied as an undergraduate very useful.  Group theory is useful.  It seems to me that theoretical physics and nuclear physics are not well funded in the US compared to other branches of physics.

Related from the "other" side (coming from math): For a future in physics, should I pursue a dual major as opposed to just taking extra physics courses in mathematics degree?

@AnonymousPhysicist What is the difference? Finishing 2 majors gives 2 bachelor's degrees right?

@BCLC no, a major is only part of a degree in the US.  If you get a double major, you have fulfilled the requirement to complete a major twice, but you have not completed the other degree requirements twice.

@AnonymousPhysicist My experience is different universities handle this differently. Some universities seem to only award one degree to graduates, regardless of number of majors. Some seem to award 2 pieces of paper, although whether this really counts as 2 degrees, I can't say.

I would also add that depending on the physics you want to do, some programming / computer science courses might do you good too. For numerical topics as: conformal bootstrap, S-Matrix bootstrap,  Complexity/Gravity/Tensor Neworks, quantum computing  (IT from Qubit related things).

Obtaining a double major is helpful but not necessary.  In making your decision, consider what you would do instead of obtaining the double major.  Research experience will count for more than a double major.

As a current PhD student in theoretical physics, I find that the concepts from upper-level math courses are often helpful, the theorems sometimes helpful, and the tools almost never helpful.
For example, when working on research-level quantum mechanics one should have a good handle on what a Hilbert Space is. Sometimes theorems about Hilbert spaces are helpful, but it is rare. The methods use to prove such theorems basically never come up.

There is one additional small advantage to doing a double major in math or physics (or double bachelor degree in some countries). If you want to continue in academics you will quite likely find out you will want to continue in mathematics instead and will be able to do so. (This happens to be personal experience where none of the many double bachelor students I knew chose to continue in physics in the end. Some did in maths.) Also Math seems to have way better career opportunities in academia. (At least compared to theoretical physics.)

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None of 25 students at question session before exam has a question. What should lecturer do?
In a comment on another question Josef shared an anecdote.

And just another anecdote: A few weeks ago, there was a question hour at the last lecture timeslot before the exam. About ~25 of more than 120 people taking the course where there. Nobody actually had a question. All just wanted to hear the other questions...

I have seen this many times. Sometimes the lecturer can push students and someone will ask "what will be in the exam?" which is obviously useless and just makes the lecturer restate what (usually) is already told multiple times about content and procedure of the exam.
What should the lecturer do in such a case?
Technically it seems correct to just say "if you have no more questions, the session is over" and go home.
Yet there are times when the lecturer had to commute to this specific lecture and it seems travel-time spent in vain if nothing is actually done.
Students have also come in hopes that the session will help them prepare for exam. And usually the average question-session student is above the average from all the courses students, and lecturers don't like to let down the more interested and responsible students who assumed they don't want to miss this event. And usually this is what happens - the lecturer tries to improvise and talk on some subjects that seems more tricky/useful for the lecturer.
What are the best strategies so the lecturer could actually make the "question" session worth the attendees time?
I know it isn't good to broaden the question intentionally, but solid plans how to prepare and avoid the no-question situation at all are also welcome here.

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

Some specific suggestions:

You likely know what material the students have been having issues with, based on questions from lecture, office hours, previous homework sets, or whatever. Mention those topics and review them.
Instead of asking for broad-based questions, go over the syllabus lecture by lecture, calling out specific topics by name and asking whether there was any issues with each of those. It'll help prime their memory.
Ask the TAs to attend. If that's not possible, ask them to send you common questions ahead of time. They also know what the students are finding difficult.

Regarding your second suggestion I might even try giving handouts of exam question list. Currently they have electronic version but only a couple of students print it out and take it to the question session.

It might also be effective to ask the students to send questions they'd like to see covered ahead of time. That gives you (and your TAs) more time to prepare and it allows students to ask questions without feeling embarrassed.

Speaking as a student, I think the big issue is people feel uncomfortable speaking up.  In environments where the students feel comfortable speaking, there are generally a lot more questions.  I don't know what magic those professors use to make that possible...

@Dancrumb - I had that listed as a bullet, then figured that if they're not asking now, they wouldn't send ahead of time. That said, if the only issue is shyness, then yes, that's a good idea.

If a student is afraid to ask a question in a classroom they are going to suck at the real world.

Those review sessions (first bullet point) usually bias the students to think its an insider info: that those are topics that will appear in the exam, and they begin to study only those. It causes a problem when this bias is confirmed - whomever didn't show up at the session will feel left out. It causes a problem when it is not confirmed. Students will complain that they were left out. Just like the Monty Hall doors, the lecturer usually knows the exam contents, so those topics to review are a trap.

@MatthewWhited - Unfortunately, many students are overly shy. Part of the teaching experience should be to convey to them that this approach will hurt them in real life.

@eykanal, even if you "convey" to me that being shy will hurt me in life, I can't change that. That is just my personality. Not everyone needs to be an extrovert to succeed in life. Because I am that person - the overly shy one.

Shy students can be open and friendly too, it's not like they're vampires afraid of socializing light. But they won't just stand in the middle of a silent judging crowd of 120 unknown (and yes, UNKNOWN, big classrooms tend to form small friend groups and never meet everyone else unless required by the teacher) people and just ask what they surely think is the most stupid question ever.

If it's a social-inhibition problem, ask every student to write their question or "pass" anonymously on a piece of paper and put it into the envelope that you will pass around. Then take a short while to see if there are commonly asked questions and if not, pick interesting or useful ones. If all say "pass", sigh and go home.

@Dancrumb.  Pre-sent questions is a very good way to go.  It could be that more than one student offers the same question, thus giving the lecturer time to formulate his answer, also ask himself why a section of the group was struggling with a certain issue - Q&A's work both ways.

@MariaDeleva "extrovert" is not the opposite of shy and "introvert" is not a synonym. Now, having said that, I am personally both shy and introvert... nevertheless I've always been the one in the classroom who asked most questions, both in my country and in two others where I was an exchange student. Being introvert has nothing to do with understanding when it's time to ask.

A big issue is that they're reticent to be "that person" who monopolizes the time with their questions. They're also concerned about revealing their ignorance to their peers - despite the probability that their peers likely have similar ignorance. Hence this is why you're getting 25 students attending an optional review session, all hoping someone else asks the questions. An added wrinkle is that they're probably hoping you reveal what's on the test, and don't want to inadvertently waste time asking questions about things that are not going to be on the test.
Just because it's a review session for the explicit purpose of asking questions doesn't mean that your students aren't still students. As you're probably aware, students are (generally) horrible at in-class participation unless you explicitly construct the environment to encourage it. So I'd recommend pulling out the same bag of tricks you use during normal lectures to encourage class participation.
One I like is the awkward pause. We, as lecturers, tend to fill space. Typically it's less than a second between asking "Are there questions?" and  then saying something else. You need to wait a good 5-6 seconds after asking before saying your next thing. This gives students time to organize their thoughts and get up the courage to respond. The length of the pause is going to feel awkward and you'll need to fight the temptation to fill the silence. -- That's a good thing. The students will feel the same awkwardness and feel the same temptation - and hopefully succumb by asking a question.
You also should lower the stakes. One reason they don't ask is because they're afraid of looking like the unprepared one to their peers. (Yes, despite the explicit purpose of this session is to prepare them.) You need to re-phrase the purpose of the session such that they know it's not just for the bumbling incompetent. This can be rather simple:

Anyone have any questions? (*mumbling* "No.")
So we don't need this review section? (*murmured disagreement*)
Everyone has this stuff down cold? You are all going to get perfect scores on the test? (*vocal disagreement*)
There's not any topic you want to know better? (It's important to emphasize the "any" here.)

At this point I'm guessing that someone will ask a question, or point to a particular topic they think they should know better. You've just lowered the stakes. It's no longer about playing catch-up for the woefully unprepared, it's now something that's useful for anyone who isn't confident they'll get a perfect score (which is probably everyone who showed).
Take an anonymizing approach. They're worried they'll make a fool of themselves in front of their peers or you by revealing that they're ignorant. (Or worse, revealing that they think they're ignorant on some trivial point.) Remove that fear by asking them anonymously. Have them write things down on pieces of paper, and pass them forward without names. Directly asking for questions might not help much if they don't have well formed questions, but you can get around this some by providing a prompt like "Write down what you think the three most important topics for the class are, and how confident you are on your knowledge of them on a scale of 1-5."
You can then get the students' responses and quickly skim through them for topics where a number of people are expressing doubts. (Even if it's only a 4/5.) Then present that topic to the group as one where a number of people are struggling. This will likely prompt people to ask questions about the topic. But if not, you can always take a "peer teaching" approach by asking someone to volunteer a summary of the topic, which should segue nicely into questions. Even if not, you can summarize the summary, highlighting things that they got right and filling in the blanks where things are missing or slightly incorrect. (To encourage participation, be sure to emphasize points where they got things right, and minimize drawing attention to where they got things wrong while still correcting the summary.)

