Organizational Issues & Learning Targets. Procedural Programming Lecture 1 - Summer Semester 2016

Size: px
Start display at page:

Download "Organizational Issues & Learning Targets. Procedural Programming Lecture 1 - Summer Semester 2016"

Transcription

1 Procedural Programming Lecture 1 - Summer Semester 2016 & Joachim Zumbrägel Faculty of Engineering Institute of Computer Engineering Organizational Issues & Learning Targets 1

2 Course Organization consists of 6 Lectures 6 Exercises with 6 Homeworkd 8 Practical Trainings provides you with basic knowledge about programming (what an engineer should know) has no exam is passed when you got 96 points from 114 (18 from homework, 96 from the practical training) 3 Exercise We have 6 exercises We discuss topics of the lecture We discuss exercises and their solutions We discuss home exercises. You get points for your home exercise. (3 points per solved homework, all together 18) Home exercises are announced at the end of the exercise and must be solved within 1 week, only hand written solutions accepted 4 2

3 Practical Training We have 8 different practical trainings Each practical training takes 90 minutes Each practical training consists of - homework (3 points) - a test in the beginning of each practical training (3 points) - the practical training itself (6 points) => 12 points per practical training 96 POINTS FOR ALL PRACTICAL TRAININGS 5 Practical Training (expected Schedule) R EGISTRATION within next 7 days, until Tuesday the 19 th of April i d /ti/ / d ti /teaching/ss16/pp/lab/registration/index.php /l i t ti /i h Time schedule and group list will be announced after registration deadline Tuesday, starting at 12:00 Wednesday, starting at 15:45 6 3

4 Schedule Date Le. (2h) Ex. (2h) P (2h) Topic of the practical training Tu L1 Tu L2 Tu E1 Tu E2 Tu L3 Tu Pfingstferien Tu L4 P1 Variables, Data Types, Statements, Hello World Tu L5 P2 Boolean expressions, Conditional Executions Nested Tu E3 P3 Finite and Infinite Loops, Break and Continue Tu L6 P4 Functions: Parameters and Arguments, Void Tu E4 P5 Arrays Tu E5 P6 Addressing: Direct and Indirect Addressing to Pointers Tu E6 P7 Strings and Files Tu P8 Math Functions, Linked Lists, and Binary Trees Tu RT Repetition Date for P1 P8, used for missed labs 7 Learning Targets (1/2) After completion of this course, the student: is able to understand and analyze a logical problem. This comprises a clear definition of the problem, and a statement on where the core of the problem lies, is furthermore able to propose a potential way, method or approach that promises to lead to a solution, e.g. understands and is able to apply standard problem solution approaches like reduction, 8 4

5 Learning Targets (2/2) can formalise the proposed problem solving in a generally accepted (algorithmic) manner, by cutting it down into atomic elements of procedures and represent these in pseudo-code or in form of a flowchart, understands the terminology involved in this subject, and can explain relevant terms like computer, program, algorithm, structure, sequence and selection and repetition, operator and operand, function, iteration, recursion, etc. 9 Literature 5

6 Literature V. Anton Spraul (2012) Think like a programmer, an introduction to creative problem solving, no starch press. Arthur Whimbley, Jack Lochhead (1986) Problem Solving & Comprehension, Lawrence Erlbaum Associates, Inc. Nell Dale, Michael McMillan, Chip Weems, Mark A. Headington (2002) Programming and Problem Solving with Visual Basic.NET, Jones & Bartlett Learning. Wolfgang P. Kowalk (1996) System, Modell, Programm. Vom GOTO zur objektorientierten Programmierung, Spektrum Akademischer Verlag. Dietrich Boles (2007) Programmieren spielend gelernt mit dem Java-Hamster-Modell, Vieweg+Teubner Verlag. 11 What is programming (good for)? 6

7 Writing a computer program is only one step in the Program Development Process 13 Implementation is the second step in the Program Development Process Programming or Coding is the process of writing a program for a computer in order to solve a problem. Procedural programming is a programming paradigm, where a number of Simple (only one part of the solution), Independent (separation of concerns), and reusable (by procedure calls) program pieces (modules) constitute a program as a whole. 14 7

8 Programming requires background knowledge, experience, creativity and a problem-solving stance How does a computer execute a program? How to code a program that implements an algorithm? How to design an algorithm giving instructions for solving a problem? How to handle problems such that a computer can assist in solving it? 15 Mind the gap! (1/2) Computers allow for computing concrete models of real systems + System in Reality Component Characteristic Degree of abstraction Characteristic Manipulation AbstractedModel Attribute / Element Value Method By abstraction we simplify the complexity of a system reducing it to a model, which considers only relevant parts of the problem to be solved. 16 8

