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AECT-Atlanta-2001: Instructional Strategies in CS
111/08/01
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The Use of Innovative Strategies in
Computer Science Curricula ww
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Association for Educational Communications and TechnologyAtlanta, GA, November 8-10, 2001
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11/08/01 AECT-Atlanta-2001: Instructional Strategies in CS 2
Presenters
Ray J. AmiraultFlorida State University
Learning Development Institute
Yusra Laila VisserLearning Development Institute
Florida State University
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Why Try Innovative Strategies to Teach Computer Science?
Increasing complexity of subject matter Increased learning demands on
students Rapid change of technology Giant metaphor change: procedural to
object-oriented technology
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The Role of Instructional Design in Computer Science
Building base competency in novice (i.e., beginner) learners
Developing learner strategies for coping with change
Improving problem solving skills Responding to motivational needs of
learners and improving retention
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Present Day Situation: Computer Science Body of Knowledge*
1. Discrete Structures (DS)2. Programming Fundamentals (PF)3. Algorithms and Complexity (AL)4. Programming Languages (PL)5. Architecture and Organization (AR)6. Operating Systems (OS)7. Net-Centric Computing (NC)8. Human-Computer Interaction (HC)9. Graphics and Visual Computing (GV)10.Intelligent Systems (IS)11.Information Management (IM)12.Software Engineering (SE)13.Social and Professional Issues (SP)14.Computational Science and Numerical Methods
(CN)* Association of Computer Machinery, February, 2001
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The Recommended Curriculum:ACM/IEEE-CS Joint Task Force on Computing Curricula 2001
Set of recommendations on Computer Science curricula
Revised every few years based on task force recommendations
Describes curricular models, professional practices, characteristics of CS graduates, and other pertinent information
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Course Offered Required Elective
Analysis of Algorithms (AA) 55 (87%) 36 (57%) 20 (32%)
Architecture (AR) 59 (94%) 44 (70%) 17 (27%)
Artificial Intelligence (AI) 60 (95%) 2 (3%) 58 (92%)
Compiler Construction (CC) 58 (92%) 14 (22%) 44 (70%)
Database Management Systems (DB) 62 (98%) 14 (22%) 49 (78%)
Ethical, Social Issues (ET) 40 (63%) 35 (56%) 6 (10%)
Graphics (GR) 63 (100%) - 63 (100%)
Human-Computer Interaction (HC) 25 (40%) 2 (3%) 23 (37%)
Networks (NW) 63 (100%) 11 (17%) 53 (84%)
Operating Systems (OS) 63 (100%) 57 (91%) 12 (19%)
Parallel Computing (PC) 34 (54%) 1 (2%) 33 (52%)
Programming Languages (PL) 59 (94%) 49 (78%) 14 (22%)
Robotics (RB) 19 (30%) - 19 (30%)
Software Engineering (SE) 62 (98%) 41 (65%) 22 (35%)
Simulation (SI) 25 (37%) - 25 (40%)
Theory of Computation (TH) 52 (83%) 36 (57%) 18 (29%)
VLSI Design (VL) 17 (27%) 3 (5%) 14 (22%)
Actual Curricula (1999-2000)*
* technical report CoC/CS TR# 2000-9-1, Department of Computer Science, College of Charleston and is available in HTML at <http://www.cs.cofc.edu/~mccauley/survey/report99/>
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Specific Case: How is Programming Typically Taught?
1. Presentation of syntax and grammar rules
2. Presentations of variables, functions, and data structures
3. Simple programming assignment
4. Presentation of more advanced concepts
5. More difficult programming assignments
6. Tests on programming concepts
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Questions
Is this method the best way to teach a programming language?
How do children acquire natural language?
Is the knowledge building-block approach the way people actually learn an applied skill?
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Purpose of Inquiry
To develop a comprehensive understanding of the effectiveness of innovative instructional strategies applied in the computer science discipline.
Research results with positive effects on:Cognitive reasoningMetacognitive strategiesPost-test performanceAttitudes (ARCS)
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Research Approach
The Challenge: “Students in CS must extend beyond
programming ability. The responsibilities and challenge of this lie with the universities. Students must understand why the programming of a system is not the real problem.” (Robillard, 1996)
Initial Responses to the Challenge: Review of existing research literature on
instructional practice in Computer Science (1996 – present)
Review of CS curricula features and orientations
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Data Sources
Computer Programming
Software Engineering
Articles from Refereed Research Journals
26 27
Articles from Non-refereed Research Journals
15 3
Professional Conference Reports
12 5
Edited Book Chapters
11 13
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Research Focus
Selective research orientation on innovative strategies:1. Reviewed pool of educational research in
computer science2. Reviewed policy and research
recommendations for change in teaching practices
3. Identified documented instances of research that followed recommended changes
4. Selected innovative strategies with positive impact on variables of interest
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Teacher-Centered Instruction
“Inverted” course structuring: Focus on integrated understanding hardware/software, programming and computer language, performance setting
“Attached” instruction: Relate all instructional content directly to final product development
Incremental pragmatic approach (scaffolding) : Begin with notions of classes, objects, inheritance – use of hybrid programs)
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Situated Learning Environments
Problem-based learning Student-generated problem situations Instructor generated problem situations
Project-based learning Authentic performance contexts Simulated contexts
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Performance-Based Learning Environments
Work-based learning “Students will learn to use the best tools
available to them to accomplish the projects, and if the projects add real value to the world, such as solving problems in the local community or gathering data that contribute to the real progress of science, so much the better.”
(Spohrer, CTO, IBM Venture Capital Relations Group)
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Limitations of current response to the challenge
“The sequential mindset” Assessment/feedback Measuring integrated knowledge state Determining efficiency/effectiveness in
thinking and problem solving processes
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Our Propositions
Understanding expert performance Understanding the role of the teacher in
complex and flexible learning settings Understanding the higher order thinking
processes and attitudes Methods for learning to acquire
structural knowledge Strategies for learning to
learn/generative knowledge building
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Expert Performance in Programming
Why do some people develop expert levels of performance in programming?
What differentiates the novice programmer from the expert-level programmer?
What criteria can be used to determine who is performing at expert programming levels?
How was this expert performance attained?
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What We Know:Classic Studies in Expertise Research
Fitts and Posner (1967) - model with three phases and for acquiring acceptable (not expert) performance
Simon and Chase (1973) - theory of expertise acquisition where time spent leads to acquisition of patterns, chunks, and increasingly-complex knowledge structures
Ericsson and Smith (1991) - expert performance must be studied with individuals who can reliably and repeatedly demonstrate superior performance
Ericsson, Krampe, & Tesche-Romer (1993) - expert levels of performance are acquired gradually over time through use of deliberate practice and are mediated by mental representations developed during the deliberate practice period
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Expert Performance Continuum
Time
PerformanceLevel Expert Levels
Deliberate PracticePeriod
ArrestedDevelopment
Everyday Skills
InitialInstruction
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How Can We Research the Question of Language Acquisition?
Identify superior programmers based on real-world skills, not abstract measures
Investigate how these performers acquired superior abilities
Compare these methods to methods employed by “average” programmers
Determine the implications for ISD Implement findings in an instructional
intervention
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