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Information Technology Education Standards Jaime D.L. Caro, Ph.D. President, PSITE Project Leader, VCTI-IT Associate Professor of Computer Science, UP Diliman

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2 Based on reports from Association for Computing Machinery (ACM) Institute of Electrical and Electronics Engineers (IEEE) Computer Society International Federation for Information Processing (IFIP) Association for Information Systems (AIS) Association for Information Technology Professionals (AITP) We shall also look at policy guidelines from Philippines Commission on Higher Education (CHED)

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3 Standards? Standards for teaching IT Standards for professional development for IT educators Standards for assessment in IT education Standards for IT content Standards for IT education programs Standards for IT education systems

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4 IT Education Standards Recommendations versus Standards Minimum Standards UK benchmarking report recognized that establishing a minimum standard may discourage both faculty and students from pushing for excellence beyond that minimum. To avoid this danger, the UK report provides benchmarking standards to assess various levels of achievement. Standards for Achievement Benchmarking Standards Threshold Standard Modal Standard etc

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5 Threshold Standard representing the minimum level Demonstrate a requisite understanding of the main body of knowledge and theories of computer science/computing/information technology. Understand and apply essential concepts, principles, and practices in the context of well-defined scenarios, showing judgment in the selection and application of tools and techniques. Demonstrate the ability to work as an individual under guidance and as a team member. Discuss applications based upon the body of knowledge.

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6 Threshold Standard representing the minimum level Produce work involving problem identification, analysis, design, and development of a software system, along with appropriate documentation. The work must show some problem-solving and evaluation skills drawing on some supporting evidence and demonstrate a requisite understanding of and appreciation for quality. Identify appropriate practices within a professional, legal, and ethical framework. Appreciate the need for continuing professional development.

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7 Modal Standard representing the average level Demonstrate a sound understanding of the main areas of the body of knowledge and the theories of computer science, with an ability to exercise critical judgment across a range of issues. Critically analyze and apply a range of concepts, principles, and practices of the subject in the context of loosely specified problems, showing effective judgment in the selection and use of tools and techniques.

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8 Modal Standard representing the average level Produce work involving problem identification, analysis, design, and development of a software system, along with appropriate documentation. The work must show a range of problem solving and evaluation skills, draw upon supporting evidence, and demonstrate a good understanding of the need for quality.

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9 Modal Standard representing the average level Demonstrate the ability to work as an individual with minimum guidance and as either a leader or member of a team. Follow appropriate practices within a professional, legal, and ethical framework. Identify mechanisms for continuing professional development and life-long learning. Explain a wide range of applications based upon the body of knowledge.

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10 Excellence Standard representing the highest level Demonstrate creativity and innovativeness in application of the principles covered in the curriculum Contribute significantly to the analysis, design, and development of systems which are complex, and fit for purpose. Exercise critical evaluation and review of both their own work and the work of others.

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11 Excellence it is important for programs in computer science to provide opportunities for students of the highest caliber to achieve their full potential. programs in computer science should not limit those who will lead the development of the discipline in the future. human ingenuity and creativity have fostered the rapid development of the discipline of computer science in the past

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12 Characteristics of IT/CS Graduates System-level perspective. Graduates must develop a high-level understanding of systems as a whole. This understanding must transcend the implementation details of the various components to encompass an appreciation for the structure of computer systems and the processes involved in their construction and analysis.

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13 Characteristics of IT/CS Graduates Appreciation of the interplay between theory and practice. A fundamental aspect of computer science/IT is the balance between theory and practice and the essential link between them. Graduates must understand not only the theoretical underpinnings of the discipline but also how that theory influences practice.

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14 Characteristics of IT/CS Graduates Familiarity with common themes. In the course of an undergraduate program in computer science/IT, students will encounter many recurring themes such as abstraction, complexity, and evolutionary change. Graduates should recognize that these themes have broad application to the field of computer science and must not compartmentalize them as relevant only to the domains in which they were introduced.

