IP-Assignment-3/Assignment 3 Question 6.txt at main · riyasach189/IP-Assignment-3 · GitHub

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#COMPLETED

Goal is to develop an application for grading in courses at IIIT-Delhi. The application should
provide for (i) creating a course for grading with its name, credits, list of assessments with
percentage  weight for each (e.g. [("quiz", 5), ("mid-sem", 15), …], a grading policy as a list
percent for different grades (we will assume that only grades are: A, B, C, D, and F, so this
can be [80, 65, 50, 40, 30] (i.e. A above 80, B between 80 and 65, …, F below 30). However, the
final cutoff for each grade will be within +/- 2 of the percent specified (i.e. for A it will be
between 78 and 82) - it will be the higher of two consecutive marks within this range which have
the highest difference. (So, if the marks are 81.9, 81.8, 81.7, 81, 80.9, 80.8…, then the cutoff
will be midpoint of 81.7 and 81, which has a largest gap  (ii) adding students' marks for all
assessments given in a file - these will be rollno followed by marks for each.  Once marks are
given, it should do the grading - apply the policy to determine the grade for each student.

It should then ask the professor what he wants to do: 1. Generate a summary - which should print
the course info (name, credits), assessments and their weight, cutoffs for different grades, and
grading summary (how many As, how many Bs, etc) 2. Print the grades of all the students in a
file as: rollno, total marks, grade (one line for each student). 3. Search for a student
record - given the roll no, show marks in different assessments, total marks, and final grade.

Develop this program and use it for grading the "IP" course. The assessments and the grading policy
is given below (in the main program steps)

The main program can be something like (rough sketch):

1.cname, credits = "IP", 4
2.assessments = [("labs", 30), ("midsem", 15), ("assignments", 30), ("endsem", 25)]
3.policy = [80, 65, 50, 40, 30]
4.create-IP course (cname, credits, assessments, policy)
5.Upload-marks data - call a function/method with input as marks.txt
6.doGrading - call a function/method
7.Loop asking for what operation (1, 2, 3); perform the operation till no input given

Compare the two approaches/code: (A): In your view what are the two pros and cons of the two
approaches. (B) Compare the performance of these two in terms of time taken. For this, use the
package time. For measuring time taken, run the grading operation N (say 1000) number of times,
and run searching for student N number of times (you can pick up a roll no randomly, or just give
a few roll nos in a list and go through them repeatedly) and then compute the time for the two,
summarizing which one is faster for (i) grading operation, and which one for (ii) search operation
and by how much (takes what fraction of time of the other)

The answer to this question should be be in a .txt file, containing:

Advantages of using OO
1. more natural way of approaching the code; easier to formulate the structure of the code; more readable code
2. attribute names can be repeated

Advantages of using Dictionaries
1. separate dicts for separate records, eg. marks, grades, etc.
2. no confusion between attributes and methods

Disadvantage of using OO
1. slower code in general
2. confusion between attributes and methods

Disadvantage of using dictionaries
1. difficult to keep track of keys and values of all dictionaries
2. dictionaries and functions don't have any connection; bad software design

Performance comparison for grading operation (give in each line: value of N; time taken by two
approaches in seconds, and which is faster and by how much).
1. N: 10000
2. Time by OO: 0.014603049755096435 seconds
3. Time by dictionary: 0.016503298282623292 seconds
4. OO is faster; fraction of time OO took is: 0.884856439301732

Performance comparison for search operation (give in each line: value of N; time taken by two
approaches in seconds, and which is faster and by how much).
1. N: 10000
2. Time by OO: 0.004397900104522705 seconds
3. Time by dictionary: 0.0035261440277099608 seconds
4. Dictionary is faster; fraction of time dictionary took is: 0.8017790181463537