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Pretty much most senior level/grad algorithm classes are like this. They introduce a framework for solution and then give you about 2-3 example lecture problems

• marmetio 4y ago
  This is a problem all across engineering. Professors and textbook authors are so concerned about reserving problems to grade you on that they forget to teach the material in the first place. Imagine trying to learn a spoken language with very little input or speaking practice.
  • deleted 4y ago
    [deleted]
  • TheOtherHobbes 4y ago
    Many professors don't care about teaching. I've had conversations with physics/math/engineering academics which go "Those who are smart and motivated will get it, and I don't care about the rest."
    • blackbear_ 4y ago
      I believe that a lot of this cynicism comes after years of teaching and seeing that most students do not really care about the topic and do not want to put in more than the minimum effort required to pass. You don't realize how easy it is to tell apart those who are interested from those who are not until are on the other side.
      • njbooher 4y ago
        Plenty of new faculty are more interested in research than teaching too.
        • yodsanklai 4y ago
          > Plenty of new faculty are more interested in research than teaching too. Partly, because they're evaluated on research.
          • njbooher 4y ago
            But are being paid by tuition revenue, from people who expect them to teach. The US needs more national labs for people who want to do research but don't want to teach.
            • zem 4y ago
              and better pay and tenure options for people who don't want to do research but are good at and put significant amounts of work into teaching.
              • njbooher 4y ago
                I agree. I was envisioning that after we shipped the people who don't like teaching off to national labs that the universities would just focus on teaching and could pay those people better.
        • martopix 4y ago
          The system is unfortunately unfair to everyone, in the sense that it's an unfair expectation for people to love and be good simultaneously at teaching and research. I met many people that want to do research (because of passion or because that's what gets you a career) and are bothered by teaching; I've also met people, including myself, who prefer teaching to research, and it's unfair that neither of these groups can express their skill properly. I predict that in the future there will be more of a separation of careers, especially at the bachelor level, where having an instructor that knows the state of the art of research is not so necessary (although I'd argue that they should have a PhD). This is already happening a bit, say, in the UK, especially in fields like CS where courses are required also in other degrees curricula.
    • deleted 4y ago
      [deleted]
    • causi 4y ago
      Yep. My introductory physics course in college started with 29 students and ended with 4.
  • treeman79 4y ago
    Took an art class for fun. Been a programmer for 20 years. Final exam the instructor put a bunch of glass vases up for us draw. We had never covered glass or anything transparent. Teacher was quite surprised when this was pointed out after everyone struggled for 3 hours. She was generous on grade.
  • Mixtape 4y ago
    Something like this happened to me this past Fall. I took an undergrad fundamentals of networking course that was honestly fairly surface-level and simple, but the professor put a significant focus on developing the proofs for the course concepts through homework rather than in-class lectures. In theory that's fine, but the issue was that in a time-sensitive setting (i.e. assignments or exams) it became extremely stressful to just not understand where to begin with certain problems. Combine that with the fact that, in my experience, proofs often lead to understanding of a concept rather than the other way around and you had a lot of people going into office hours basically asking for a solution. I would be very interested to see a study on the relationship between time costs sunk into learning a concept through proofs and students' understanding of the concept as compared to when a proof is simply provided. I'd hypothesize that letting students discover the reasoning behind things on their own would be more likely to develop a deeper understanding, but I question whether there's enough time in a single semester to drive a curriculum that way.
• themitigating 4y ago
  "But there are smart people who get all this stuff "easily" (previous practice, exposure)." I believe you meant to say "(studying)"
  • IIsi50MHz 4y ago
    Prior practice/exposure does not have to be "study".
  • marmetio 4y ago
    I doubt that. If the materials are limited, then by definition there's less to study. You can choose other things to study, but it's a gamble whether it will be useful.
    • LarryMullins 4y ago
      There are different sorts of studying. To some people, studying means downloading the lecture notes, reviewing their own class notes, getting the slide deck from the professor.. To other people, studing means they actually crack open the assigned texts (which many students didn't bother buying in the first place.)
      • hahajk 4y ago
        And then to others it means spending going out and building things using concepts they're learning, not because they want to pass the course but because they're obsessed. I just started as a teacher, I don't know if that kind of obsession can be cultivated in students through a specific environment or if it's something they bring with them into the class. Or if even trying to bottle that lightning alienates less willing students.
      • eli_gottlieb 4y ago
        You're in grad-level classes where there are assigned textbooks? Bless!