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15 Characteristics of IT/CS Graduates Significant project experience. To ensure that graduates can successfully apply the knowledge they have gained, all students in computer science/IT programs must be involved in at least one substantial software project. Such a project demonstrates the practical application of principles learned in different courses and forces students to integrate material learned at different stages of the curriculum.

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16 Characteristics of IT/CS Graduates Adaptability. One of the essential characteristics of computer science over its relatively brief history has been an enormous pace of change. Graduates of a computer science program must possess a solid foundation that allows them to maintain their skills as the field evolves.

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17 Capabilities and Skills of IT/CS Graduates Cognitive capabilities relating to intellectual tasks specific to computer science/IT Practical skills relating to computer science/IT Additional transferable skills that may be developed in the context of computer science/IT but which are of a general nature and applicable in many other contexts as well

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18 Cognitive Capabilities and Skills Knowledge and understanding. Demonstrate knowledge and understanding of essential facts, concepts, principles, and theories relating to computer science and software applications. Modeling. Use such knowledge and understanding in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoff involved in design choices. Requirements. Identify and analyze criteria and specifications appropriate to specific problems, and plan strategies for their solution.

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19 Cognitive Capabilities and Skills Critical evaluation and testing. Analyze the extent to which a computer-based system meets the criteria defined for its current use and future development. Methods and tools. Deploy appropriate theory, practices, and tools for the specification, design, implementation, and evaluation of computer-based systems. Professional responsibility. Recognize and be guided by the social, professional, and ethical issues involved in the use of computer technology.

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20 Practical Capabilities and Skills Design and implementation. Specify, design, and implement computer-based systems. Evaluation. Evaluate systems in terms of general quality attributes and possible tradeoffs presented within the given problem. Information management. Apply the principles of effective information management, information organization, and information-retrieval skills to information of various kinds, including text, images, sound, and video. Operation. Operate computing equipment and software systems effectively.

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21 Practical Capabilities and Skills Human-computer interaction. Apply the principles of human-computer interaction to the evaluation and construction of a wide range of materials including user interfaces, web pages, and multimedia systems. Risk assessment. Identify any risks or safety aspects that may be involved in the operation of computing equipment within a given context. Tools. Deploy effectively the tools used for the construction and documentation of software, with particular emphasis on understanding the whole process involved in using computers to solve practical problems.

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22 Additional Transferable Skills Communication. Make succinct presentations to a range of audiences about technical problems and their solutions. Teamwork. Be able to work effectively as a member of a development team. Numeracy. Understand and explain the quantitative dimensions of a problem.

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23 Additional Transferable Skills Self management. Manage one's own learning and development, including time management and organizational skills Professional development. Keep abreast of current developments in the discipline to continue one's own professional development.

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24 Coping With Change teaching methodology that emphasizes learning as opposed to teaching students continually being challenged to think independently challenging and imaginative exercises that encourage student initiative sound framework with appropriate theory that ensures that the education is sustainable

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25 Coping With Change up to date equipment and teaching materials information resources and appropriate strategies for staying current in the field cooperative learning and the use of communication technologies to promote group interaction need for continuing professional development to promote lifelong learning

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26 Principles Computing is a broad field that extends well beyond the boundaries of computer science. Computer science draws its foundations from a wide variety of disciplines. Development of a computer science curriculum must be sensitive to changes in technology, new developments in pedagogy, and the importance of lifelong learning. Curricula must include professional practice as an integral component.

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27 Computing Curricula 2001 December 15, 2001 Final Report of the Joint ACM/IEEE-CS Task Force on Computing Curricula joint undertaking of the Computer Society of the Institute for Electrical and Electronic Engineers (IEEE-CS) and the Association for Computing Machinery (ACM) Curricular guidelines and set of recommendations for undergraduate programs in computing

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28 IEEE-ACM Computing Curricula al/index.htm al/index.htm Previous recommendations came out in 1965, 1973, 1981, 1991, 2001 Latest: December 15, 2001

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29 ACM-IEEE Computing Curricula