"They're also concerned about revealing their ignorance to their peers - despite the probability that their peers likely have similar ignorance." - this made me think that the ones having questions are usually the ones who prepared and thought through. Can we find an encouraging (not derogatory to others) way to say "if you gotta question, you are among the best here and you will help your peers if you ask it because they don't even know they don't know that thing."

I always make a point of announcing, for these kinds of review sessions, that "I won't prepare anything; if nobody brings questions, it's going to be an incredibly awkward hour" I try to do it in a way to get a bit of a laugh, but then make sure it's understood that it really is up to the students to make sure we have something to do.

Indeed, it's underestimated how much time can intervene between the asking of a question (including the question, "Do you have any questions about the course?") and receiving an answer.  Give it time.  The "awkward pause" works not because it's awkward, but because it's a pause.

Most of what you suggest in the first two sections would make students feel more like fools.

This answer would deserve an upvote for the use of succumb alone, not to mention the fact that all suggested points are simply perfect.

Anecdotally, I've heard of the question, "What is a question that someone might have about this topic?" to be effective (but I'm coming from more of a secondary education perspective).

Your students want to learn (otherwise they wouldn't have shown up), but don't have any specific questions. It sounds like they want a general review of subject material with specific regard to the test. If you have a test from previous years that covers the same material, you could work out selected problems from that exam with the class. When doing a specific example, I am sure there will be questions that will come up more readily than when holding a wide-open Q&A session.

Your first point is why I don't feel like sending everyone home :) But what if the exam is oral? Can't show previous exam in that case... We usually have students draw some topics (from a known set of topics which is quite similar to the syllabus) and tell about those in a structured manner followed by aditional questions from the examiner.

@Džuris: Speaking as a student, in that case I would appreciate if the instructor pulled a few topics at random and started asking detailed questions. As a student, (1) if I can't answer it, I then know I had better review that material again, and (2) if I can answer it, then I have a better understanding of how the oral exam itself will look/feel. Think of it essentially being "review by Socratic method".

@tonysdg One of my improvisation routes is taking a couple of topics of random and tell myself what should exactly should be covered in this question (without actually covering everything). That also seems a way to show how the exam will look. Your way seems nice and I'd like that myself as a student. However most students wouldn't like to be the ones who have to answer in the pilot exam.

@Džuris: If one of my peers was to show up to a review session and then be unwilling to actually participate in the review, my own response to them would be "why did you come?" That said, I wouldn't bother worrying about the students who won't answer. Find the ones that will participate with you, and focus on them. If others want to join in, that's great -- see if there's a way to tune your questioning to help them too. If not, that's not your problem - it's an optional review session, right?

If you don't manage to stimulate questions by the methods mentioned in other answers, you can certainly end the review session early.  But you shouldn't go home.
Announce that you'll stay until the end of the scheduled time, in case anyone has a question they'd like to discuss individually.  Sometimes students do have questions but are too shy to ask them in front of everyone, or they think their question wouldn't be interesting or relevant to other students (though usually they are wrong about that).  Also, since question sessions are often scheduled outside regular class time, some students may have to show up late; you want to still be there.
This also helps keep you from being perceived as "lazy" by the students.
You can also say "I'll be in my office if anyone thinks of more questions", so that if nobody comes you can get some other work done.  But be sure to leave a note on the classroom door for latecomers!

Here's a list of my tips. The formulations may be a bit exaggerated, but you probably get the points…

Phrase right. The phrasing of your own question makes a huge difference. Do not ask "Does anybody has questions?". Why not? First: The only correct answers are "Yes" or "No". While this sounds stupid and no student would ever answer with "yes" (one even more so for "no") it still is correct. Second: It sounds like having a question would be something special. Also, I would not recommend to ask "What questions do you have?". I would recommend to start the question session with: "Now we have time for all question you might have about the material. Please go ahead and ask your questions." This makes it clear, that it is perfectly normal that there are indeed questions, and that you very much expect to answer questions.

Announce early so that the students can prepare.

Rewards. Once I had the question session  at the same date when I had print out of the lecture notes ready. I started the question session and when the first guy asking a question I first answered the question, said "Thanks for the question! Here have a copy of the lecture notes."  I did not say anything more about that (in fact, I had announced earlier that everybody will get lecture notes) but still, it sparked more questions, and the students noticed, that questions are appreciated. Also other small rewards like sweets can be used (haven't done that myself, but people told me that it works, too).

Give the students time to think. After you said "Now it's time for your questions." you should wait in silence. Don't say anything more to encourage questions for some time. And with that I don't mean 5 to 10 seconds (which would actually feel like a long time for you) and I also don't mean 30 seconds (which feels like eternity for you) but I mean 1 to 5 minutes. I am serious. Think about it - how long does it take to formulate a question in case you aren't prepared? (And it seems like the students came unprepared…) I have been in questions session where there have been a few minutes silence (believe me, I looked at the watch) before the first question came, but then the questions kept coming… In some cases you may sense (after a  minute or so) that further encouragement could be helpful. I would not suggest to say the same thing again, but

Make clear that questions are for the students benefit and their responsibility. To put it bluntly: "If nobody has any question, then I do not have any answers." I actually say that in class, and it helps. If there are really no questions, follow the suggestion by Nate Eldredge.

Do not ask questions and answer them yourself. Once once you started to ask a question for the students, answer it or review some material on your own, the students will be back to "lecture mode". They'll takes notes, think about what you say, but will be passive again. I also had question session that ended with no questions and that was that. If this happens to you, it would be good, if the students would get a second chance to ask questions. An alternative approach could be, to ask extremely simple questions (well, you may think that they are extremely simple…) and wait until somebody from the audience answers. When I ask questions, and nobody answers, I usually say "okay, well have to leave this here, but if you have any answer at a later time, feel free to interrupt" or "okay, we may come back to this question at a later time".

+1 "... I also don't mean 30 seconds (which feels like eternity for you) but I mean 1 to 5 minutes. "

Give the students time to think is spot on. Several minutes may be required and it feels like an eternity.

My usual approach is to begin with a summary of what all the topics are that will be covered by the exam.  Or, as I put it, "Here's a list of all the things that you've forgotten we talked about." That serves to break the ice, lower the stakes (as suggested by R.M.), and prime their memory about specific topics (as suggested by eykanal).  After that, I usually get plenty of questions to fill up the time, though I occasionally have to wait them out through an awkward pause (as discussed in R.M.'s answer).

This is what my professors typically did in undergrad.  As a student, I found it to be very useful.

But that makes the session more than a questions session, and may give even more people who don't have questions that feeling that they have to be present. That is what leads to this problem in the first place. If you have useful things like summaries to share, they go in the normal lectures.

One technique I have used for this situation (in addition to other other fine suggestions in other answers) is to collect an FAQ list from sessions in previous years. If it all goes quiet and I don't want to force the direction of the material, I show them some of the Frequently Asked Questions (sans answers) and see if any of those questions pique their curiosity. I also include silly questions in the list.
They then may vote for or point at one of the items of the FAQ and the discussion can start.
In the first year of operation I seeded the FAQ myself.
You may find this useful also.

First, I warn the students in advance that these sessions will be entirely based around their questions, and that I will not be preparing any specific material for them.
At the revision session itself, I start with a quick reminder of the major topics that we've covered in the course (i.e., that the exam will cover) so that students have a "menu" to choose from. I then take as many questions as students come up with, recording them on the board, before answering any.
I find that the "menu" is a good way of getting students started asking questions. Once somebody starts, either others agree that topic X would be a good one to revise – which helps me to know that it is worth spending some time on it – or they see that no-one has asked about their own bugbear, topic Y. Either way, it doesn't take long to gather enough questions to last the whole session. Then I can choose to answer in whatever order makes most sense – by topic, or easiest ones first, or whatever.