9 Mind the gap! (2/2) The state of a model is the total of its actual values, which gives us information. We make use of this information to derive knowledge about systems, which helps us to understand and to solve problems. Wisdom Knowledge Information Data Data is information in a form that a computer can use it. It comes in many forms such as: letters, words, number, etc. 17 Problem-Solution (1/3) Changing the model state involves changing its attribute values (data). An algorithm gives a sequence of instructions to change attribute values, which results a state-changing flow of the model. Procedural modelling is based on the classic algorithm concept. Problem-solving techniques and strategies can be applied to cope with a model s complexity. 18 9

10 Problem-Solution (2/3) Algorithm isa logical sequence of instructions for solving a problem in a finite amount of time using a finite amount of information. Recipe Construction Manual Wording of a law 19 Problem-Solution (3/3) An algorithm is executed by a processor, which can be a human or a computer depending on its representation (language). Program Data and instructions written in a programming language for carrying out operations that are used by a computer to solve a problem. Programming is the act of reformulating a problem solving approach (an algorithm) in a programming language (as a program). Compiler and Interpreter are tools to translate a program written in a high-level programming language to machine code (binaries), thereby the gap between different levels of abstraction

11 Computer as program processor Computer one that computes. It is a programmable device that can manipulate data. In 1945, John von Neumann published a conception for constructing universal computer systems, the von Neumann Computer. These computer architectures were successors of the ENIAC system. Institute for Advanced Study in Princeton Founder of Computer Architecture 21 Components of Von Neumann Architecture 1. Input unit: accepts data to be processed from environment. 2. Control unit: controls the other components so that instructions (program) are executed in correct sequence. 3. Main memory (programs and data): an ordered sequence of equaly sized storage cells with a distinct address, each capable of holding a piece of data. 4. Arithmetic logic unit: performes arithmetic (addition, division, etc.) and logical operations (comparing two values). 5. Output unit: presents results of processing to environment

12 The fetch-execute cycle Central Processing Unit (CPU) is made up of ALU and Control Unit. It executes the instructions (program) stored in the memory. 1. The Control Unit retrieves (fetches) the next code instruction (as indicated by program counter) from memory. DATA and INSTRUCTIONS 2. The Instruction is translated into control signals. 3. The control signals tell the appropriate unit (ALU, Memory, I/O) to perform (execute) the instruction. PROGRAM COUNTER 4. The sequence repeats from Step Example of A := B+C in 3-address-code (3AC) Address Content of storage cell Comments adr := con 7 Assignment of cell B with value adr := con 5 Assignment of cell C with value adr := val val Arithmetic operation add Storage cell for variable A Storage cell for variable B Storage cell for variable C Program Counter state:

13 Role of Man and Machine in Problem-Solving Computer is a powerful tool that performs: Faster More reliable With greater memory* then we could do by hand. But, the computer is NOT intelligent. WE specify what to do and how by conducting a relatively mechanic act named programming. Solving problems is a creative task, which requires development of proper solution approaches to complex and sufficiently understood problems. * Human working memory is 7 +/-2 pieces of information Problem Analysis 13

14 How to cope with problems From Problem to Solution The human-way Problem Comprehension Problem Solving Techniques Think like a programmer Transition to algorithmic i solutions Algorithmic solutions by step wise refinement 27 Kinds of problems Problems in-the-large : Engineering task Follows a process, is well developed Problems in-the-small : Puzzle Heuristic approaches Getting started with problems o EASY: see the answer o MEDIUM: see the answer once you engage o HARD: need strategies to come with a potential solution, sometimes for even getting started 28 14

15 Example Problem Connect each box with its same-letter mate without letting the lines cross or leaving the large box. 29 Approaches this problem (1/3) Avoidance No positive attitude as required for a programmer. Overcoming hesitation & delay, even if a solution to the problem is not obvious

16 Approaches to this problem (2/3) Cheat Captain Kirk s approach to the Kobayashi Maru test Change problem conditions: reorder boxes 31 Approaches to this problem (3/3) Engage Experiment: Make a drawing Consider extreme cases: If direct connection does not work, what else could be possible... Collaborate 32 16

17 Example Problem Its solution 33 Problem Comprehension -- Characteristics of Effective Problem Solvers Inaccuracy in Reading Collect all relevant facts of the problem Checking them, concern for accuracy to avoid simple mistakes Inaccuray in Thinking Restate the problem, focus on really comprehending the problem Avoid guessing and jumping to conclusion Weakness in Problem Analysis; Inactiveness Approach problem in a step by step manner look for things that are familar (make analogies), reuse them and merging a solution Divide/reduce the problem, break the problem into parts Have a plan Lack of perseverance Do not get frustrated, take positive attitute Avoid mental block: the fear of starting, overcoming procrastination Failure to Think Aloud Communicate, Ask questions, team work (pair programming), ask for support 34 17

