CS2013-final-report

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CSCI 0190: Accelerated Introduction to Computer Science, Brown University Providence, RI, USA Shriram Krishnamurthi sk@cs.brown.edu http://www.cs.brown.edu/courses/csci0190/ Knowledge Areas that contain topics and learning outcomes covered in the course Knowledge Area Programming Languages (PL) 13 Software Development Fundamentals (SDF) 10 Software Engineering (SE) 6 Algorithms and Complexity (AL) 5 Parallel and Distributed Computing (PD) 1 Total Hours of Coverage Where does the course fit in your curriculum Brown has three introductory computing sequences as routes into the curriculum. The other two are spread over a whole year, and cover roughly a semester of content in programming and a semester of algorithms and data structures. This course, which is one of these, compresses most of this material into a single semester. Students elect into this course, either through high school achievement or performance in the early part of one of the other sequences. Approximately 30 students it every year, compared to 300-400 in the other two sequences. What is covered in the course The course is a compressed introduction into programming along with basic algorithms and data structures. It interleaves these two. The data structures cover lists, trees, queues, heaps, DAGs, and graphs; the algorithms go up through classic ones such as graph shortest paths and minimum spanning trees. The programming is done entirely with pure functions. It begins with graphical animations (such as simple video games), then higher-order functional programming, and encodings of laziness. What is the format of the course Classroom time is a combination of lecture and discussion. We learn by exploration of mistakes. How are students assessed There are about ten programming assignments. Students spend over 10 and up to 20 hours per week on the course. Course textbooks and materials There is no textbook. Students are given notes and code from class. All programming is done in variations of the Racket programming language using the DrRacket programming environment. - 447 -
Why do you teach the course this way The material of the course is somewhat constrained by the department’s curricular needs. However, the arrangement represents my desires. In interleaving programming and algorithmic content to demonstrate connections between them, I am especially interested in ways in which programming techniques enhance algorithmic ones: for instance, the use of memoization to alter a computation’s big-O performance (and the trade-offs in program structure relative to dynamic programming). The course is revised every year, based on the previous year’s outcome. Students consider the course to be extremely challenging, and of course recommend it only for advanced students. Body of Knowledge coverage KA Knowledge Unit Topics Covered Hours AL Advanced Data Structures, Algorithms, and Analysis Balanced trees, graphs, string-based data structures, amortized analysis 5 PD Parallelism Fundamentals Parallel data structures, map-reduce 1 PL Object-Oriented Programming All 3 PL Functional Programming All 6 PL Event-Driven and Reactive Programming All 2 PL Basic Type Systems All 2 SDF All All 10 SE Software Design Design recipe 3 SE Software Verification Validation Test suites, testing oracles, test-first development 3 - 448 -
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Computer Science Curricula 2013 Cur
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Computer Science Curricula 2013 Cop
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CS2013 Steering Committee ACM Deleg
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Chapter 5: Introductory Courses ...