The first sentence itself is already upvote-worthy. Telling students, in advance, that they need to have questions prepared is great, because it makes them actually think about what they do/don't know, instead of hoping that they will just gain wisdom through osmosis. Furthermore, instructors can inadvertantly reveal information about the specific exam questions when they come up with review session exercises; making the students come up with the questions mitigates that pitfall as well.

Consider drafting an optional block of exercises or applications to do together if this happens in a period. I've used fairly simple conceptual exercises from a completely novel perspective (not a direction they've seen before); or possibly a scan of vocabulary, fill-in-the-blank, or find-the-error questions from the book.
Or even possibly an entirely new optional lecture topic. On the one hand, these are more dedicated students; on the other hand, desire to focus on the test may make them restless in this scenario.

I've done this before: Show questions that were given on previous exams.
In the case of essay questions, I've also shown some answers that were given by students, and explained why one answer was scored higher than another. This gives the students a better idea of what I'm looking for in an answer to an essay question.
Other problems can be given to students to work out in pairs, with a class discussion afterwards.
In other words, if no one has any questions, simply say, "Okay, then - if you don't have any questions for me, then I have some questions for you," and give them some problems that do a good job representing the kinds of things they might find on the exam.
I've found this to be effective, but you'll have to do more prep work than you usually do. However, at least you'll have a backup plan if the student-led discussion goes nowhere. Moreover, the real up-front work comes early on. Once you have a library of sample or former questions to draw from, those can keep getting reused from year to year.

Ask them to send you questions in advance, and begin the session with questions received in advance.  Include in the announcement of the Q&A session that you will spend the first n minutes on those questions.  Those questions and their answers will likely inspire more questions and get the session going.  (If no-one actually sends you a question, you could reuse an old one or plant a fake one just to get started.)
Prepare a list of topics - either a handout or a slide (or both).  (This can be the same as the list on the review sheet, or an outline of the sections covered.)  If/when no-one has a question, refer to the list and ask which topic they're most interested in or least prepared for; if you can rattle off subtopics they can narrow it down.  If that doesn't inspire any specific questions, it will at least give you an idea what part of the material is most valuable to review.
It can be hard for students to translate a lack of understanding into a particular question.  (When I took undergraduate Statistical & Thermal Physics we spent a lot of time in uncomfortable silence, understanding too little to form useful questions, the professor speaking far over our heads.)  If students come in hoping to hear questions from others I expect that to mean that they know they don't understand it all but they haven't been able to find the right questions to pursue to connect all the dots for themselves, and you have the difficult job of guiding them to that.  There doesn't seem to be any general solution to this problem, but I usually start by trying to discover particular gaps in understanding, which I hope my suggestions above will help with.

+1 for "It can be hard for students to translate a lack of understanding into a particular question"

Our teacher create a shared document for "open questions" one week before the exam. Everyone can write into this document questions related to the course material with reference to one or more specific slides. That's how we do it.

You can propose:

One extra point for them who asks dull question.
Two extra points for them who asks good question.
No point for them without question.

You can also set up open and anonymous forum, where students can chit-chat and discuss the subject. Join it after some time, one month perhaps, and answer as many questions as you can/want. Serious ones seriously, funny ones funny, but correct, way. Bring some of them to the Q&A session.
Joingly...
When I came to university when we were leaving sequences and upgrading to functions our math professor stated, that he was bored and generated pi for us to show us what real numbers are. When he showed fifth slide full of digits, he had had to be really bored, he covered the small ellipsis in the end and said "And this is just rational number... I was also so bored that I changed one digit... Who will be the first one to find it?"
He got the answer right before midnight. And confirmed it at 3 AM.
He gave us all the questions in the exam. All 12 000 of them. In Q&A sessions we can discuss them, and we were. The sessions took 3 hours each...
This was the best course I have attended so far.

This is somewhat echoed by other answers, but only as part of a longer list of suggestions. From past experience, I think it's crucial to:

Tell students well ahead of time that they're expected to bring
questions, and that you're not supplying pre-packaged material.
Nicely point out after asking, "Who has the first question?" that
you're willing to wait silently for as long as it takes. This can be
minutes! After facing ~30 seconds of silence, I've suggested to
students that they talk to other students near them for a few
minutes, and come up with questions. This works well.

One of my professors used to hold "Online Office Hours" on Sunday afternoons from the comfort of his living room couch.
The Software  used was Adobe Connect (no affiliation on my part) and allowed him to use his Ipad as a streaming and doodling device.
Questions could be asked either by typing in a chat box (this grants anonymity and makes it easier for insecure students to ask) or by voice as in any conference call.
All in all doing it like this may lower the bar for actively participating and as an added benefit you do not need to commute.

One of my lecturers would ask people to email him questions for a question session.
This worked well because it allowed him to prioritise questions and if he had a large amount of questions he could book an extra session.
Email has other benefits like anonymity to other students, removing the problem of students forgetting the question and allowing you to respond to any issue with a question before the session

You can ask the students to send questions to you before the session and go over them during the question session. This way students have more time to phrase their questions, and don't have to be the one that asks "dumb" questions in front of their peers.
You can also communicate beforehand that if there are no questions send at all before a certain deadline (say, one day before the question session) you will cancel the question session to not waste anyone's time.
This is what some of my professors did, and I feel that this way there are more questions than in other question sessions.

In my experience, students are always hungry and poor (ok, a bit of a generalisation, I know).
So a good ice breaker can be to offer something for free such as chocolate bars to bribe people to ask questions. A chocolate for everyone who asks anything until the bag runs out will likely get questions started, and once the ice is broken there'll likely be more questions thought of.
As an anecdote, I know a real estate auctioneer who gives a bottle of champagne away each  auction. Not to the person who wins the auction, but to the first bidder, to get things started.

One thing that works well for me is, the class before the problem session (so 2 or 3 days earlier) pass out copies of an old exam for the class. Ideally, choose a hard but fair one. Tell the students that I would appreciate it if they would look at this, think about how they would do, and come prepared with questions about things they might have had trouble with.
Not all of them do it, of course, but enough do to get the discussion started.

Adding to R.M's suggestion about taking the anonymizing approach. Google Slides has this feature called Slides Q&A which lets the audience ask question whenever they like anonymously or by name. In my experience, this has increased the amount of questions asked in lectures. The lecturer can now choose when to answer certain questions and filter out less important questions.
But an other suggestion is to let the students set up an agenda before even inviting to a question session. This can be announced a week ahead and that if enough problems, topics or other discussions are submitted by mail, you will set up a extra lecture for preparing for the exam. This will change the format of the sessions a bit, but will most likely get the students to state what they need help with.

Omitting for the time a discussion of many other variables, effective teaching, including Q & A sessions, is based on the quality of the relation between the teacher and the student.  A major factor in the development of such a relation is time spent feeling personally connected.  In a class, the likelihood of feeling that connection probably has some sort of inverse relation to the class size.  Long term, more discussion on optimum class size

The easiest thing I would say to do is to bring up hypothetical problems that are on the exam, maybe make one up on the spot, or use some from previous assignments and ask them if they know how to answer it.  This may answer questions about a topic that the students didn't even know they had.  Also constantly encouraging students to ask questions can be beneficial - some students may have questions but are too nervous to ask.  From personal experience, teachers who consistently encouraged my fellow students and I to ask questions were always the ones that got me out of my shell and got me talking.

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I have a written original research (Genetics) from my PhD. My thesis advisor has since retired and my lab has closed (in 2016). How do I publish this?
I have a well-written original article that has yet to be published. I had it drafted (manuscript for submission) along with my Ph.D. thesis and since my lab had closed and my thesis advisor retired, I haven't gotten a chance to publish it. During my Ph.D., I presented it at various congresses and had even gotten recognized at conferences for the data and people had come up asking when I may be publishing that data. It has been 4-5 years now as I have moved to another opportunity after my Ph.D. and never got around to publish that data.
What are my options now? At this point, I am fine to publish it in any journal. Just want to submit it somewhere for public access as I believe this data may be useful for future research. Also, the topic is specific so there is not a lot of research on that theme these days which is a further reason why I think I should publish this. My lab was the only lab at that time dealing with specific genetic mutations (in mice) so I do think this will still be relevant for the field.
Where can I send this to? As I have to do it myself, I'm looking for cost and time effective means. I am not worried about the impact factor at this point, just for people to get access to my research should there be interest. Since the lab closed, I will not be able to conduct any further studies if reviewers request.