18 Problem-Solving Techniques & Strategies Abstraction, Reduction, Means-End Analysis Analogy, Find a pattern, Adapt different point of view Brainstorming, Research, Experiment, Try-and-Error Top-Down (Divide-and-conquer approach): Hierachical structuring Bottom-Up (Building-block approach): Working Backwards Proof, Logical reasoning 35 Another Problem: How to cross the river? The fox, the goose, and the sack of corn. The boat can carry one item at a time. The fox cannot be left on the same shore as the goose, and the goose cannot be left on the same shore as the sack of corn

19 END of 1st Lecture 19

GACE Computer Science Assessment Test at a Glance

GACE Computer Science Assessment Test at a Glance GACE Computer Science Assessment Test at a Glance Updated May 2017 See the GACE Computer Science Assessment Study Companion for practice questions and preparation resources. Assessment Name Computer Science

More information

CS 101 Computer Science I Fall Instructor Muller. Syllabus

CS 101 Computer Science I Fall Instructor Muller. Syllabus CS 101 Computer Science I Fall 2013 Instructor Muller Syllabus Welcome to CS101. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts of

More information

Computer Architecture CSC

Computer Architecture CSC Computer Architecture CSC 343 001 Greg T. Harber Department of Computer Science Nelson Rusche College of Business McGee 303B gth@cs.sfasu.edu 468-1867, 468-2508 Office Hours Monday 10:30-11:30 1:30-2:30

More information

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts

More information

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate

More information

Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I

Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I Session 1793 Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I John Greco, Ph.D. Department of Electrical and Computer Engineering Lafayette College Easton, PA 18042 Abstract

More information

Software Maintenance

Software Maintenance 1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories

More information

Rover Races Grades: 3-5 Prep Time: ~45 Minutes Lesson Time: ~105 minutes

Rover Races Grades: 3-5 Prep Time: ~45 Minutes Lesson Time: ~105 minutes Rover Races Grades: 3-5 Prep Time: ~45 Minutes Lesson Time: ~105 minutes WHAT STUDENTS DO: Establishing Communication Procedures Following Curiosity on Mars often means roving to places with interesting

More information

LEGO MINDSTORMS Education EV3 Coding Activities

LEGO MINDSTORMS Education EV3 Coding Activities LEGO MINDSTORMS Education EV3 Coding Activities s t e e h s k r o W t n e d Stu LEGOeducation.com/MINDSTORMS Contents ACTIVITY 1 Performing a Three Point Turn 3-6 ACTIVITY 2 Written Instructions for a

More information

Teaching Algorithm Development Skills

Teaching Algorithm Development Skills International Journal of Advanced Computer Science, Vol. 3, No. 9, Pp. 466-474, Sep., 2013. Teaching Algorithm Development Skills Jungsoon Yoo, Sung Yoo, Suk Seo, Zhijiang Dong, & Chrisila Pettey Manuscript

More information

Taking Kids into Programming (Contests) with Scratch

Taking Kids into Programming (Contests) with Scratch Olympiads in Informatics, 2009, Vol. 3, 17 25 17 2009 Institute of Mathematics and Informatics, Vilnius Taking Kids into Programming (Contests) with Scratch Abdulrahman IDLBI Syrian Olympiad in Informatics,

More information

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these

More information

Computer Organization I (Tietokoneen toiminta)

Computer Organization I (Tietokoneen toiminta) 581305-6 Computer Organization I (Tietokoneen toiminta) Teemu Kerola University of Helsinki Department of Computer Science Spring 2010 1 Computer Organization I Course area and goals Course learning methods

More information

MINISTRY OF EDUCATION

MINISTRY OF EDUCATION Republic of Namibia MINISTRY OF EDUCATION NAMIBIA SENIOR SECONDARY CERTIFICATE (NSSC) COMPUTER STUDIES SYLLABUS HIGHER LEVEL SYLLABUS CODE: 8324 GRADES 11-12 2010 DEVELOPED IN COLLABORATION WITH UNIVERSITY

More information

Knowledge-Based - Systems

Knowledge-Based - Systems Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University

More information

Intermediate Algebra

Intermediate Algebra Intermediate Algebra An Individualized Approach Robert D. Hackworth Robert H. Alwin Parent s Manual 1 2005 H&H Publishing Company, Inc. 1231 Kapp Drive Clearwater, FL 33765 (727) 442-7760 (800) 366-4079