  • tnel77 4y ago
    Sometimes. There were certain topics that I didn’t have to do any studying for and others that took a lot of effort.
• xyzzyz 4y ago
  > But there are smart people who get all this stuff "easily" (previous practice, exposure). Or, simply by virtue of being more intelligent. Life is not fair, and some people do have it easier than others, without having to pay for it with extra study time. If you are pursuing a research career, and you observe other students putting similar amounts of effort, but obtaining much better results, understanding things more quickly and getting better ideas, you should probably just drop out. Not everyone can be world class athlete, and not everyone can be a world class researcher, and refusing to accept it will often lead to wasted years and wasted potential: many people who are not smart enough to do top research often can achieve great success in eg. corporate world or startups, or achieve higher family goals and do it earlier (most people will be made more happy by their children than by publishing a mediocre paper that nobody will read). Being hell-bent on following a particular dream can bring a lot of misery.
  • chitowneats 4y ago
    Same thing with passing a football or nailing a technical ice skating routine. You can either do it, or you can't. Practice helps but is not a panacea, unfortunately. Find another occupation, friend. You are owed nothing.
    • progman32 4y ago
      Or a better teacher. The limiting factor isn't necessarily the student.
      • chitowneats 4y ago
        So we should ensure equal opportunity to ice-skating lessons, in perpetuity, for all humans? Just in case?
        • qotgalaxy 4y ago
          [dead]
        • eyelidlessness 4y ago
• opportune 4y ago
  I did well in Algorithms courses (I took 4-5 and TA’d the intro class). I also studied math on top of CS. Imo a lot of the problem that CS students face in algorithms is due to not having a mathematical background. Even though you are introduced to concepts like invariants, induction, and propositional logic in a discrete math class, IMO it takes more exposure and practice to get to the point where these concepts get useful in algorithms. In mathematics, and algorithms, a proof is not typically just applying invariants and propositional logic - it also requires more open ended thinking and (usually) applying patterns seen in class/coursework to solve problems. And even if not asked to formally prove correctness or complexity, knowledge of these concepts makes reasoning about algorithms a lot easier. Most CS students at the point of taking intro algorithms know how to do some basic coding, know data structures, and if not also pursuing mathematics, have knowledge of calculus (useless) and discrete math. Unless the discrete math course was very rigorous and had a lot of coursework, taking Algorithms at that point is like walking into the deep end with floaties on - you might survive, but you’re not really ready. In Algorithms, like in competitive programming and software interviews, you also need to have a list of all the different data structures, algorithms, and strategies you know well enough that you can ask yourself if X data structure or Y pattern is appropriate for the task. IME this is often not suggested for people studying Algorithms but it should be. Even if you don’t fully get a problem, you can probably get most of the way by identifying the general approach you should use.
  • qsort 4y ago
    This. There's a lot of babbling about how coding is the limiting factor, but it really isn't. What people struggle with is solving the problems in their head. Writing the actual code after you've done that is almost trivial. Another angle is how you need to think in abstract terms in the solution space. Most people whose knowledge of algorithms is weak think like: "okay, the problem says the word 'array' so I probably need a quicksort here". People who are successful think like "if this array were sorted, I could search an element in O(lg n) time instead of O(lg n). Can I solve the problem with this additional hypothesis?". It's almost like being a chess player: when you're weak you come up with moves and hope they work, when you get stronger you look at a position and come up with plans and reasons why certain moves will or will not work. The top-down mode of thinking is how you write a solution but not how you come up with one, the actual process is more "inside out", you find out stuff thinking about the problem and then you connect the dots. Part of it is just experience and wit, but much can be taught, and formal mathematics helps the most.
• guntherhermann 4y ago
  This describes some of my CS modules experience down to a tee. Overall I had a great time and I look back on the whole experience fondly, but those few modules where nothing clicked, nothing made sense and the examples given never fit the work I had to do... This was before the days of YouTube where you could look and study up hundreds of videos on the topic to finally find the one that clicks, which is a remarkable thing to have access to. I'm not clever, but I am resolute, and having an unyielding attitude to obstacles is what has gotten me this far in my career.
• krageon 4y ago
  This matches my experience of an IT education in university perfectly. 90% of my class just didn't get it at all and the remaining 10% had a lot of experience in the field already (there were a small amount of extremely smart folks as well of course - they needed no background). Kind of defeats the purpose of teaching, but it does serve to weed out the folks that can't pick things up very fast. I think that is one of the goals of high-level schooling.