It is a bit unclear what you are asking for. Just a suggested venue? Or something more?

Are you the sole author? Submitting then is easy, otherwise you will need to contact your advisor and any other authors. Publication charges might be trickier, but not all journals have them (or they can be waived). Of course, if in a field that uses arxiv or something similar you can just post it there once you have an account.

Another consideration is that you may need to update the literature review, but if there isn't too much research on the topic these days that shouldn't be too much work.

You publish it by submitting it to a journal and getting it accepted if it is good enough. As @Buffy said, what else do you need to know exactly?

@JonCuster You'd be surprised - I vividly remember myself sitting there, in front of the finished article which looked just like any article from a journal X and not knowing what to do next. Before you do a submission on your own, it is a completely opaque black box turning written articles into publications - all that knowledge about journal workflows and whatnot doesn't do anything until you walk that path for the first time and realize there's no more to it than what you already know. At least this was the case for me and I know I am not alone in that.

@Lodinn I mean, OP has a PhD and presumably has figured that part out by now.

@Scientist Presumably though, you do need to get in touch with someone about whether you have permission to share the data.

@AzorAhai-him- Eh, one could hope, but that is not a given (at least I know of a handful of examples of people graduating with all of the articles written by a supervisor, and a few more of articles (co-)authored by them but submitted by a supervisor). A PhD is supposed to make one an independent researcher, but does not always succeed in that.

First of all, it's great when an author decides to get an old thing published, rather than leaving it as a dangling forever preprint. I've done this several times, as life sometimes gets in the way of submitting something promptly. I think a lot of people were in this position due to the disruption of the pandemic. I've also seen it hundreds of times in math. Here's what I recommend:

Update the literature review to mention papers that have come out in the years since the research was conducted.

Update the bibliography. In math we often cite preprints. Over 5 years, some would have been published.

I usually add a line to the acknowledgments explaining the timeline a bit. Probably you are thanking your PhD advisor anyway. It's easy enough to say "This paper was originally written in 2016 under the supervision of Professor Z, and the author is grateful to him and to University Y for providing the facilities where the research was carried out."

As a referee or editor, lines like that help me figure out the setting of the paper within the literature. It can also help the referee figure out what is and is not a reasonable request (e.g., if they were thinking to ask for new experiments, a line like this helps them realize that won't be possible).
Lastly, you need to identify a journal. One strategy is to look at journals where the papers you cite/built on ended up published. A related strategy is to look at editorial boards to see which journals have editors in the research area of the paper. Doing this will give you a set of journals. You might want to aim for the lower end of the spectrum in terms of journal ranking, simply because the work is slightly out of date, it won't be possible to run new experiments since the lab closed, and the editor also knows the paper is unlikely to generate a lot of citations since it has already been known for several years (since you presented it at conferences). Still, many editors and referees like to see old preprints get published, for the sake of the scientific literature and, as the OP said, to get the data out there.

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Applying for graduate programs in different subfields?
Suppose you have done work in two subfields of a main field, and have a keen interest in both of them. Some examples might be,

Main field: Physics, Subfields: Experimental Quantum Optics and Condensed Matter Theory
Main field: English, Subfields: American Literature and Medieval Studies/Literature
Main field: Mathematics, Subfields: Topology and Numerical Analysis

Is it a bad idea to apply to some programs in one subfield and some programs in the other subfield? I'm faced with this dilemma, and my hope is to choose which subfield to go into based on my acceptance offers.
Or is it better to just buckle down and only apply for programs in one subfield? Certainly that would make you more focused, and thus more desirable as a candidate (and also more focused as a researcher).

Do you mean at the same university? Because if you apply to one subfield in one university and a different subfield at another university, how will they know?

The main problem you'll face is the increased workload you'll face in preparing your application materials. Depending on the programs, you'll often be expected to identify faculty you'd be interested in working with, and you'll be expected to succinctly show why your research interests fit with that person. The statement will also be viewed as a whole, so you will inevitably need to either focus a single application around certain aspects or be clear that you are open to a range of sub-fields within a given discipline.
Some people like very focused statements, while others are still welcoming of more broadly stated interests, so one is not necessarily good or bad. That's part of your unique situation and interests, but the advice I've been given is if you really do have broad interests, express that because it will attract advisers and programs that value that. But if you really only want to study certain things, say that because that too attracts you to the right people you're more likely to be happy with in the long run.
Most people (all I've talked with, anyway) find preparing multiple drafts of all the materials stressful as it is, so having to prepare multiple versions with very different tones and a different focus likely will take you some more time and effort to do well. So if you are applying to only one sub-field per application you can still focus your statement on that field and just the right people to work with in that field - but you are going to have to create at least 2 sets of very different personal statements, each of which will be customized per program.
As long as you are OK with the extra workload, I am not aware of any other downsides beyond application fees if you are applying to more programs in total. Applying to programs with different sub-fields is fine if you really interested in both, and will not generally make you a less attractive candidate because no one but you (and maybe your letter writers) has any idea you're applying to a variety of programs.


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Can I be admitted to a graduate program in Computer Science with relevant work experience, but academic background in Electronic Engineering?
As mentioned, I have an undergraduate degree in Electronic Engineering but my work experience (>3 years) is in server-side software development including development of horizontally scalable and distributed services. I am interested in getting a graduate degree in Computer Science. However, most of the programs I have looked at require an undergraduate degree with computer science courses. There are a few programs that do not have this requirement and I shall be applying for.
My question is; will it be possible for me to prove my eligibility purely on the basis of my work experience and thus get admitted into a graduate program? I am fairly confident that I have (most of) the required skill-set and knowledge base and that I can catch up on the rest easily.

"graduate degree" - masters or PhD?

Upon re-reading your question, are you possibly applying to a part-time Master's program?

@ff524 I'm looking to get into an MS program

@Compass No, I intend to get into a full-time program. Although, I'd probably settle for a part-time one

@user3139006 You can probably get by with demonstrating mastery of a core language like Java or C++. Would probably need to contact each school you apply to in order to clarify, though.

@Compass I'm hoping my (fairly reasonable) knowledge of C, Python, Erlang and JavaScript will do the trick.

@user3139006 Probably.

We really need a boilerplate question "Can I get into frad school in X with an undergraduate degree in Y?"  The answer is yes, you can.

There are cases where you can get admitted even though you have another (closely related) degree because some programs give students a chance to study things on their own, though this usually varies between countries and universities.
Another thing that could affect your chances is the type of computer science that you want to apply for: it's probably going to be difficult to get into a theoretical computer science program (some of my colleagues at my current university have been rejected from a computer science master's program at a respected university because their undergraduate degrees contain the word "engineering"), but it might be easier to get into some more applied field.
The best thing would be to contact someone at your target universities; they are usually able to tell you if you have a chance to be admitted.
Hope this helps and good luck.

That sounds like reasonable advice, thanks! I'd upvote your response but I do not have any reputation points at the moment so I'll mark it as the answer.

it's probably going to be difficult to get into a theoretical computer science program — [citation needed] This depends on your interests and experience, not so much on what degree you have.


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Completing premed requirements after Bachelors
I'm thinking about applying to medical schools in the USA. Many medical schools require a two-year course in chemistry including organic chemistry and a year long course in biology, as well as requirements in mathematics and physics.
I have already satisfied the mathematics and physics requirements and a portion of the chemistry requirements during my undergraduate study. Are there any programs that will let me cherry pick classes to satisfy the application requirements?

Yes. At many (most?) universities in the United States, you can take courses as a non-matriculating student to get your premed requirements.

I think that's a good idea. Would there be any reason that a school would view courses taken in a standard premed postbac program more positively than courses taken as a non-degree/non-matriculating student?

@Mark I have never served on a med school admissions committee, but among people i know who went to med school it's very common to fill prerequisites as a non-matriculating student.

As an ex-premed, I can attest to this. That being said, I believe most chemistry classes require lab time, so I don't believe taking them online is an option if you were considering that.


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What is Cumulative Impact Index of a journal?
I recently came across the term "Cumulative Impact Index". I came to know about it here, which includes the statement, "publication of
5 International Journal papers, each Journal having a cumulative impact index of not less than 2.0".
I knew of peer-reviewed, but as this term was bit new I surfed the web and visited Impact Factor on Wikipedia, but it is still not clear.  Can any one of the learned members may kindly explain to me, preferably with an example?