More information

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits. DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE Sample 2-Year Academic Plan DRAFT Junior Year Summer (Bridge Quarter) Fall Winter Spring MMDP/GAME 124 GAME 310 GAME 318 GAME 330 Introduction to Maya

More information

Using Virtual Manipulatives to Support Teaching and Learning Mathematics

Using Virtual Manipulatives to Support Teaching and Learning Mathematics Using Virtual Manipulatives to Support Teaching and Learning Mathematics Joel Duffin Abstract The National Library of Virtual Manipulatives (NLVM) is a free website containing over 110 interactive online

More information

Computer Science 141: Computing Hardware Course Information Fall 2012

Computer Science 141: Computing Hardware Course Information Fall 2012 Computer Science 141: Computing Hardware Course Information Fall 2012 September 4, 2012 1 Outline The main emphasis of this course is on the basic concepts of digital computing hardware and fundamental

More information

CS 100: Principles of Computing

CS 100: Principles of Computing CS 100: Principles of Computing Kevin Molloy August 29, 2017 1 Basic Course Information 1.1 Prerequisites: None 1.2 General Education Fulfills Mason Core requirement in Information Technology (ALL). 1.3

More information

CREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT

CREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT CREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT Rajendra G. Singh Margaret Bernard Ross Gardler rajsingh@tstt.net.tt mbernard@fsa.uwi.tt rgardler@saafe.org Department of Mathematics

More information

Computer Science. Embedded systems today. Microcontroller MCR

Computer Science. Embedded systems today. Microcontroller MCR Computer Science Microcontroller Embedded systems today Prof. Dr. Siepmann Fachhochschule Aachen - Aachen University of Applied Sciences 24. März 2009-2 Minuteman missile 1962 Prof. Dr. Siepmann Fachhochschule

More information

Phys4051: Methods of Experimental Physics I

Phys4051: Methods of Experimental Physics I Phys4051: Methods of Experimental Physics I 5 credits This course is the first of a two-semester sequence on the techniques used in a modern experimental physics laboratory. Because of the importance of

More information

What is a Mental Model?

What is a Mental Model? Mental Models for Program Understanding Dr. Jonathan I. Maletic Computer Science Department Kent State University What is a Mental Model? Internal (mental) representation of a real system s behavior,

More information

Backwards Numbers: A Study of Place Value. Catherine Perez

Backwards Numbers: A Study of Place Value. Catherine Perez Backwards Numbers: A Study of Place Value Catherine Perez Introduction I was reaching for my daily math sheet that my school has elected to use and in big bold letters in a box it said: TO ADD NUMBERS

More information

Activities, Exercises, Assignments Copyright 2009 Cem Kaner 1

Activities, Exercises, Assignments Copyright 2009 Cem Kaner 1 Patterns of activities, iti exercises and assignments Workshop on Teaching Software Testing January 31, 2009 Cem Kaner, J.D., Ph.D. kaner@kaner.com Professor of Software Engineering Florida Institute of

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

Learning to Schedule Straight-Line Code

Learning to Schedule Straight-Line Code Learning to Schedule Straight-Line Code Eliot Moss, Paul Utgoff, John Cavazos Doina Precup, Darko Stefanović Dept. of Comp. Sci., Univ. of Mass. Amherst, MA 01003 Carla Brodley, David Scheeff Sch. of Elec.

More information

School of Innovative Technologies and Engineering

School of Innovative Technologies and Engineering School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius

More information

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,

More information

Guide to Teaching Computer Science

Guide to Teaching Computer Science Guide to Teaching Computer Science Orit Hazzan Tami Lapidot Noa Ragonis Guide to Teaching Computer Science An Activity-Based Approach Dr. Orit Hazzan Associate Professor Technion - Israel Institute of

More information

Seminar - Organic Computing

Seminar - Organic Computing Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts

More information

Computer Science 1015F ~ 2016 ~ Notes to Students

Computer Science 1015F ~ 2016 ~ Notes to Students Computer Science 1015F ~ 2016 ~ Notes to Students Course Description Computer Science 1015F and 1016S together constitute a complete Computer Science curriculum for first year students, offering an introduction

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

A Metacognitive Approach to Support Heuristic Solution of Mathematical Problems

A Metacognitive Approach to Support Heuristic Solution of Mathematical Problems A Metacognitive Approach to Support Heuristic Solution of Mathematical Problems John TIONG Yeun Siew Centre for Research in Pedagogy and Practice, National Institute of Education, Nanyang Technological

More information

Proof Theory for Syntacticians

Proof Theory for Syntacticians Department of Linguistics Ohio State University Syntax 2 (Linguistics 602.02) January 5, 2012 Logics for Linguistics Many different kinds of logic are directly applicable to formalizing theories in syntax

More information

We are strong in research and particularly noted in software engineering, information security and privacy, and humane gaming.