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30 CS Body of Knowledge DS. Discrete StructuresSP. Social & Professional Issues PF. Programming FundamentalsNC. Net-Centric Computing AR. Architecture & OrganizationIS. Intelligent Systems AL. Algorithms & ComplexityIM. Information Management SE. Software EngineeringHC. Human-Computer Interaction PL. Programming LanguagesGV. Graphics & Visual Computing OS. Operating SystemsCN. Computational Science

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31 Pedagogy Focus Groups PFG1. Introductory topics and courses PFG2. Supporting topics and courses PFG3. The computing core PFG4. Professional practices PFG5. Advanced study and undergraduate research PFG6. Computing across the curriculum

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32 Computing Curricula Topics 14 Subject Areas 132 topics divided between these 14 subject areas 64 out of 132 topics designated as core

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33 Core vs. Elective Core: Those topics required of all students in all CS degree programs Minimal, and is not a complete curriculum Must be supplemented by additional material May be taken as introductory, intermediate, or advanced course Elective: Topics that are not part of the core

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34 Discrete Structures (43 core hrs.) DS1. Functions, relations, and sets (6) DS2. Basic logic (10) DS3. Proof techniques (12) DS4. Basics of counting (5) DS5. Graphs and trees (4) DS6. Discrete probability (6)

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35 Programming Fundamentals (38) PF1. Fundamental programming constructs (9) PF2. Algorithms and problem-solving (6) PF3. Fundamental data structures (14) PF4. Recursion (5) PF5. Event-driven programming (4)

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36 Algorithms & Complexity (31) AL1. Basic algorithmic analysis (4) AL2. Algorithmic strategies (6) AL3. Fundamental computing algorithms (12) AL4. Distributed algorithms (3) AL5. Basic computability (6)

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37 Architecture & Organization (36) AR1. Digital logic and digital systems (6) AR2. Machine level representation of data (3) AR3. Assembly level machine organization (9) AR4. Memory system organization and architecture (5) AR5. Interfacing and communication (3) AR6. Functional organization (7) AR7. Multiprocessing and alternative architectures (3)

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38 Operating Systems (18) OS1. Overview of operating systems (2) OS2. Operating system principles (2) OS3. Concurrency (6) OS4. Scheduling and dispatch (3) OS5. Memory management (5)

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39 Net-Centric Computing (15) NC1. Introduction to net-centric computing (2) NC2. Communication and networking (7) NC3. Network security (3) NC4. The web as an example of client-server computing (3)

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40 Programming Languages (21) PL1. Overview of programming languages (2) PL2. Virtual machines (1) PL3. Introduction to language translation (2) PL4. Declarations and types (3) PL5. Abstraction mechanisms (3) PL6. Object-oriented programming (10)

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41 Human-Computer Interaction (8) HC1. Foundations of human-computer interaction (6) HC2. Building a simple graphical user interface (2)

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42 Graphics & Visual Computing (3) GV1. Fundamental techniques in graphics (2) GV2. Graphic systems (1)

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43 Intelligent Systems (10) IS1. Fundamental issues in intelligent systems (1) IS2. Search and constraint satisfaction (5) IS3. Knowledge representation and reasoning (4)

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44 Information Management (10) IM1. Information models and systems (3) IM2. Database systems (3) IM3. Data modeling (4)

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45 Social & Prof Issues (16) SP1. History of computing (1) SP2. Social context of computing (3) SP3. Methods and tools of analysis (2) SP4. Professional and ethical responsibilities (3) SP5. Risks and liabilities of computer-based systems (2) SP6. Intellectual property (3) SP7. Privacy and civil liberties (2)

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46 Software Engineering (31) SE1. Software design (8) SE2. Using APIs (5) SE3. Software tools and environments (3) SE4. Software processes (2) SE5. Software requirements and specifications (4) SE6. Software validation (3) SE7. Software evolution (3) SE8. Software project management (3)

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47 Implementation Strategies

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48 Introductory Courses Implementation Strategies Programming first Imperative first Objects first Functional first Breadth first Algorithms first Hardware first