Related: The Agony and the Ecstasy—
The History and Meaning of the Journal Impact Factor Doesn't go into the math specifically from a quick scan, but basically, impact factor is a correlation between number of times a journal's article is cited to how many articles it publishes.

The term "Cumulative Impact Factor" (or cumulative impact index) has been used with different meanings by different authors.
One definition is that the cumulative impact factor for an individual author is simply the sum over all papers that the author has published of the impact factors of the journals that the papers were published in.  e.g.
You've published 3 papers in Journal A that has impact factor 1.2.
You've published 1 paper in journal B that has impact factor 1.9.
You've published 2 papers in journal C that has impact factor 1.7.
Your CIF would then be
CIF=3*1.2+1*1.9+2*1.7=8.9
Like all bibliometrics, it's a very crude measure.   You can get a high score on this measure by publishing a lot of research in journals that aren't very prestigious, or by publishing a very small amount in highly prestigious journals.
Furthermore, the metric says nothing about the impact of your research compared with other papers in the journals in which you've published (e.g. you might have published a very widely cited paper in a journal with a low impact factor but you won't get any extra credit for that.)
The term "cumulative impact factor" has also been used for measures of the impact factor of a journal over extended periods beyond the usual definition of impact factor.  See for example:
http://www.garfield.library.upenn.edu/papers/impactofcumimpfacs.pdf
I don't believe that ISI has ever defined this term in their formal reports on the impact factors of journals, so I'm afraid that any use of the term will depend on the particular group or person that is computing the cumulative impact factor.

From the quoted statement, the OP is clearly asking about the latter, so maybe you can elaborate on that.

I don't think that there is any one standard definition of the cumulative index factor/index for a journal.  You could extend the usual two year period to five years, or ten years, or to the life of the journal, or ...

There are apparently a bunch of universities in India that want faculty to have published at least "five papers in journals with a cumulative impact index of 2.0."  Presumably these universities are using a report that someone used the raw ISI data to get these cumulative impact indices.

Thanks for the answer and discussion. But one or two things are slightly confusing till. I am getting back to you soon. Regards, Subhabrata.

So, here["https://en.wikipedia.org/wiki/Computational_Linguistics_%28journal%29"] does Impact Factor come for 2009? Regards, Subhabrata.

Subhabrata; your link is broken, but I assume that you intended to link to the Wikipedia article on the impact factor.  The regular impact factor includes only citations of articles published in the last two years.  Cumulative impact factors extend this period to some longer time period.

Dear Sir, Thank you. I am reposting the link. It is the Wikipedia article on Computational Linguistics(MIT Press Journal). ["https://en.wikipedia.org/wiki/Computational_Linguistics_%28journal%29"] Regards, Subhabrata.


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How much do my career prospects suffer if I start a mathematics/CS PhD. at the age of 27?
I was doing a PhD in a different subject and that was going nowhere and I lost interest in everything that I was doing. But I always loved mathematics and during the dark days of my 3rd of my (former) PhD. I happened to meet some exceptional theoretical CS people who encouraged me a lot. One of them got me as a TA and I am starting afresh in this new field.
How optimistic should I be about a career ahead?
I want to be a researcher/faculty.

Starting PhD at 27 is not old at all.

I will start my PhD at 29. Don't feel old.

I finished my CS Master's at 26 (27 now). If you told me I was getting old for a PhD, I'd just give you the old fish eye. I think my dad got his PhD in CS in his mid-30s. I mean, let's say you finished your PhD at 35. You'd still have 30 years of potential teaching ahead of you. Some of your students wouldn't even be born when you finished your PhD!

How much do my career prospects suffer if I start a mathematics/CS PhD. at the age of 27?

Not at all. 27 is not considered "old" by any stretch of the imagination.
Also see this answer on MathOverflow and this question here on Academia about a PhD student who is actually unusually old.

Suffered compared to what? What is the alternative?
If the alternative is completing your current program that appears to be going noowhere, how long will it take you to complete that? And, given that in your words you have no passion for it, how good that research would be?
If the alternative is dropping out of PhD program? Well, your odds of becoming a faculty with no PhD are zero.
Take what you can from your current field, and go where your passion is. It is 30 years career ahead of you. By already working on a PhD, you learned basics how to do research, write papers, etc... The fields will be different, but you still have a head start. And whatever your other field was, you will bring a new, fresh perspective to your new field.

I bet there are a lot of faulty people with no PhDs.  Faculty, on the other hand...

@AAA What, and ruin my joke? :-)

As some people have stated before, 27 is not really that old to be in continuing education. I work with a colleague at GTRI who is just starting a Math PhD at 27 so its not as unusual as you would think. I think it would really depend on which industry you are involved in or plan on being involved in.
If you like/want to run in startup scene than the time you spend earning a PhD may not serve you as well as trying to get involved with an incubator program.
If you are in industry, but not doing R&D, than you may not need a PhD either. Most CS is not at the PhD level, even when technically advanced.
If you are working for a research lab affiliated with a university, or working with a company that does/funds PhD level research programs in Computer Science, than go for it. Especially if you are going to be working while you are enrolled, many people roll their career work into their research project.

You're not alone and you're not old. I started my PhD when I was 25, hated it after 1.5 years, actually found out what I fancy doing, applied for a PhD in that field and I'm starting that PhD next month. Losing 1-2 years is way better than spending 3+ years in a field that you don't like (moreover, you're likely to perform below your potential since, well, you don't like that first field. Imagine what you can accomplish in a field you actually feel excited about).


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Combining absolute and curve grading?
I know some advantages and disadvantages of using absolute grading as well as curve grading. Next semester, I plan to combine both by first grading absolutely, and then use curve grading to add points to the students.
For example, if one gets 80% (usually an A) absolutely, but when curved he gets a B, then I will use only the absolute letter which is the A. However, if one gets 70% (B) absolutely but when curved he gets an A, then I will use the A.
My reasoning is just like those who support curve grading. I might prepare too difficult exam tests or some things might not go according to plan during my course. While I actually support absolute grading, I think this might be a good idea. (?) I don't like the idea of just purely using curve grading that can lower high grades, or purely using absolute grading which is not always appropriate as university students' and instructors' performance are not absolute.
Of course, evaluating each course will always be the top priority, but this grading I hope can provide some small "healing" to error-prone college courses each semester.
ADDED:

The curve I use is the usual bell curve for distributing A, B, C, D, and F..
I am teaching mathematics for undergraduate.
There are less than 30 students each batch.
This is a new program in this university..and there are only 2 batches..so "past grades" are not recorded just yet unless I take data from other universities..which might not be a good idea?

All the inputs are very helpful. I do not plan to strictly use curve grading as I mentioned. I also believe the longer this program goes the better we can get..

Say a bit about the scale. Do you have 30 students or 300 in a class?

Ill add to my post.

You have assumed that students' performance follows a normal distribution, with a grade of C in the middle of that bell curve and very few grades of A or F at the tails.  My own experience, and that of my peers, is that a typical college class has a cohort of good students who will mainly earn grades of A or B, some number of poor students who will earn unsatisfactory grades, and a few "in the middle" students.  That gives a bimodal distribution, with the valley just about where the peak is in that bell curve.
I urge you to look at past classes and check the grade distributions when you have used absolute grading.  If you don't already have something very like a normal distribution, forcing one will distort the assessment results badly.
Your question says your goal is to help students, and the problem is that assessments may be too difficult or the course didn't go as planned.
I'd like to suggest a different approach that's a little more work than artificially distorting the grade distribution, but is much easier to explain to students.  (Um, and to department chairs, if it comes to that.)  You haven't mentioned your field, nor whether your students are undergraduates, but if your examinations can have a reasonable number of questions, measure the performance on every question and then exclude from the computed grade any where performance of the class as a whole is below some cutoff you establish heuristically.  Almost everyone's grade will go up, and you can easily explain that you excluded those questions or problems that were "too hard."  (In the rare case where a student gets only the "hard" problems right, you can make an independent adjustment; that never happened to me.)  I do this with a spreadsheet that has student names in the left column, then one column for each question.
Once you've evaluated an assessment, you must look at the questions you excluded as "too hard" and If the difficulty was with the question, fix it.  If you believe the question is reasonable, then you need to review the material with the class again.  When that happened to me, I'd warn the students that they would see that question or problem again, and so they should prepare for it.
If you do this for every assessment, you will find that your exam questions and your coverage of the material converge, and after a very few semesters, you will no longer have questions that are "too hard" and you'll be comfortable with absolute grading.