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CC152: Computer Architecture and En
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Programming Languages and Technique
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Chapter 1: Introduction ACM and IEE
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KA subcommittee Chairs (as members
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Knowledge Areas The CS2013 Body of
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of the implications of social respo
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Manaris (College of Charleston), Sa
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Chapter 2: Principles Early in its
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novel, interesting, and effective w
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Appreciation of the interplay betwe
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to be able to communicate with, and
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● The learning outcomes and hour
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● Many strong computer-science cu
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include appropriate elective materi
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We use three levels of mastery, def
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Parallel and Distributed Computing
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select topics from Tier-2 (to inclu
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common“CS0” courses: precursor
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Tradeoffs: A programming-focused in
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Parallel Processing Traditionally,
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Chapter 6: Institutional Challenges
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Computer Science Across Campus An a
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programs do not have an explicit li
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accessible, building interdisciplin
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provide for assessing the effective
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AL. Algorithms and Complexity (19 C
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[Core-Tier2] 7. Describe various he
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Learning Outcomes: 1. Define the cl
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Architecture and Organization (AR)
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7. Evaluate the functional and timi
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AR/Interfacing and Communication [1
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Computational Science (CN) Computat
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CN/Introduction to Modeling and Sim
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CN/Processing [Elective] The proces
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CN/Data, Information, and Knowledge
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Discrete Structures (DS) Discrete s
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DS/Basic Logic [9 Core-Tier1 hours]
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DS/Graphs and Trees [3 Core-Tier1 h
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Graphics and Visualization (GV) Com
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GV/Fundamental Concepts [2 Core-Tie
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GV/Geometric Modeling [Elective] To
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GV/Visualization [Elective] Visuali
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HCI/Foundations [4 Core-Tier1 hours
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HCI/User-Centered Design and Testin
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Learning Outcomes: 1. Explain basic
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o Game engines o Mobile augmented r
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IAS. Information Assurance and Secu
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OS/Concurrency 1.5 OS/Scheduling an
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SP/Social Context 0.5 SP/Analytical
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11. Discuss the importance of usabi
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Learning outcomes: [Core-Tier2] 1.
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Learning outcomes: 1. Describe the
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Information Management (IM) Informa
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• Design of core DBMS functions (
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3. Demonstrate use of the relationa
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IM/Physical Database Design [Electi
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• Presentation, rendering, synchr
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IS/Fundamental Issues [1 Core-Tier2
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4. Describe over-fitting in the con
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IS/Agents [Elective] Cross-referenc
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Learning Outcomes: 1. Explain the d
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Networking and Communication (NC) T
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NC/Networked Applications [1.5 Core
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NC/Social Networking [Elective] Top
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OS/Overview of Operating Systems [2
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OS/Memory Management [3 Core-Tier2
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OS/File Systems [Elective] Topics:
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Platform-Based Development (PBD) Pl
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Learning Outcomes: 1. Design and im
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Because the terminology of parallel
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PD/Parallelism Fundamentals [2 Core
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• Topologies o Interconnects o Cl
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o Software as a service o Security
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PL. Programming Languages (8 Core-T
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PL/Functional Programming [3 Core-T
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o o o Enforce invariants during cod
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PL/Compiler Semantic Analysis [Elec
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• Metaprogramming: Macros, Genera
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PL/Language Pragmatics [Elective] T
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emphasize formal analysis (e.g., Bi
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Learning Outcomes: 1. Analyze and e
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Software Engineering (SE) In every
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such system are assigned to the cor
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Learning Outcomes: [Core-Tier1] 1.
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SE/Tools and Environments [2 Core-T
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SE/Software Design [3 Core-Tier1 ho
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[Elective] • Potential security p
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o o o o Code segments Libraries and
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Systems Fundamentals (SF) The under
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2. Describe how hardware, VM, OS, a
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2. Describe the scheduling algorith
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Social Issues and Professional Prac
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understanding of our commitment to
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Learning Outcomes: 1. Evaluate stak
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Learning Outcomes: [Core-Tier1] 1.