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49 Required Topics in Introductory Courses Functions, relations, and sets Basic logic Basics of counting Discrete probability Fundamental programming constructs Recursion Overview of programming languages Virtual machines Declarations and types Abstraction mechanisms History of computing

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50 Required Topics in Introductory Courses Functions, Relations, and Sets Minimum core coverage time: 6 hours Topics Functions (surjections, injections, inverses, composition) Relations (reflexivity, symmetry, transitivity, equivalence relations) Sets (Venn diagrams, complements, Cartesian products, power sets) Pigeonhole principle Cardinality and countability Learning objectives: 1. Explain with examples the basic terminology of functions, relations, and sets. 2. Perform the operations associated with sets, functions, and relations. 3. Relate practical examples to the appropriate set, function, or relation model, and interpret the associated operations and terminology in context. 4. Demonstrate basic counting principles, including uses of diagonalization and the pigeonhole principle.

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51 Required Topics in Introductory Courses Basic Logic Minimum core coverage time: 10 hours Topics: Propositional logic; Logical connectives Truth tables Normal forms (conjunctive and disjunctive) Validity Predicate logic; Universal and existential quantification Modus ponens and modus tollens Limitations of predicate logic Learning objectives: 1. Apply formal methods of symbolic propositional and predicate logic. 2. Describe how formal tools of symbolic logic are used to model algorithms and real-life situations. 3. Use formal logic proofs and logical reasoning to solve problems such as puzzles. 4. Describe the importance and limitations of predicate logic.

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52 Required Topics in Introductory Courses Basic of Counting Minimum core coverage time: 5 hours Topics: Counting arguments Sum and product rule Inclusion-exclusion principle Arithmetic and geometric progressions Fibonacci numbers The pigeonhole principle Permutations and combinations Basic definitions Pascal's identity The binomial theorem Solving recurrence relations Common examples The Master theorem

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53 Required Topics in Introductory Courses Basic of Counting Minimum core coverage time: 5 hours Learning objectives: 1. Compute permutations and combinations of a set, and interpret the meaning in the context of the particular application. 2. State the definition of the Master theorem. 3. Solve a variety of basic recurrence equations. 4. Analyze a problem to create relevant recurrence equations or to identify important counting questions.

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54 Required Topics in Introductory Courses Discrete Probability Minimum core coverage time: 6 hours Topics: Finite probability space, probability measure, events Conditional probability, independence, Bayes' theorem Integer random variables, expectation Learning objectives: 1. Calculate probabilities of events and expectations of random variables for elementary problems such as games of chance. 2. Differentiate between dependent and independent events. 3. Apply the binomial theorem to independent events and Bayes theorem to dependent events. 4. Apply the tools of probability to solve problems such as the Monte Carlo method, the average case analysis of algorithms, and hashing.

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55 Required Topics in Introductory Courses Discrete Probability Minimum core coverage time: 6 hours Topics: Finite probability space, probability measure, events Conditional probability, independence, Bayes' theorem Integer random variables, expectation Learning objectives: 1. Calculate probabilities of events and expectations of random variables for elementary problems such as games of chance. 2. Differentiate between dependent and independent events. 3. Apply the binomial theorem to independent events and Bayes theorem to dependent events. 4. Apply the tools of probability to solve problems such as the Monte Carlo method, the average case analysis of algorithms, and hashing.

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56 Required Topics in Introductory Courses Fundamental Programming Constructs Minimum core coverage time: 9 hours Topics: Basic syntax and semantics of a higher-level language Variables, types, expressions, and assignment Simple I/O Conditional and iterative control structures Functions and parameter passing Structured decomposition

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57 Required Topics in Introductory Courses Fundamental Programming Constructs Learning objectives: Analyze and explain the behavior of simple programs involving the fundamental programming constructs covered by this unit. Modify and expand short programs that use standard conditional and iterative control structures and functions. Design, implement, test, and debug a program that uses each of the following fundamental programming constructs: basic computation, simple I/O, standard conditional and iterative structures, and the definition of functions. Choose appropriate conditional and iteration constructs for a given programming task. Apply the techniques of structured (functional) decomposition to break a program into smaller pieces. Describe the mechanics of parameter passing.

