Introductory Programming With Python
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Introductory Programming With Python

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Describes a first year introductory programming course using Python and some of the student outcomes of the course.

Describes a first year introductory programming course using Python and some of the student outcomes of the course.

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    Introductory Programming With Python Introductory Programming With Python Presentation Transcript

    • Introductory Programming with Python Brendan McCane
    • Hitchhiker’s Guide to Programming
      • Programming is hard. You just won’t believe how vastly, hugely, mind-bogglingly hard it is. I mean, you may think making a decent sandwich is hard, but that’s just peanuts to programming.
    • Donald Knuth says:
      • In fact, my main conclusion after spending ten years of my life working on the TEX project is that software is hard.
    • CS1
      • Average failure rate for CS1 courses is 33% (min 0%, max 60%).
      • Most universities teach Java, C# or C++.
      • Industrially relevant and OO is more “natural” right?
    • Design Principles
      • As simple as possible
      • Few magical incantations
      • Immediate feedback (no compile/run cycle)
      • A practical and modern language
    • Target Audience
      • Students majoring in computer science
        • Python in semester 1 (optional)
        • Java in semester 2 (compulsory)
      • Students not majoring in computer science
        • Mostly science or technical majors
    • Curriculum
      • We modified an open source textbook
      • How to Think Like a Computer Scientist
      • You can download our version from:
        • http://www.cs.otago.ac.nz/staffpriv/mccane/teaching.html
    • Curriculum
      • Introduction
      • Variables, expressions
      • Python builtins
      • Functions (2)
      • Conditionals
      • More functions
      • Test Driven Development
      • Files and modules
      • Iteration (2)
      • GUI programming
      • Case study (2)
      • Strings (2)
      • Lists (2)
      • Tuples and sets
      • Dictionaries
      • System Programming
      • OOP
      • Case study
    • Course Structure
      • Tight integration between lectures and laboratories
      • 24 lectures, 21 laboratories
      • Terms requirement – students had to submit 18 labs (not marked)
      • Labs had basic + advanced exercises
      • Lectures were programming demonstrations
    • Assessment
      • Mid-semester test (20%) – multi-choice
      • Final Exam (60%) – multi-choice
      • Mastery tests (2 x 10%)
        • Programming problems under test conditions
        • Tests published beforehand
        • 4 sections per test, each worth 2.5%
        • Each section pass/fail
        • Graded based on doctests + code inspection
    • Mastery Test Example (lab 9)
      • def score(numbers):
      • """
      • give the average of the numbers excluding the biggest
      • and smallest one
      • >>> score([2, 7, 9, 10, 13, 1, 5, 12])
      • 7.5
      • >>> score([3, 7, 2.5, -4])
      • 2.75
      • """
    • Final Exam Example
      • def matrix_to_sparse(in_matrix):
      • sparse = {}
      • for row_index,row in enumerate(in_matrix):
      • for col_index,val in enumerate(row):
      • if val != 0:
      • sparse[(row_index,col_index)] = val
      • return sparse
      • matrix = [[0,0,1], [0,2,0], [3,0,0]]
      • sparse = matrix_to_sparse(matrix)
      • print sparse[(2,0)], sparse[(1,1)], sparse[(0,2)]
    • Outcomes
    • Outcomes
      • 172 students attended first lab
      • 28% A
      • 23% B
      • 22% C
      • 27% failed (19% failed terms)
    •  
    • Notable Comments
      • Easy topics: first half
      • Hard topics: second half
      • Best aspect: labs
      • I would like to change: “a move away from extensive use of programming”
      • “ F*%! Matrices”
    •  
    •  
    • Statistical Results
      • Java with Python better than Java without (p=0.001)
      • Java 2009 better than Java 2008 (p=0.0007)
      • Java without Python (2009) not better than Java 2008 (p=0.18)
    • Conclusions
      • Python is a joy to teach
      • Learning Python is a good pre-cursor for learning Java
      • Open source textbooks are fantastic
      • Mastery tests are masterful
      • Force students to attend labs