We are strong in research and particularly noted in software engineering, information security and privacy, and humane gaming. Computer Science 1 COMPUTER SCIENCE Office: Department of Computer Science, ECS, Suite 379 Mail Code: 2155 E Wesley Avenue, Denver, CO 80208 Phone: 303-871-2458 Email: info@cs.du.edu Web Site: Computer

More information

WSU Five-Year Program Review Self-Study Cover Page

WSU Five-Year Program Review Self-Study Cover Page WSU Five-Year Program Review Self-Study Cover Page Department: Program: Computer Science Computer Science AS/BS Semester Submitted: Spring 2012 Self-Study Team Chair: External to the University but within

More information

Cleveland State University Introduction to University Life Course Syllabus Fall ASC 101 Section:

Cleveland State University Introduction to University Life Course Syllabus Fall ASC 101 Section: Cleveland State University Introduction to University Life Course Syllabus Fall 2016 - ASC 101 Section: Day: Time: Location: Office Hours: By Appointment Instructor: Office: Phone: Email: @CSU_FYE (CSU

More information

Visual CP Representation of Knowledge

Visual CP Representation of Knowledge Visual CP Representation of Knowledge Heather D. Pfeiffer and Roger T. Hartley Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001, USA email: hdp@cs.nmsu.edu and rth@cs.nmsu.edu

More information

My Program is Correct But it Doesn t Run: A Preliminary Investigation of Novice Programmers Problems

My Program is Correct But it Doesn t Run: A Preliminary Investigation of Novice Programmers Problems My Program is Correct But it Doesn t Run: A Preliminary Investigation of Novice Programmers Problems Sandy Garner 1, Patricia Haden 2, Anthony Robins 3 1,3 Computer Science Department, The University of

More information

A Context-Driven Use Case Creation Process for Specifying Automotive Driver Assistance Systems

A Context-Driven Use Case Creation Process for Specifying Automotive Driver Assistance Systems A Context-Driven Use Case Creation Process for Specifying Automotive Driver Assistance Systems Hannes Omasreiter, Eduard Metzker DaimlerChrysler AG Research Information and Communication Postfach 23 60

More information

1.11 I Know What Do You Know?

1.11 I Know What Do You Know? 50 SECONDARY MATH 1 // MODULE 1 1.11 I Know What Do You Know? A Practice Understanding Task CC BY Jim Larrison https://flic.kr/p/9mp2c9 In each of the problems below I share some of the information that

More information

Computerized Adaptive Psychological Testing A Personalisation Perspective

Computerized Adaptive Psychological Testing A Personalisation Perspective Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES

More information

Evolution of Collective Commitment during Teamwork

Evolution of Collective Commitment during Teamwork Fundamenta Informaticae 56 (2003) 329 371 329 IOS Press Evolution of Collective Commitment during Teamwork Barbara Dunin-Kȩplicz Institute of Informatics, Warsaw University Banacha 2, 02-097 Warsaw, Poland

More information

Writing Research Articles

Writing Research Articles Marek J. Druzdzel with minor additions from Peter Brusilovsky University of Pittsburgh School of Information Sciences and Intelligent Systems Program marek@sis.pitt.edu http://www.pitt.edu/~druzdzel Overview

More information

Class Meeting Time and Place: Section 3: MTWF10:00-10:50 TILT 221

Class Meeting Time and Place: Section 3: MTWF10:00-10:50 TILT 221 Math 155. Calculus for Biological Scientists Fall 2017 Website https://csumath155.wordpress.com Please review the course website for details on the schedule, extra resources, alternate exam request forms,

More information

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016 AGENDA Advanced Learning Theories Alejandra J. Magana, Ph.D. admagana@purdue.edu Introduction to Learning Theories Role of Learning Theories and Frameworks Learning Design Research Design Dual Coding Theory

More information

SIE: Speech Enabled Interface for E-Learning

SIE: Speech Enabled Interface for E-Learning SIE: Speech Enabled Interface for E-Learning Shikha M.Tech Student Lovely Professional University, Phagwara, Punjab INDIA ABSTRACT In today s world, e-learning is very important and popular. E- learning

More information

CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University

CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9

More information

What is Thinking (Cognition)?