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58 Required Topics in Introductory Courses Recursion Minimum core coverage time: 5 hours Topics: The concept of recursion Recursive mathematical functions Simple recursive procedures Divide-and-conquer strategies Recursive backtracking Implementation of recursion

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59 Required Topics in Introductory Courses Recursion Minimum core coverage time: 5 hours Learning objectives: 1. Describe the concept of recursion and give examples of its use. 2. Identify the base case and the general case of a recursively defined problem. 3. Compare iterative and recursive solutions for elementary problems such as factorial. 4. Describe the divide-and-conquer approach. 5. Implement, test, and debug simple recursive functions and procedures. 6. Describe how recursion can be implemented using a stack. 7. Discuss problems for which backtracking is an appropriate solution. 8. Determine when a recursive solution is appropriate for a problem.

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60 Required Topics in Introductory Courses Overview of Programming Languages Minimum core coverage time: 2 hours Topics: History of programming languages Brief survey of programming paradigms Procedural languages Object-oriented languages Functional languages Declarative, non-algorithmic languages Scripting languages The effects of scale on programming methodology

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61 Required Topics in Introductory Courses Overview of Programming Languages Minimum core coverage time: 2 hours Learning objectives: 1. Summarize the evolution of programming languages illustrating how this history has led to the paradigms available today. 2. Identify at least one distinguishing characteristic for each of the programming paradigms covered in this unit. 3. Evaluate the tradeoffs between the different paradigms, considering such issues as space efficiency, time efficiency (of both the computer and the programmer), safety, and power of expression. 4. Distinguish between programming-in-the-small and programming-in-the-large.

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62 Required Topics in Introductory Courses Virtual Machines Minimum core coverage time: 1 hour Topics: The concept of a virtual machine Hierarchy of virtual machines Intermediate languages Security issues arising from running code on an alien machine Learning objectives: 1. Describe the importance and power of abstraction in the context of virtual machines. 2. Explain the benefits of intermediate languages in the compilation process. 3. Evaluate the tradeoffs in performance vs. portability. 4. Explain how executable programs can breach computer system security by accessing disk files and memory.

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63 Required Topics in Introductory Courses Declarations and Types Minimum core coverage time: 3 hours Topics: The conception of types as a set of values with together with a set of operations Declaration models (binding, visibility, scope, and lifetime) Overview of type-checking Garbage collection Learning objectives: 1. Explain the value of declaration models, especially with respect to programming-in-the- large. 2. Identify and describe the properties of a variable such as its associated address, value, scope, persistence, and size. 3. Discuss type incompatibility. 4. Demonstrate different forms of binding, visibility, scoping, and lifetime management. 5. Defend the importance of types and type-checking in providing abstraction and safety. 6. Evaluate tradeoffs in lifetime management (reference counting vs. garbage collection).

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64 Required Topics in Introductory Courses Abstraction Mechanisms Minimum core coverage time: 3 hours Topics: Procedures, functions, and iterators as abstraction mechanisms Parameterization mechanisms (reference vs. value) Activation records and storage management Type parameters and parameterized types Modules in programming languages Learning objectives: 1. Explain how abstraction mechanisms support the creation of reusable software components. 2. Demonstrate the difference between call-by-value and call-by-reference parameter passing. 3. Defend the importance of abstractions, especially with respect to programming-in-the-large. 4. Describe how the computer system uses activation records to manage program modules and their data.

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65 Required Topics in Introductory Courses History of computing Minimum core coverage time: 1 hour Topics: Prehistory -- the world before 1946 History of computer hardware, software, networking Pioneers of computing Learning objectives: 1. List the contributions of several pioneers in the computing field. 2. Compare daily life before and after the advent of personal computers and the Internet. 3. Identify significant continuing trends in the history of the computing field.

