This course is an introduction to the basic concepts of programming languages, with a strong emphasis on functional programming. The course uses the languages ML, Racket, and Ruby as vehicles for teaching the concepts, but the real intent is to teach enough about how any language “fits together” to make you more effective programming in any language -- and in learning new ones. This course is neither particularly theoretical nor just about programming specifics -- it will give you a framework for understanding how to use language constructs effectively and how to design correct and elegant programs. By using different languages, you will learn to think more deeply than in terms of the particular syntax of one language. The emphasis on functional programming is essential for learning how to write robust, reusable, composable, and elegant programs. Indeed, many of the most important ideas in modern languages have their roots in functional programming. Get ready to learn a fresh and beautiful way to look at software and how to have fun building it. The course assumes some prior experience with programming, as described in more detail in the first module. The course is divided into three Coursera courses: Part A, Part B, and Part C. As explained in more detail in the first module of Part A, the overall course is a substantial amount of challenging material, so the three-part format provides two intermediate milestones and opportunities for a pause before continuing. The three parts are designed to be completed in order and set up to motivate you to continue through to the end of Part C. The three parts are not quite equal in length: Part A is almost as substantial as Part B and Part C combined. Week 1 of Part A has a more detailed list of topics for all three parts of the course, but it is expected that most course participants will not (yet!) know what all these topics mean.
Grading Policy
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Из курса от партнера University of Washington
Programming Languages, Part A
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Introduction and Course-Wide Information (Start Here)
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Dan GrossmanProfessor
Computer Science & Engineering
[MUSIC]
In this video, I want to go over how grading will work in this course.
All of this information is detailed elsewhere.
So in that sense, you could consider this video optional.
But I think that it's helpful to highlight key points,
I've also learned that people are less likely to miss key details if there
is a video that goes over some of the information.
I also don't consider grades to be a major focus in these online courses and
I hope that's true for you as well, but I know these things matter to people.
And so, I want to have a clear policy and make sure everyone understands it.
The key issue in this video, of course is what the policies are, but
I also tried to give a bit of rationale to justify why that can help things make
a little more sense.
And also, let me acknowledge that there are trade offs and
no perfect policy that's going to be the best fit for
whatever ones trying to get out of this course.
So for each homework, you will first, submit it to an auto-grader.
A computer program that will look at your assignment.
Run it on test cases, analyze what language features you're using and
assign a grade.
And we do this in a more strict fashion that in many of the MOOCs out there.
So the basic thing is too pass, you need too get 80%.
I actually think that is a reasonable bar.
I hope that you will continue trying until you get a hundred percent and
fix things up, but that is the way it works.
If we ever change that 80% threshold, I may not rerecord this video,
but I think its in about the right place.
And then the strict thing is that we're only
going to let you submit at most once per day.
Now, why are we doing that?
I'll explain more on the next slide, but
it really means you need to do the whole homework together.
So what we don't allow you to do in a sort of reasonable way is just do the first
problem and then see how you're doing, and then just the next little problem, and
see how you're doing.
And I know that some of you will prefer that, but there are good pedagogic reasons
to make it more like things on a traditionally university campus.
Well, the homework is do it in the end of the week and
you do your best job as you can and everything and then you submit it.
Now, the worst part is with this policy is when you make a tiny little mistake that
causes your code to not compile.
Or if you upload the wrong file, then you're going to have to wait til the next
day to submit what you meant to submit in the first place.
And that's a casualty of this policy and I apologize in advance for
it, but there's not a good easy way to fix that.
And I guess all I can really say is well, you'll only have to wait a day.
So this is a stricter policy than most MOOCs, but
it's actually still much more lenient than the new traditional university course.
You get to keep submitting every day, if you want to.
Although I really encourage you to do the assignment,
really try to be confident you have everything right and then submit it.
And then if you want to try again to do better, well then, of course,
that opportunity's available to you.
I should also point out that this MOOC also used to have a much stricter
policy where you could only submit twice and then that was it.
But in the more modern model that you can continue in the MOOC,
learning until you can master the material and finish.
Of course,
having a hard limit where you can never submit again doesn't make any sense.
And so, once per day is actually a pretty lenient policy
compared to where things used to be.
So, what I'm really going for here is unlike a lot of online courses where
you're just trying to get through a tutorial and move on to the next thing.
I don't want you to keep twiddling and changing little things, and
using the auto-grader as a substitute for
your own understanding of the material, and your own testing of the programs.