I'll note that automated grading systems or scan analysis will probably do a per-item analysis for you. Writing a program yourself to do it isn't that hard. Even spreadsheet macros can do the job.

@Buffy Absolutely.  I used a spreadsheet "template" with the formulas set up beforehand.

I'll also note that there is a statistical measure (I forget the name) that correlates overall performance on an exam with performance on a particular question. If the correlation is negative, it is a bad question, since the "better" students tended to do worse on this question and vice versa. Maybe someone can supply the name and point to the formula for it.

@Buffy: That's literally just correlation, or more officially, the Pearson correlation coefficient r. N.B.: The Blackboard LMS obscures this by using the nonstandard term "Discrimination" (explained halfway down this page).

@DanielR.Collins, actually, it is something else, maybe subtle. I remember a name associated with it (not Pearson). I haven't used it (nor auto grading tools) for more than 30 years. But something like "Discrimination" as you note.

@JeffreyJWeimer  The goal is to lean not to write questions that are "too hard," while not penalizing students when it happens.  See the last sentence of my answer.

@Buffy: Okay, but Discrimination is defined exactly as the Pearson correlation coefficient on that linked page above.

This is indeed amazing suggestion. However my concern with this is for the future. I am sure we know that if students know this exists, future students will challenge the questions themselves and demand that some questions are just too hard.

To ur point where we may believe that the question is reasonable and we should let them know that they have to be prepared, we make sure that the question is based on the curriculum learning outcomes, which are established by considering standards applied in various programs in this country as realistic as possible. What if, by that argument, we always are sure about the question? Therefore, since I better should never like blaming my students, the course evaluation must be done, including my lecturing..but that is for the future, is it not? Hence, I came up with this idea.

Oh by the way I added some points that can be considered as part of the discussion..

@bms  The classification as "too hard" comes from the professor, either heuristically as I suggested, or more formally using a statistical functional.  So, a student who challenges a question as too hard can be told, "No, it wasn't.  Most of the class got it right."

I see. Correct me if Im wrong. U suggest that the hardest question score can be dropped and rare cases are treated differently (like bonus right?), right? The calculations for the bonuses are still challenging, don't u think? I mean, if only one student answers correctly that question, this student should have added points, right? Is this the same as weighted scoring, where the weights are on the questions, but the weights are determined relatively AFTER the actual grading?

Actually, I believe that strict curve grading is both immoral/unethical and a misuse of statistics. It is immoral since the grade a student gets isn't dependent on only their own efforts but the efforts of others and in some ways just the current composition of a class, which is partly determined by sheer luck.
It is a misuse of statistics, since a distribution, such as a normal distribution, is intended as a derived or emergent property of the characteristics of large population. But when use in strict curve grading, it is imposing a distribution on a small population for which you have little or no evidence of its appropriateness. See the answer of Bob Brown for a further discussion of distributions that are likely, but not guaranteed to occur. Suppose your Physics class is composed of 30 Einstein clones, for example.
Strict Absolute Grading is also immoral/unethical (but perhaps less so) because it doesn't admit the fact that students are humans and humans make mistakes. Some of those mistakes are inconsequential, but absolute grading can make them very consequential. Also, in most uses, it doesn't reflect the fact that humans can learn from their mistakes. So, it turns the classroom into a grading scheme as opposed to a learning environment. The other problem with absolute grading,  though not an ethical one, is the constant whining of students who have "just missed" the next mark. You would like to have a system in which that isn't necessary.
The other question that arises is what the grades are based on. I find that over reliance on exams, especially high risk exams is also counterproductive educationally. Among other things, they can force the students into harmful practices such as cramming. It is difficult, though not impossible, to create an exam for which cramming is completely ineffective, but such things can also be very difficult to evaluate. If an exam only evaluates objective facts, it has, in my view, little educational value and can be actively destructive.
I prefer, then, to base grading on projects and writings rather than exams and tend to favor group work, though that is an artifact of my field. I also try, as much as possible to think of myself as a teacher, not an evaluator. The educational process should dominate the whole system with evaluation being useful to learning, not just to classification into grade buckets.
One way to do this is to permit (and encourage) re-work by students after grading and feedback is given. The underlying scoring rubric is like that of absolute grading, but the first evaluation isn't necessarily the final one. I've even asked students to resubmit work after I've discussed proper solutions with the class. A re-submission can earn additional points/marks, but not up to full marks, so as to discourage too much dependence on re-work. There is no whining when students just fall a bit short. They can make corrections. Even correcting and resubmitting inconsequential mistakes is an advantage as it reinforces the learning in their minds.
This resubmission system need not be overly burdensome on the grader if students turn in the old work (along with the grader comments made there) along with the new, and marking (highlighting) the changes. A glance at the package can be enough to reassign grades for the new work.
Note that what happens when you do this is that you spend more time and effort with those students who most depend on your teaching, rather than those who nearly always turn in essentially perfect work.

Resubmission is a great solution indeed. The issue which you also mentioned, tho, is concerning the time. I don't mind the effort honestly, but here we are challenged with conducting research too..which is time-consuming. Therefore, we usually give only one additional test for some nearly failed students after the final exam..which is not common since it depends on each lecturer who has different research load.. I usually give it, but now I plan to give the test after this idea is done, perhaps?

Please do not plan on using a curve.
Combining absolute and curved grading makes no sense.  Curves should be used when a test or assignment was surprisingly hard.  You should avoid curves when possible.

I might prepare too difficult exam tests or some things might not go according to plan during my course.

Based on previous exams, you should be able to calibrate future ones pretty well.  I am now convinced that even if I posted the actual test with the answers beforehand, people would still fail it.  Good students will study and make good grades, bad students won't, no matter how much hand-holding you give them.
Here are some alternatives to grading on a curve that will give students a chance to make a better grade.
Allow students to redo problems they missed on the exam for 25%-50% of the points
So a 10 point question is now worth 2.5 or 5.  This makes students do some work on their own, but given others got the exam right, it should be pretty straightforward.  This lets people possibly jump one letter grade (usually).  For added security limit the change to say 15 or 20 points total.
Assign "bonus-points" homework assignments
Let students earn a set amount of extra credit that will be applied to their grade at the end.  This has the advantage that students that may be working part/full time, or with a high course load can plan in extra time up front.
Allow students to drop one test
Drop every student's lowest test grade.  Some professors don't allow people to drop the final, but I'd encourage you to allow students to drop the final if they have an A.  Very few students change their end-of-course grade by the final, and most A and B student will generally do well on it.
This has the advantage that if one test is extremely difficult, it will just be the one most people drop.  This is essentially a built-in curve but has two advantages
1) A curve pit students against each other - this doesn't.  This allows every student 1 "do-over" if they had a bad schedule or an emergency.  Great students are essentially rewarded by not having to study for the last test.
2) From a professors perspective, if one test is just super difficult, you've already built in a mechanism to fix it.  The "super hard test" will get dropped.

Also, I'll point out that dropping n tests is conveniently built into existing LMS's.

Yes..especially that dropping test part, I considered it too. I believe what you mean by hard is that the question is not reasonable based on the learning outcomes, isn't it? The problem here is also that my department never allows too many students to fail.. When students do not reach the standards, they add points..which is something I won't do.. Hence, as you pointed in the first paragraph, isn't curve (not strictly) a solution?

(con't) as it might not be possible due to the pressure of the deparment..

Doesn't the opportunity to redo questions for partial credit favor the students who did poorly?


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What to do if your phd went well?
I will soon be in my final PhD year and am about to start my job search. Suppose that for some reason I suspect that I did quite well, research-wise. My main question is:

What is the optimal job search strategy/advice in this case?

