PseudoCode (reprise)

PseudoCode (reprise)

Programming and Algorithms Damian Gordon Module Description This module is an introduction to programming, program design and algorithms. Students are introduced to a structured procedural programming language and to commonly used algorithms and their implementation. This module assumes no prior knowledge of programming or algorithms. The aims of this module are to:

Teach the fundamentals of procedural programming Teach the principles of good program design, implementation, documentation and testing. Teach the theory and application of elementary algorithms and data structures. Learning Outcomes On Completion of this module, the learner will be able to Design and write computer elementary programs in a structured procedural language. Use a text editor with command line tools and simple Integrated

Development Environment (IDE) to compile, link and execute program code. Divide a computer program into modules. Test computer programs to ensure compliance with requirements. Implement elementary algorithms and data structures in a procedural language. Indicative Syllabus Introduction: What is a program? Source code. Machine code. Editing, Compiling, Linking Debugging. Use of an Integrated Development Environment (IDE).

Basic Data Types: integer, floating-point and character data and variables. Basic Input-Output: Display data on a screen. Input data from the keyboard. Programming Structures: Conditional statements: Boolean values and expressions, logical and relational operators, if-statement, casestatement, compound conditional statements. Indicative Syllabus Iterative constructs: while-statements, for-statements and nested control statements. Structured Programming: functions, parameter passing,

returning values. Introduction to Data Structures: single-dimensional arrays, two-dimensional arrays, dynamically allocated arrays Indicative Syllabus Basic Algorithms: summation, counting, numeric operations, swapping, maximum and minimum, simple array manipulation. Elementary Sorting Algorithms: Internal Sorting. Exchange sort. Interchange sort. Bubble sort. Shaker sort. Insertion sort. Testing and debugging: Objectives and principles of testing.

Choosing appropriate test data. Simple debugging using a program trace. Documentation: Style guidelines. Indicative Syllabus Assessment Type Weighting (%)

Written Assessment 70 Continuous Assessment 30 Introduction to Programming Damian Gordon

Binary Assembly Python IDLE Algorithms

An Algorithm is a series of instructions Examples of algorithms include Musical scores Knitting patterns Recipies Row 1: SL 1, K8, K2 tog, K1, turn Algorithms Problem Solving

Analyse Problem (Analysis) Develop Algorithm (Design) Convert into programming language (Development) Blended Learning Traditional Conversation Interaction

Collaboration Motivation On-Line Mobility Tracking Globalisation Flexibility

Blended Learning Diana Laurillards Conversational Framework Teachers Conceptual Model Reflection on student

performance Discussion Adaption of world Teachers Constructed World

Students Conceptual Model Adaption of actions Interaction Reflection on Interaction

Students Experiential Knowledge Gilly Salmons 5-Stage Model of e-Learning Tech Support

Development Knowledge Construction Information Exchange On-Line Socialization Access & Motivation

e-Moderating Providing Links Supporting responding Conferencing Facilitating

process Searching, personalizing software Facilitating tasks Sending and receiving messages

Set-up system and access Providing bridges Welcoming and encouraging Pam Moules eLearning Ladder

Video Conferencing Interactive Media Information Gathering Technical Support E-mail Discussions ICT Skills

Chat rooms ICT Access Length of Engagement Facilitation Group Work

On-line Communities of Practice I N C R E A S

I N G John Hatties Meta-Analysis 1. Feedback 2. Students Prior Cognitive Ability 3. Instructional Quality

4. Direct Instruction 5. Premeditation 1. Feedback 2. Students Prior Cognitive Ability 3. Instructional Quality 4. Direct Instruction 5. Premeditation Robert Gagn's

Nine Events of instructional Design Gain Attention Inform Learner of Objectives Stimulate recall of prior learning

Present Information Provide Guidance Elicit Performance

Provide Feedback Assess Performance Enhance Retention and Transfer Some Challenges of Blended Learning

Technical Issues Motivation of the Students Student Participation Ensuring Interactive of lessons

Organisational Alignment

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