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66 Other Topics in Introductory Courses Proof techniques: The structure of formal proofs; proof techniques: direct, counterexample, contraposition, contradiction; mathematical induction Algorithms and problem-solving: Problem-solving strategies; the role of algorithms in the problem-solving process; the concept and properties of algorithms; debugging strategies

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67 Other Topics in Introductory Courses Fundamental data structures: Primitive types; arrays; records; strings and string processing; data representation in memory; static, stack, and heap allocation; runtime storage management; pointers and references; linked structures Basic algorithmic analysis: Big O notation; standard complexity classes; empirical measurements of performance; time and space tradeoffs in algorithms

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68 Other Topics in Introductory Courses Fundamental computing algorithms: Simple numerical algorithms; sequential and binary search algorithms; quadratic and O(N log N) sorting algorithms; hashing; binary search trees Digital logic and digital systems: Logic gates; logic expressions

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69 Other Topics in Introductory Courses Object-oriented programming: Object-oriented design; encapsulation and information-hiding; separation of behavior and implementation; classes, subclasses, and inheritance; polymorphism; class hierarchies Software design: Fundamental design concepts and principles; object-oriented analysis and design; design for reuse

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70 Other Topics in Introductory Courses Using APIs: API programming; class browsers and related tools; programming by example; debugging in the API environment Software tools and environments: Programming environments; testing tools Software requirements and specifications: Importance of specification in the software process Software validation: Testing fundamentals; test case generation

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71 Intermediate Courses: Goal To present the fundamental ideas and enduring concepts of computer science that every student must learn to work successfully in the field. In doing so, these intermediate courses lay the foundation for more advanced work in computer science.

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72 Intermediate Courses: Implementation Strategies Traditional approach in which each course addresses a single topic Compressed approach that organises courses around broader themes System-based approach Web-based approach that uses networking as its organizing principle

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73 Intermediate Courses: Topic-Based Approach CS210 T. Algorithm Design and Analysis CS220 T. Computer Architecture CS225 T. Operating Systems CS230 T. Net-centric Computing CS260 T. Artificial Intelligence CS270 T. Databases CS280 T. Social and Professional Issues CS290 T. Software Development CS490. Capstone Project

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74 Intermediate Courses: Compressed Approach CS210 C. Algorithm Design and Analysis CS220 C. Computer Architecture CS226 C. Operating Systems and Networking CS262 C. Information and Knowledge Management CS292 C. Software Development and Professional Practice

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75 Intermediate Courses: Systems-Based Approach CS120. Introduction to Computer Organization CS210 S. Algorithm Design and Analysis CS220 S. Computer Architecture CS226 S. Operating Systems and Networking CS240 S. Programming Language Translation CS255 S. Computer Graphics CS260 S. Artificial Intelligence CS271 S. Information Management CS291 S. Software Development and Systems Programming CS490. Capstone Project

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76 Intermediate Courses: Web-Based Approach CS130. Introduction to the World-Wide Web CS210 W. Algorithm Design and Analysis CS221 W. Architecture and Operating Systems CS222 W. Architectures for Networking and Communication CS230 W. Net-centric Computing CS250 W. Human-Computer Interaction CS255 W. Computer Graphics CS261 W. AI and Information CS292 W. Software Development and Professional Practice

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77 General Requirements Mathematical rigor The scientific method Familiarity with applications Communications skills Working in teams The complementary curriculum

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78 Advanced Courses Advanced courses – courses whose content is substantially beyond the material of the core.

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79 Sample Curricula: Minimum Requirements Cover all 280 hours of core material in the CS body of knowledge Require sufficient advanced coursework to provide depth in at least one area of computer science Include an appropriate level of supporting mathematics Offer students exposure to "real world" professional skills such as research experience, teamwork, technical writing, and project development

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80 Thesis vs Final Project Thesis is research-oriented Thesis must have “original contribution to knowledge.” Project is development-oriented Project may be software development, information systems development, or web application development

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81 ACM Recommendation CS390. Capstone Project Course Description: Offers students the opportunity to integrate their knowledge of the undergraduate computer science curriculum by implementing a significant software system as part of a programming team. Prerequisites: CS261, CS262, or CS360

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82 ACM Syllabus: Using APIs Human-centered software evaluation Human-centered software development Graphical user-interface design Graphical user-interface programming Software requirements and specifications Software design Software validation Software project management Software tools and environments Effective team management Communications skills

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83 ACM Approach: This course is different in flavor and concept from most of the earlier courses in the curriculum in that it is focused primarily on a project. There may be lectures—particularly if the earlier courses do not cover the full set of required units in the core— but the overall idea is that students should have a chance to apply all the skills they have learned in the curriculum toward the completion of a team project. Thus, this course has the effect of reinforcing concepts that have been learned earlier in a more theoretical way.