Generally, I can think of two options that are "better" than a regular postdoc:

A fancy fellowship or a named postdoc position
A tenure-track (TT) position

First, do I have to decide between postdoc and tenure track before getting an actual offer? That is, is there anything special I need to do when applying for tenure track besides pressing buttons on mathjobs.org? (Also, is there an option I missed?)
Second, I have questions about each option.
Option 1: Is there a comprehensive list of "really good" postdoc fellowships? Or can we combine a list here? I know that there is Clay fellowship, Miller fellowship at Berkeley, Society fellowship at Harvard, Veblen instructorship at IAS. What other well-known similar positions come to your mind? How do they compare between each other, and to a regular postdoc or a tenure track?
Option 2: Since this depends on many random factors, it seems like if I apply two times, that is, apply next year and then (if I only get a postdoc) apply after my postdoc ends, the probability of landing a nice TT job increases. On the other hand, the quality of a TT job offer I can get after a postdoc will likely be higher because I will have better publications. So I'm not sure whether it is even worth it to seek a TT position now as opposed to settle for a postdoc right now and just apply for TT in 3 years after it ends. Although I've seen people switch between TT jobs, I don't know how common that is. Also, how much of a blocking factor here will be not having lots of teaching experience? (I have some but not as much as most postdocs.) Finally, I understand that when applying for postdoc, I should prefer schools where my area is represented best, but this seems not too crucial when applying for TT, right?
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To be specific, the field is pure math, the country is US, and I am not eligible for NSF.
A couple of other disclaimers: I understand that the answer depends on my priorities, so let's say the top priority is landing the "best" possible tenured position I can at some point in the next ~7-10 years (which includes not having to teach too much before getting a TT job offer; money is not too important to me personally at this point). I also understand that it's very hard to tell objectively how good my research is, but for the sake of argument let's assume that it's good enough, comparable to other TT job applicants.

You’ve completely written of industry research, working at a think tank, government employ, and several other contexts where you would be given money to do research outside of a university. Is there a particular reason for this? If so, you should address it in the body of your post, if only to guide discussion and inform us about your assumptions. Almost every PhD is marketable in some non-university setting.

Are we talking potential future Fields Medal candidate level, or not quite?

@StellaBiderman Thanks, I agree that there are many other great options in industry. But I think the question is already a bit too broad, even when restricted to academia. (Which is the only option I'm interested in right now anyway...)

If your ultimate goal is to have a tenure in Academia, then of course, grab a TT job offer when you have one. I am not sure what you're asking here.

@AlexanderWoo Not quite (yet:). Sorry if the question is stated too arrogantly. What I had in mind was a solid range of phd students who did like a phd + 2/3 of a postdoc during the 5 years of their phd, which looks pretty common to me. (So in particular getting Clay seems like a long long shot, but I'd still like to know what are all the options available.)

@scaaahu What I'm asking is: what is better, getting a TT job at, say, Columbia right now or at, say, UC Berkeley in 3 years? I don't know whether it's easy to go to Columbia for 3 years and then switch to UC Berkeley because there will be no "organized job search" once I'm already on a TT.

You won't know what's going to happen 3 years later. Maybe the president will announce 10 billion US$ fund for mathematicians. Maybe we'll go through a financial crisis, no university would offer new TT jobs. Who knows? Grab it when you have it is my point.

You're 1st question is a shopping question, which is off topic for the site. Your 2nd question seems to be specific to your situation.

You’re overthinking things (but not by much, and in a way that is both typical and understandable given your current situation and vantage point on the job market for academic math positions).
The main things you need to know are:

You don’t need to make any decisions until you get an offer.
You don’t need to do anything “special” when applying for jobs other than follow the application instructions, and there isn’t much else you can do to gain an advantage, other than obvious things (like proving a major result, winning a major award, and having strong visibility in giving talks at various places, especially departments where you’ll be applying).
Your chances of getting a tenure track offer straight of your PhD at a very good (say, top 50 R1) US university are pretty low, assuming you are an average person who meets your description of yourself in the question.
Your chances of getting a tenure track offer at one of the top math departments in the country (like the one you mentioned in the comments) straight out of your PhD are extraordinarily low, again assuming you are an average person who meets your description of yourself, and even assuming you are quite a bit above average. Of course, there is a possibility that you are a person for whom this is a realistic prospect, but if so then I would say you have been rather modest in your description of yourself.

The bottom line is that at the moment your optimal strategy is the obvious one, which is to apply to any place/position where you think you have a realistic chance of getting an offer (and if it doesn’t embarrass you too much, even places where you don’t think you have a realistic chance). Once you get some offers, you may or may not be faced with a minor dilemma of the kind you seem to be worried about. But there’s a good chance that won’t happen (e.g., you’ll get a few postdoc offers and it will be fairly obvious which one is best for you). And even if it does, I think it will become fairly clear in due course what is the right choice. Trying to preemptively make strategic decisions of the kinds you are thinking about long before those decisions are even applicable and when you don’t have all the information is pretty much a waste of your time and mental energy.
Good luck!

@user37208 I personally know several mathematicians who got tenure track positions at very good US institutions straight out of their PhD, and none of them are among the top half dozen mathematicians of their generation. It doesn’t happen frequently, but it’s not inconceivable either.

@user37208 5-10 years ago.

First of all, ranking isn't everything. When metrics are all we see, humans tend to obsess over them. But you may find other factors are far more important in deciding how happy you are in your career. So one valuable part of the application process will be seeing the differences and determining what you prefer. But to answer your question:
A postdoc is a training position; you take it to learn from your postdoctoral advisor(s). Do you feel you need to learn more or do you feel capable of leading your own research projects now? One year is too short to accomplish much in a postdoc. Unless you hit the ground running with your own projects that you know will pay off. And then why are you doing this in someone else's lab again?
A faculty position is an independent research position. If you feel you are ready for it and would be productive, you will have much more support and opportunity to get results and apply for funding in this role versus as a postdoc. Teaching is not a big danger to your time unless you unwisely let it be.
In summary if your only goal is to pad your CV with accomplishments in the next year, you will be far more able to do that in a faculty position. Rather than comparing whether to apply for that highly-ranked job now with no job, versus later with a better CV from a postdoc, you should instead be comparing apply later with a better CV from a faculty position at a lower-ranked school, versus with a better CV from a postdoc.
One important note: once you start applying to TT jobs, you need to produce. For example, suppose you apply to open TT positions this year, don't get the interview. Then you subsequently kind of have a weak year, but do get a bit more done (e.g. take that big name postdoc, but produce no big pubs yet). You are still probably in no position to reapply to the same places. So there is the danger of "starting too soon". For searches that remain unfilled over multiple years (which can be pretty common as the stars get a lot of offers and leave many in the lurch), you will need to make a case that they should reconsider you the next time you apply. That requires results.


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What are the pros and cons of working in a small or a large department?
If somebody has two tenure-track offers from two institutions of similar standing, with similar pay and tenure requirements, but one has a large department and one a small one, what are the pros and cons of each?
Examples are provided below to give a reference point, for answers need not address only these.
School A:

A small, non-departmentalized business school, with a total faculty count ~ 30. There is no department chair.
Only two faculty are in the applicant's field. Both of them are full professors, one of them are relatively research-inactive, the other one is very well-known and respected

School B:

Very large (~25 TT faculty with Assistant, Associate, Full Professors) and well-respected department in the field
More exposure to the field, as they invite more seminar speakers

What are the pros and cons of the large and small departments?

While I can see how this might be considered "depends on circumstances", and hence understand the close votes, i think there is a worthwhile general question in here about the pros and cons of large vs small departments. Voting to leave open, but if it gets closed then I recommend editing the question to make it as general as you can so that it would apply to others.

@Flyto I agree. I will try to edit the question to make it more generalizable.

Small universities are at greater risk of closing in the next few years due to lower enrollment and a shrinking population of young people  in the near future. This risk is countered by a larger endowment. Examine both these environmental circumstances of any small department.

@DavidSmith it's not really that much of a risk -- the school and the university are both doing very well. Both A and B are R1 and Top 50.

@JohnSmith I have submitted a major edit in an attempt to generalise the question and make it applicable to others. Please have a look and make sure it still reflects your intent. I do not know if this will get it re-opened, but high-rep members will be asked to vote on it.
It would be useful if you could indicate what country it refers to, because this like this might vary quite a lot.

From your post history, I'm assuming this is in the US, and have tagged accordingly. Feel free to update if you're asking about a different part of the world.

In my field (math, US) a department of 20 faculty would not be regarded as "very large", it would be either "small" or "medium."

The overall size of the institution may also be an important consideration.