  • winReInstall 4y ago
    But we have machines for that? Data access and repetition in itself is worthless. Refinement, and gaining new data from old recipes is the actual value. Why construct a machine, that basically selects for the wrong value, creating a reproduction academia, instead of a innovation academia. Why weed out what was your original value proposition? It seems very strange.
    • ianai 4y ago
      Because they don’t believe in the value proposition they’re selling. Instead of forming students into high quality output they’re taking in students and pushing out anything but already high quality students. The need to have seen something before a class to have a chance at passing it well is fundamentally wrong.
    • krageon 4y ago
      Absorbing and applying knowledge fast is a requirement for surviving in academia. Having classes that weed people out is not a bad thing. That's what I was saying - I don't see how this is pure reproduction, this is novel application of existing ideas. That's how innovation can start.
      • lll-o-lll 4y ago
        Nah, I don’t buy this at all. I’ve never been a fast knowledge absorber, but that just meant I required a bit more time on my own, struggling with the content. Still managed to get max grades at undergrad university (didn’t go further because, well money). Also never stopped me from excelling in all work positions so far in life. If anything, a bit of struggle helps you go deep, and deep is much more valuable than fast but shallow (in my opinion). Not professional advice, YMMV etc.
• greggman3 4y ago
  I forgot where I heard this but "It's impossible to remember what it was like not to know something". This generally means all kinds of things are obvious to someone that knows a topic. They're so obvious they're invisible, forgotten about, and therefore you don't even think to teach them.
  • deskamess 4y ago
    That's a great quote! When you know, you know; if you don't it can feel helpless.
• KMnO4 4y ago
  I found a lot of things needed time to sink in. I would struggle to wrap my head around the exam content and stress myself out. Then the class would finish and I wouldn’t see the content for another semester, where I’d either be helping another student study or it would come up in a related course. Suddenly, my brain would just “get” it and I’d know exactly how to start.
• tgv 4y ago
  What did you think? They were going to give your your diploma because you're such a nice person?
• raducu 4y ago
  > who get all this stuff "easily" (previous practice, exposure) I've graduated in Eastern Europe, my experience might be different, but I CONSTANTLY heard my peer bitching&moaning about the materials to be hard. I found them to be "easy", as in easy to score 7/10 to get to 9/10 you'd have to apply yourself or be smart and to get to 10/10 you'd really have to apply yourself. Granted, I did have "previous practice" -- meaning I was highly interested in programming and electronics. There were some infamous professors that made things "hard" for students -- those were the reaaaly boring, ultra-math-heavy-showoff-pricks I'm glad they had a car accident and died. I still remember I didn't even bother going to the course of such a prick professor after course 3, just repeated the course the next year with a much more down-to-earth professor, and my blood still boils when I think how much fun I had just studying opams mathematical circuits with the second professor's materials and how bad the first prick-professor made us feel with his showoff maths. I didn't even go to the second professors in-person courses, I could tell from his study book he was a down-to-earth guy and his interest was conveying cool stuff to his students and not how much of math guru he was. So when things were "hard" it was usually because the professor was an actual showoff prick and not because of an unfortunate missunderstanding on his part about our capabilities as students. Those pricks know very well what they're doing and how scared students are about them and how many they are failing, they just get off on that stuff.
• musicale 4y ago
  > about 2-3 example lecture problems that they walk you through My thinking on this may be evolving. I used to think that you would learn more figuring out things for yourself. There may be something to that, but now I am thinking that having a more relaxed learning curve with many worked examples (not just a few) will help more students succeed with the course material. Students who have seen the material before will be bored, but the examples and gradual learning curve are intended for those who are seeing and learning the material for the first time. In my own experience, I find that many problems require a particular, often subtle or non-obvious, trick or insight to solve. This can result in wasting a huge amount of time until you are fortunate enough to discover it, assuming you actually do. These sorts of problems, especially on exams, are beloved by instructors because they produce modal distributions that make it easy to pick winners and losers and/or sort students into grading bins. They're hated by students because they tend to reward prior knowledge or experience - or sheer luck - rather than effort in the current course. I wonder if the author is making a mistake trying to put research-like questions into a problem set, even one intended for early doctoral students. (Imagine putting students in competition with each other on research problems - that would seem to be isolating, cruel, and bad for a research group!) I would certainly hope that the exams are more reasonable.