What is Thinking (Cognition)? What is Thinking (Cognition)? Edward De Bono says that thinking is... the deliberate exploration of experience for a purpose. The action of thinking is an exploration, so when one thinks one investigates,

More information

Syllabus for CHEM 4660 Introduction to Computational Chemistry Spring 2010

Syllabus for CHEM 4660 Introduction to Computational Chemistry Spring 2010 Instructor: Dr. Angela Syllabus for CHEM 4660 Introduction to Computational Chemistry Office Hours: Mondays, 1:00 p.m. 3:00 p.m.; 5:00 6:00 p.m. Office: Chemistry 205C Office Phone: (940) 565-4296 E-mail:

More information

University of Groningen. Systemen, planning, netwerken Bosman, Aart

University of Groningen. Systemen, planning, netwerken Bosman, Aart University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document

More information

Getting Started with Deliberate Practice

Getting Started with Deliberate Practice Getting Started with Deliberate Practice Most of the implementation guides so far in Learning on Steroids have focused on conceptual skills. Things like being able to form mental images, remembering facts

More information

INTRODUCTION TO PSYCHOLOGY

INTRODUCTION TO PSYCHOLOGY INTRODUCTION TO PSYCHOLOGY General Information: Instructor: Email: Required Books: Supplemental Novels: Mr. Robert W. Dill rdill@fhrangers.org Spencer A. Rathus, Psychology: Principles in Practice. Austin,

More information

Data Structures and Algorithms

Data Structures and Algorithms CS 3114 Data Structures and Algorithms 1 Trinity College Library Univ. of Dublin Instructor and Course Information 2 William D McQuain Email: Office: Office Hours: wmcquain@cs.vt.edu 634 McBryde Hall see

More information

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR ROLAND HAUSSER Institut für Deutsche Philologie Ludwig-Maximilians Universität München München, West Germany 1. CHOICE OF A PRIMITIVE OPERATION The

More information

Lecturing Module

Lecturing Module Lecturing: What, why and when www.facultydevelopment.ca Lecturing Module What is lecturing? Lecturing is the most common and established method of teaching at universities around the world. The traditional

More information

MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE

MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,

More information

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1 Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

More information

Reinforcement Learning by Comparing Immediate Reward

Reinforcement Learning by Comparing Immediate Reward Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate

More information

Course Content Concepts

Course Content Concepts CS 1371 SYLLABUS, Fall, 2017 Revised 8/6/17 Computing for Engineers Course Content Concepts The students will be expected to be familiar with the following concepts, either by writing code to solve problems,

More information

CS177 Python Programming

CS177 Python Programming CS177 Python Programming Recitation 1 Introduction Adapted from John Zelle s Book Slides 1 Course Instructors Dr. Elisha Sacks E-mail: eps@purdue.edu Ruby Tahboub (Course Coordinator) E-mail: rtahboub@purdue.edu

More information

UNDERSTANDING DECISION-MAKING IN RUGBY By. Dave Hadfield Sport Psychologist & Coaching Consultant Wellington and Hurricanes Rugby.

UNDERSTANDING DECISION-MAKING IN RUGBY By. Dave Hadfield Sport Psychologist & Coaching Consultant Wellington and Hurricanes Rugby. UNDERSTANDING DECISION-MAKING IN RUGBY By Dave Hadfield Sport Psychologist & Coaching Consultant Wellington and Hurricanes Rugby. Dave Hadfield is one of New Zealand s best known and most experienced sports

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Study Group Handbook

Study Group Handbook Study Group Handbook Table of Contents Starting out... 2 Publicizing the benefits of collaborative work.... 2 Planning ahead... 4 Creating a comfortable, cohesive, and trusting environment.... 4 Setting

More information

Language properties and Grammar of Parallel and Series Parallel Languages

Language properties and Grammar of Parallel and Series Parallel Languages arxiv:1711.01799v1 [cs.fl] 6 Nov 2017 Language properties and Grammar of Parallel and Series Parallel Languages Mohana.N 1, Kalyani Desikan 2 and V.Rajkumar Dare 3 1 Division of Mathematics, School of

More information

Modeling user preferences and norms in context-aware systems

Modeling user preferences and norms in context-aware systems Modeling user preferences and norms in context-aware systems Jonas Nilsson, Cecilia Lindmark Jonas Nilsson, Cecilia Lindmark VT 2016 Bachelor's thesis for Computer Science, 15 hp Supervisor: Juan Carlos

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

Running Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY

Running Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY SCIT Model 1 Running Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY Instructional Design Based on Student Centric Integrated Technology Model Robert Newbury, MS December, 2008 SCIT Model 2 Abstract The ADDIE