I understand that can convenient.
That it can be frustrating to put a lot of time in on homework and then when you go
to the auto-grader, have it tell you lots and lots of things are wrong.
But I truly believe,
the best way to learn this material is to focus on the assignment and
think to yourself about how the pieces fit together and is everything correct.
And we give you a lot of help in the assignment,
the types the functions should have, example test cases.
So, you can do that reasoning on your own and
then the auto-grader can be just a sanity check when you're done.
That's the idea.
And if we let you just use the auto-grader as much as you wanted, it'd be much,
much easier to get everything right.
Basically, you would just fix things one at a time until you happen to pass all
the test cases and I think that's not the way to learn the material in the course.
Now I want to say, there is no perfect policy.
This grading policy is not perfect.
There's trade-offs here and it's just sort of the best compromise.
I need to pick one policy for everyone and that's what we've come up with.
A few details and other parts of the course,
that was the main auto-grader policy.
So first of all, we have some challenge problems on the homework.
They are not worth very much credit.
They're just a few percent bonus, if you will.
And I would love to take someone who turn in the homework.
Got everything right and got the challenge problems right and
give them say, 104% out of a 100%.
Now at the time I'm recording this video, the Coursera platform doesn't let me give
more than 100% the way we're doing the auto-grading infrastructure.
So unfortunately, I can't give you more than 100%.
You'll get it in your text feedback, you should feel very good about getting it.
But in terms of your official course score and course record,
it'll max out at 100% and maybe we can fix that sometime in the future.
Let me also talk about peer assessment,
which I think is a really important part of this course.
After you're done with the auto-grader for
an assignment, then you need to turn in your assignment again for
others in the course to look over your code and give you feedback on your style.
And you're going to give feedback to three other
people in the course on the style of their submissions.
Now the way this is going to work is you can on the Coursera platform
participate in peer assessment at any point, but you should not until
you pass the auto-grader and you won't be able to do it correctly.
Because the file that has the detailed grading instructions for
peer assessment also has a sample solution in it.
And therefore,
won't be available to you until you get that 80% threshold on the auto-grader.
Because otherwise, you would have a sample solution sitting there right in a file
available to you, which would be far too tempting.
And really, demotivating for those of you working hard on the homework.
So the idea is after you past the grading threshold,
then the peer assessment rubric will be available to you in that separate file,
then you can participate in the peer assessment and
you'll have that file that you need in order to do the peer assessment.
Now the actual pair assessment, I think is really crucial for learning a lot
from the course and style, but it's not going to affect your grade very much.
We'll make it worth a small amount.
Basically, any score you get on the pair assessment is enough to continue in
the course and it will just have a modest 10% affect on your overall course grade.
So we're doing this, because just passing the auto-grader would give a bad
impression on what's important in the course.
The test cases are not everything that matters in writing clean elegant code that
demonstrates you understand the concepts in the course and
I've learned that people learn a ton by looking at other solutions, and
getting feedback from peers on their own work.
So, it is really important to be kind and helpful.
We are doing this not to create a mean grading system.
But in fact, to provide a constructive opportunity for everyone too help each
other and we tried to structure it in a way that will be meaningful too you in
learning the material with out being too time consuming.
The exams, when we get there.
There's one at the end of part A and
then a second one all the way at the end of part C.
Like on the programming assignments, you can take it at most once per day.
You need to get at least an 80% and I want these to be timed exams.
Now, the Coursera platform at the time of this video recording won't enforce
a time limit.
But part of the honor system is obeying it, so that you study for
the exam and then take the exam.
And if you don't do well enough, because you studied the wrong things,
then you'll have the opportunity to take it again.
But I don't want you to just open the exam and then go and look up each question, and
spend hours doing it that way.
Because I think you will learn far more by studying for
the exam than you will by just answering the questions on the exam.
Now, what's the exam going to be like?
Well, we'll get there in a few sections.
But we provide practice exams,
which will give you a good sense of what questions would be like and
it's an opportunity to really engage in the material in a different format than
just writing programs and participating in the auto-grader and the peer assessment.
So with that, we are through the grading policy.
I apologize for marching you through those details, but I do think the homeworks and
exams including the peer assessment for
the homework assignments are a great way to learn this material.
Certainly, you will learn far more by doing the homeworks than just by watching
the videos.
And I hope you will enjoy them,
as challenging exercises that will stretch your mind and cause you to learn a lot.
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