I would favor the larger place for an early career person and the smaller one for someone in mid-late career. Since the two institutions are of similar rank/standing it would imply that the pressure for research at the two places is similar, unlike the situation where the smaller one were a liberal arts college with lesser research requirements.
The advantage of the larger place for the earlier career is the fact that there are greater, closer, collaboration opportunities and more opportunity to share ideas through seminars and such. You also say that the larger place is active in bringing in visitors. Even the coffee lounge at a large university is a very fruitful place to spend time.
This might be different if the smaller place is rich in travel money and provides travel funds for conferences and such. It might also be different if it is located in a place where contact with many local businesses/corporations, with possible consulting or collaboration possible.
But, to build a research focused career you need contact with a lot of people and it needs to be fairly intense in many cases to be fruitful. More local people with similar interests is a plus.
For an older, established, academic this is less important as they have likely already developed that circle of professional acquaintances and collaborators that the new kid on the block doesn't yet have.
This might be different if the person is interested in multi-disciplinary work within the field, crossing sub-field boundaries. That might be equally available at the smaller place.
Don't ignore place in your decision. Small towns and big city living is quite different, both in cost and style. But some big universities are located in small towns and vice-versa. You want a comfortable life style, not just a comfortable work environment.

Geography matters also scientifically. A small place near a number of other universities has some of the advantages of a big department.


text:
Editors resigning because they published a low quality paper
Very recently the chief editors of the journal
'EMS SURVEYS IN MATHEMATICAL SCIENCES'
resigned because they published a paper (whose "methodology does not contradict Cantor’s and non-standard analysis views and is based on the
Euclid’s Common Notion no. 5 “The whole is greater than the part” " - from the abstract) which turned out to be of questionable quality.
Apparently the editors were "unaware of what was happening" (statement of the editorial board). This seems very peculiar to me; of course it happens here and there that an accepted article contains a minor error, sometimes even a major error. But usually the editors do not resign simply because they accepted a paper with flaws. Does anyone know of similar cases? In this particular case the paper will apparently not be retracted, even though apparently none of the editors was aware that this will be published. Why would editors choose to publish it nevertheless?
Officially (according to the publisher, as quoted here) "it seems that there is not enough grounds for a formal retraction." And yet the statement suggests that the editorial board is thoroughly unhappy.
Remark: I am particularly interested in mathematics journals (hence the tag). However, I am also interested in other fields.

Where have you looked? So we don’t go over the same ground...

@SolarMike I have never seen something like this, and google only provided me with cases where the editors resigned for reasons that, broadly speaking, may be said to be 'political' (e.g. the elsevier case).

Maybe look for what happens when journals accept randomly-generated papers (e.g. SciGen). In that case, it's not that the paper is just wrong, or poor quality. It's that its acceptance implies that there was no effective oversight at any level. I could see that leading to a resignation.

@Nat https://arxiv.org/pdf/1606.00160.pdf  is a critique of the 'mathematics' of the paper, and, unfortunately, as well of the author.

@Roland This is what they claim. But one of the chief editors said "I formally confirm that the paper has been correctly processed by referees whose scientific reputation is definitely outstanding." The author says that the editors in chief asked him to retract the paper himself, which is also strange. But it is true that, officially, they didn't accept it, so I changed my formulation.

It's decently common. I Googled for "peer review failure editor resign" and some examples are: 1, 2, 3, 4.
You ask "Why would editors choose to publish [the paper] nevertheless?" There're many possible reasons as to why editors may choose to publish a flawed paper:

Some members of the editorial board approved of the paper and still do, while others didn't (and the ones that didn't are the ones resigning). Journals typically do not get every member of the editorial board to approve a paper before it's accepted.
They made a mistake, but now that the paper is already published, they don't consider the errors critical enough to retract it (or they don't consider it fair to the author).
They realized the paper is dubious but decided to err on the side of acceptance. People who subscribe to this idea think, "referees only give opinions; the real peer review happens after a paper is published".
The journal is currently lacking papers, so they're inclining towards accepting more papers to fill their issues.
Difficulty communicating with the author, e.g. if the author isn't very responsive, then editors might choose to accept "as is" rather than wait several months for an author response (especially if the author isn't likely to make major changes to the manuscript). I also remember reading about a flawed paper that was accepted because the author was from the Soviet Union, and getting the author to correct it through the Soviet censors takes too much time.

In the case of this particular paper, the editorial board claims no knowledge of what was happening. At this point, it's speculation to try to figure out what actually happened. However, I would guess (based on my experience in publishing) that after the journal received the paper the desk editor attempted to find a member of the board to handle it, but everyone contacted either declined to handle it or simply did not respond to emails. In that case the desk editor can hardly let the paper sit there collecting dust, so (s)he did the next best thing which was to DIY, with less than ideal consequences. I've seen this happen and even done it myself quite a bit. I've never actually ended up in the news for doing this, which shows that usually DIY-ing doesn't lead to problems. But if it does, that's when we look at one another and sigh.

"and the ones that didn't are the ones resigning" ... but why would editors who had nothing to do with that paper resign while the ones who approved it stay? Are they protesting against their fellow editors?

@muru the latter, aye. Maybe past tense is incorrect - some members of the editorial board might still approve of the paper. I'll edit that.

What does DIY mean?

Do It Yourself - ususally for home repairs and such but the use is spreading and relevant here.

My interpretation is that the real issue is not the paper itself, but the way it was handled. There may be nothing sufficiently wrong with the paper to justify withdrawing it, even though it isn't high enough standard for acceptance in the first place. But if I found I was editing a journal where a paper had been accepted without anyone on the editorial board knowing, I would be very unhappy with how the process was (not) functioning. If this has happened once, could it happen again? Has it happened before? Was it really a mistake, or is there some corruption going on?

"As you might have observed, I'm not in the news"... this is a bit difficult to observe when your display name is @user3727079 :-)

@JessicaB have you considered editing more journals? The desk editor will probably be more than happy to give you the paper! It's probable that this has happened before and will happen again, but it's probably not corruption: 1) it's not an open access paper so there's little to gain; 2) the editorial board didn't say they haven't seen all / most of the papers, only this one, implying they are handling other papers; 3) the paper passes "obviously wrong" tests - the author sounds like he knows what he's talking about, and has the publication record to back it up.

@user3727079 I didn't say I necessarily agreed with the conclusion. But if that situation did occur I would be concerned, and want to know what the explanation was. If the response was 'sorry, it was a mistake, then ok. But a response like 'it's none of your business, keep your nose out' would be problematic. I'm saying that the issue may not be the paper itself, but the way the whole thing panned out.


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doing masters while working?
I work full time as a bit of a mix: sql server DBA, BI developer, DW admin, ETL developer…
I hold a BS in Systems Engineering, and wanted to continue studying…
What are my options to balance a possible Masters in business intelligence and at the same time working full time?
I live and work in SouthWest London. Work is a bit flexible (I would be able to modify my working hours a little bit).
I have no knowledge in current options, but; is there any university in London where I would be able to do a masters in BI, attending to classes maybe only Friday and Saturdays?!

I am currently doing the exact same thing you are wanting to do: working full time as a data engineer and pursuing a Master's degree part-time in data analytics. It took me a very long time to find a program that was focused on BI or anything near that since it is still a relatively new field (new as in gaining traction in academia). Also, Business Intelligence is very wide, it encompasses a lot of different things. University College London (UCL) offers a part-time master's degree in Data Science which encompasses some BI stuff and more. If that's not exactly what you meant by "BI", then I would recommend searching for schools in London (or worldwide, in which case you'd be looking for online programs - which is also very possible) that offer a BI program, and then seeing if it can be part-time or not.


text:
How to cite a website/manual where the authors are (mostly) unknown?
For my thesis, I need to cite some pieces of software documentation. The website in question is the documentation of Kubernetes.
I'm fine with using @misc entries in my bibtex file, but obviously I need to fill in the author field.
Usually, when I can't find the author of a specific website, I look up the imprint at the bottom of the page. As the Kubernetes documentation doesn't provide any imprint, there is only © 2017 The Kubernetes Authors or Copyright © 2017 The Linux Foundation® at the bottom of the page.
To me, it looks a little bit odd to have "The Kubernetes Authors" in my bibliography. What should I do?

You can cite using @misc without Author field. To to that, you need to include a key field. Here is an example:
@misc{KubernetesManual2017,
    title = {{Kubernetes Manual}},
    howpublished = "\url{please put the url}",
    year = {2017},
    note = "[Online; accessed 04-Dec-2017]",
    key = "KubernetesManual2017",
}

With this solution, the bibtex entry appears without an author in the bibliography. Is this really the academic way? Obviously, someone must have written the documentation.

If a document has authors, obviously we should mention them. However, if a document doesn't mention any author then possibly it was meant to be that way.