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[Elective] 8. Discuss ways to influ
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• Differences in access to comput
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Appendix B: Migrating to CS2013 A g
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and are treated as a topic geared t
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GV The storage of analog signals in
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SDF This new knowledge area pulls t
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Table B-3: Core Learning Topics and
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Table B-4: New and Expanded Core Le
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IAS Core-Tier1: • Analyze the tra
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IS NC Core-Tier2: • Translate a n
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• Describe at least one design te
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• Explain why the creation of cor
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• Use refactoring in the process
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• Apply simple algorithms for exp
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Appendix C: Course Exemplars While
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IS U. San Francisco Artificial Inte
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ACM/IEEE-CS CS2013 Course-Exemplar
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CSCI 140: Algorithms, Pomona Colleg
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AL PD advanced data structures algo
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What is the format of the course Th
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CS 256 Algorithm Design and Analysi
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and other applications including Ba
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• Data races and higher-level rac
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CS/ECE 552: Introduction to Compute
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Body of Knowledge coverage KA Knowl
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AR Assembly Level Machine Organizat
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Altruism/Collaboration/Competition,
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everyday lives, and therefore at le
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COSC/MATH 201: Modeling and Simulat
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ehaviors, simplifying assumptions;
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MAT 267: Discrete Mathematics, Unio
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Identify a problem and analyze it i
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CS109 covers: • Counting • Comb
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CS 250 - Discrete Structures I, Por
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CS 251 - Discrete Structures II, Po
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DS Graphs and Trees Tree, tree trav
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The course gives an overview to a v
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CS371: Computer Graphics, Williams
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Texture mapping, including minifica
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Other materials are: R and RStudio
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3. Assignment (20% + 10%) Due in we
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CO328: Human Computer Interaction,
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Human Computer Interaction, Univers
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Human-Computer Interaction, Stanfor
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Human Information Processing (HIP),
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Software and Interface Design, Univ
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Computer Systems Security (CS-475),
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Ullman and Widom, 2 nd edition, Pre
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Technology, Ethics, and Global Soci
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SP Professional Communication These
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Why do you teach the course this wa
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search, probabilistic reasoning, lo
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Students are expected to spend abou
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CS188: Artificial Intelligence, Uni
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Introduction to Artificial Intellig
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Computer Networks, Williams College
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NC Introduction All 1.5 NC Networke
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CSCI 432 Operating Systems, William
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OS Security and Protection Access c
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Assignment Five: Memory management
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Programming Language: C Assignment
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Parallel Programming Principle and
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PD Parallel Algorithms, Analysis, a
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• Grama, A., Gupta, A., Kumar V.
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Body of Knowledge coverage For each
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1: Map-reduce for CS1/WMR 2: Multic
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What is the format of the course Co
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Csc 453: Translators and Systems So
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CSCI 434T: Compiler Design, William
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AR Machine-level representation of
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Languages and Compilers, Utrecht Un
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COMP 412: Topics in Compiler Constr
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separate compilation is the key fea
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programming languages. A major them
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CSCI 1730: Introduction to Programm
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CSC 2/454: Programming Language Des
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PL Runtime Systems Data layout; sto
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How are students assessed Over 10 w
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Programming Languages and Technique
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esults from programming language th
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PL Event-driven and Reactive Progra
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Overall, students learn the followi
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PL Type systems Compositional type
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What is the format of the course Th
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Introduction to Computer Science, H
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PD PD/Parallelism Fundamentals Para
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• Analytical Reasoning: Assertion
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Page 408 and 409: SE Software Validation and Verifica
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Page 428 and 429: Analyze basic logical fallacies in
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Page 442 and 443: The Digital Age, Grinnell College G
Page 444 and 445: AR Digital logic and digital system
Page 446 and 447: COS 126: General Computer Science,
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Page 452 and 453: An Overview of the Two-Course Intro
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Page 458 and 459: One way to document this spiral app
Page 460 and 461: 3. Data types and structures 1. pri
Page 464 and 465: ◦ Comparison of results for small
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Page 468 and 469: Bluegrass Community and Technical C
Page 470 and 471: Knowledge Units in a Typical Major
Page 472 and 473: Notation: < 25% of KU covered # 25-
Page 474 and 475: Appendix: Information on Individual
Page 476 and 477: Curricular Analysis The Knowledge U
Page 482 and 483: management, the Internet, e-mail, t
Page 484 and 485: Mathematical Foundations (two desig
Page 486 and 487: Multi-paradigm, Introductory Sequen
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Page 494 and 495: CSC 312 - Implementation of Program
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Notation: < 25% of KU covered # 25-
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Additional Comments We instituted a
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CS 145: Introduction to Databases T
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Williams College Department of Comp
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Knowledge Units in a Typical Major
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Notation: < 25% of KU covered # 25-
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Possible Curricular Revisions Note
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CSCI 237 - Computer Organization Th
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surface scattering functions, binar
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A Cooperative Project of
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