More information

Learning, Communication, and 21 st Century Skills: Students Speak Up For use with NetDay Speak Up Survey Grades 3-5

Learning, Communication, and 21 st Century Skills: Students Speak Up For use with NetDay Speak Up Survey Grades 3-5 Learning, Communication, and 21 st Century Skills: Students Speak Up For use with NetDay Speak Up Survey Grades 3-5 Grades: 3-5 Subjects: Language Arts, Social Studies/History, Math, Government, Civics,

More information

Implementing a tool to Support KAOS-Beta Process Model Using EPF

Implementing a tool to Support KAOS-Beta Process Model Using EPF Implementing a tool to Support KAOS-Beta Process Model Using EPF Malihe Tabatabaie Malihe.Tabatabaie@cs.york.ac.uk Department of Computer Science The University of York United Kingdom Eclipse Process Framework

More information

Think A F R I C A when assessing speaking. C.E.F.R. Oral Assessment Criteria. Think A F R I C A - 1 -

Think A F R I C A when assessing speaking. C.E.F.R. Oral Assessment Criteria. Think A F R I C A - 1 - C.E.F.R. Oral Assessment Criteria Think A F R I C A - 1 - 1. The extracts in the left hand column are taken from the official descriptors of the CEFR levels. How would you grade them on a scale of low,

More information

Knowledge Elicitation Tool Classification. Janet E. Burge. Artificial Intelligence Research Group. Worcester Polytechnic Institute

Knowledge Elicitation Tool Classification. Janet E. Burge. Artificial Intelligence Research Group. Worcester Polytechnic Institute Page 1 of 28 Knowledge Elicitation Tool Classification Janet E. Burge Artificial Intelligence Research Group Worcester Polytechnic Institute Knowledge Elicitation Methods * KE Methods by Interaction Type

More information

South Carolina College- and Career-Ready Standards for Mathematics. Standards Unpacking Documents Grade 5

South Carolina College- and Career-Ready Standards for Mathematics. Standards Unpacking Documents Grade 5 South Carolina College- and Career-Ready Standards for Mathematics Standards Unpacking Documents Grade 5 South Carolina College- and Career-Ready Standards for Mathematics Standards Unpacking Documents

More information

Bachelor Class

Bachelor Class Bachelor Class 2015-2016 Siegfried Nijssen 11 January 2016 Popularity of Topics 1 Popularity of Topics 4 Popularity of Topics Assignment of Topics I contacted all supervisors with the first choices Most

More information

Automating the E-learning Personalization

Automating the E-learning Personalization Automating the E-learning Personalization Fathi Essalmi 1, Leila Jemni Ben Ayed 1, Mohamed Jemni 1, Kinshuk 2, and Sabine Graf 2 1 The Research Laboratory of Technologies of Information and Communication

More information

Ontology-based smart learning environment for teaching word problems in mathematics

Ontology-based smart learning environment for teaching word problems in mathematics J. Comput. Educ. (2014) 1(4):313 334 DOI 10.1007/s40692-014-0020-z Ontology-based smart learning environment for teaching word problems in mathematics Aparna Lalingkar Chandrashekar Ramnathan Srinivasan

More information

A Study of Metacognitive Awareness of Non-English Majors in L2 Listening

A Study of Metacognitive Awareness of Non-English Majors in L2 Listening ISSN 1798-4769 Journal of Language Teaching and Research, Vol. 4, No. 3, pp. 504-510, May 2013 Manufactured in Finland. doi:10.4304/jltr.4.3.504-510 A Study of Metacognitive Awareness of Non-English Majors

More information

Physical Versus Virtual Manipulatives Mathematics

Physical Versus Virtual Manipulatives Mathematics Physical Versus Free PDF ebook Download: Physical Versus Download or Read Online ebook physical versus virtual manipulatives mathematics in PDF Format From The Best User Guide Database Engineering Haptic

More information

PART C: ENERGIZERS & TEAM-BUILDING ACTIVITIES TO SUPPORT YOUTH-ADULT PARTNERSHIPS

PART C: ENERGIZERS & TEAM-BUILDING ACTIVITIES TO SUPPORT YOUTH-ADULT PARTNERSHIPS PART C: ENERGIZERS & TEAM-BUILDING ACTIVITIES TO SUPPORT YOUTH-ADULT PARTNERSHIPS The following energizers and team-building activities can help strengthen the core team and help the participants get to