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84 UNESCO Informatics Curriculum Framework 2000 for Higher Education

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85 ACM The ACM Computing Classification System [1998 Version] html

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86 Association for Information Systems AIS is actively involved in the development and ongoing update of curriculum at both the undergraduate and graduate levels. IS’97: Model Curriculum and Guidelines for Undergraduate Degree Programs in Information Systems

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87 Association for Information Systems IS 2002: Model Curriculum and Guidelines for Undergraduate Degree Programs in Information Systems IS 2002 is the latest undergraduate model curriculum and is the first update of the curriculum effort of the AIS, ACM and AITP societies since IS'97. IS'97 has been widely accepted and has become the basis for accreditation of undergraduate programs of information systems. This report has been endorsed by seven organizations including SIM.

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88 Association for Information Systems MSIS 2000 Model Graduate Curriculum MSIS is a Model Curriculum and Guidelines for Graduate Degree Programs in Information Systems. It was jointly prepared by representatives from AIS and ACM.

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89 ECDL Foundation The European Computer Driving Licence standard of competence [since 1997] the ECDL is an internationally recognized standard of competence certifying that the holder has the knowledge and skills needed to use the most common computer applications efficiently and productively

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90 National Science Foundation (NSF) ISCC’99: An Information Systems- Centric Curriculum ’99. Program Guidelines for Educating the Next Generation of Information Systems Specialists, in Collaboration with Industry

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91 IEEE Computer Society/ACM Computing Curriculum - Computer Engineering Computing Curricula Volume on Computer Engineering: Computer Engineering Body of Knowledge

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92 National Science Education Standards outlines what students need to know, understand, and be able to do to be scientifically literate at different levels. describes an educational system in which all students demonstrate high levels of performance, in which teachers are empowered to make the decisions essential for effective learning, in which interlocking communities of teachers and students are focused on learning science, and in which supportive educational programs and systems nurture achievement.

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93 CHED CHED MEMORANDUM ORDER (CMO) NO. 25 ; Series of 2001 SUBJECT : Revised Policies And Standards For Information Technology Education (ITE) MO_25.doc MO_25.doc

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94 CHED Basic Core Topics Basic Non-ITE Core Topics Communication skills; Technical writing / presentation skills; Algebra /trigonometry; Values Formation; Probability / Statistics

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95 CHED Basic Core Topics Basic ITE Core Topics Professional Ethics / Code of Ethics for the Filipino IT Professional; Mathematical Logic / Discrete mathematics; Problem Solving; Quality Processes; Fundamentals of programming / program logic formulation; Introduction to the Internet / Web-based programming; IT Fundamentals; Computer Systems Organization

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96 Implementation Factors and Strategies There is no single ideal model curriculum. need for a considerable degree of freedom for implementation account for specific needs, restrictions, preconditions and circumstantial opportunities, such as cultural and societal setting institutional size and scope specific disciplines and educational programs offered by the educational institution available budget, personnel and resources background and potential of the faculty

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97 Implementation Factors and Strategies culture among faculty and management management commitment to informatics willingness to change student-body characteristics access to informatics expertise in general access to collaborative or transfer options with other institutes access to collaborative or transfer options with industry level of informatics penetration in the region.

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98 Curriculum Review review whole curriculum and compare with recommendations, guidelines and policies. compare curricula with actual teaching practice review each syllabi identify problems areas concentrate faculty development efforts on problem areas Ensure that introductory courses are taught properly prioritize core courses over electives

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