More information

Knowledge based expert systems D H A N A N J A Y K A L B A N D E

Knowledge based expert systems D H A N A N J A Y K A L B A N D E Knowledge based expert systems D H A N A N J A Y K A L B A N D E What is a knowledge based system? A Knowledge Based System or a KBS is a computer program that uses artificial intelligence to solve problems

More information

CEFR Overall Illustrative English Proficiency Scales

CEFR Overall Illustrative English Proficiency Scales CEFR Overall Illustrative English Proficiency s CEFR CEFR OVERALL ORAL PRODUCTION Has a good command of idiomatic expressions and colloquialisms with awareness of connotative levels of meaning. Can convey

More information

Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm

Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm syntax: from the Greek syntaxis, meaning setting out together

More information

A R "! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ;

A R ! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ; A R "! I,,, r.-ii ' i '!~ii ii! A ow ' I % i o,... V. 4..... JA' i,.. Al V5, 9 MiN, ; Logic and Language Models for Computer Science Logic and Language Models for Computer Science HENRY HAMBURGER George

More information

Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages

Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer

More information

Predicting Students Performance with SimStudent: Learning Cognitive Skills from Observation

Predicting Students Performance with SimStudent: Learning Cognitive Skills from Observation School of Computer Science Human-Computer Interaction Institute Carnegie Mellon University Year 2007 Predicting Students Performance with SimStudent: Learning Cognitive Skills from Observation Noboru Matsuda

More information

MGMT 3362 Human Resource Management Course Syllabus Spring 2016 (Interactive Video) Business Administration 222D (Edinburg Campus)

MGMT 3362 Human Resource Management Course Syllabus Spring 2016 (Interactive Video) Business Administration 222D (Edinburg Campus) MGMT 3362 Human Resource Management Course Syllabus Spring 2016 (Interactive Video) INSTRUCTOR INFORMATION Instructor: Marco E. Garza, PhD Office: Business Administration 222D (Edinburg Campus) Office

More information

Introduction to Simulation

Introduction to Simulation Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /

More information

Spring 2016 Stony Brook University Instructor: Dr. Paul Fodor

Spring 2016 Stony Brook University Instructor: Dr. Paul Fodor CSE215, Foundations of Computer Science Course Information Spring 2016 Stony Brook University Instructor: Dr. Paul Fodor http://www.cs.stonybrook.edu/~cse215 Course Description Introduction to the logical

More information

ENEE 302h: Digital Electronics, Fall 2005 Prof. Bruce Jacob

ENEE 302h: Digital Electronics, Fall 2005 Prof. Bruce Jacob Course Syllabus ENEE 302h: Digital Electronics, Fall 2005 Prof. Bruce Jacob 1. Basic Information Time & Place Lecture: TuTh 2:00 3:15 pm, CSIC-3118 Discussion Section: Mon 12:00 12:50pm, EGR-1104 Professor

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 1: Basic Concepts of Machine Learning Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

The open source development model has unique characteristics that make it in some

The open source development model has unique characteristics that make it in some Is the Development Model Right for Your Organization? A roadmap to open source adoption by Ibrahim Haddad The open source development model has unique characteristics that make it in some instances a superior

More information

Data Fusion Models in WSNs: Comparison and Analysis

Data Fusion Models in WSNs: Comparison and Analysis Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,

More information

Introduction to Information System

Introduction to Information System Spring Quarter 2015-2016 Meeting day/time: N/A at Online Campus (Distance Learning). Location: Use D2L.depaul.edu to access the course and course materials Instructor: Miranda Standberry-Wallace Office:

More information

How to make successful presentations in English Part 2

How to make successful presentations in English Part 2 Young Researchers Seminar 2013 Young Researchers Seminar 2011 Lyon, France, June 5-7, 2013 DTU, Denmark, June 8-10, 2011 How to make successful presentations in English Part 2 Witold Olpiński PRESENTATION

More information

Function Tables With The Magic Function Machine

Function Tables With The Magic Function Machine Brief Overview: Function Tables With The Magic Function Machine s will be able to complete a by applying a one operation rule, determine a rule based on the relationship between the input and output within

More information

PH.D. IN COMPUTER SCIENCE PROGRAM (POST M.S.)

PH.D. IN COMPUTER SCIENCE PROGRAM (POST M.S.) PH.D. IN COMPUTER SCIENCE PROGRAM (POST M.S.) OVERVIEW ADMISSION REQUIREMENTS PROGRAM REQUIREMENTS OVERVIEW FOR THE PH.D. IN COMPUTER SCIENCE Overview The doctoral program is designed for those students

More information

First Grade Standards

First Grade Standards These are the standards for what is taught throughout the year in First Grade. It is the expectation that these skills will be reinforced after they have been taught. Mathematical Practice Standards Taught

More information