SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code Course Name Year Semester Theoretical Practical Credit ECTS
BLP5108 Fundamentals of Information Technology 2 Fall 3 0 3 6
Course Type : University Elective
Cycle: Associate      TQF-HE:5. Master`s Degree      QF-EHEA:Short Cycle      EQF-LLL:5. Master`s Degree
Language of Instruction: Turkish
Prerequisities and Co-requisities: N/A
Mode of Delivery: Face to face
Name of Coordinator: Instructor SEDANUR AKTÜRK
Dersin Öğretim Eleman(lar)ı: Instructor SEDANUR AKTÜRK
Dersin Kategorisi: Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives: The rapid developments in information and communication technologies have increased the importance of digital skills in every aspect of life, ranging from education to professional environments and from social relations to individual practices. This situation requires individuals to become familiar with computer systems, acquire fundamental knowledge of hardware and software, use the Internet and office applications effectively, and apply technology efficiently in daily life. The aim of this course is to equip students with digital literacy skills, enabling them to gain the knowledge and competencies they will need both in their academic studies and professional careers.
Course Content: In this course, an introduction to information technologies, an overview of computer hardware and software, file and folder management, basic settings and applications on the Windows operating system are covered. Practical topics include creating and formatting text with a word processor (MS Word), data entry, formulas, and charts with a spreadsheet program (MS Excel), and slide design and presentation techniques with presentation software (MS PowerPoint). In addition, Internet and e-mail usage, information security, and the reflections of technology on daily life and professional fields are examined with examples.

Course Specific Rules

Students are expected to read the week's topic from the recommended resources before each lesson and to do the assigned work after each lesson.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
Knowledge (Described as Theoritical and/or Factual Knowledge.)
  1) Explain the fundamental concepts of information technologies and the functioning of computer systems.
Skills (Describe as Cognitive and/or Practical Skills.)
  1) Perform basic operations such as file and folder management on the Windows operating system.
  2) Create, format, and edit documents effectively using MS Word.
  3) Acquire basic data processing skills in MS Excel, including data entry, formula usage, and chart creation.
  4) Design slides and apply presentation techniques using MS PowerPoint.
Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
  1) Enhance problem-solving and information access skills by using fundamental information technologies.
  2) Develop awareness of information security and digital ethics and apply this knowledge in daily life and professional contexts.

Weekly Course Schedule

Week Subject
Materials Sharing *
Related Preparation Further Study
1) Introduction to Basic Information Technologies-I Reference books and lecture notes.
2) Introduction to Basic Information Technologies-II Reference books and lecture notes.
3) Windows Basic Usage Reference books and lecture notes.
4) Word Basics Resource books and lecture notes.
5) Word Advanced Features-I Resource books and lecture notes.
6) Word Advanced Features-II Resource books and lecture notes.
7) PowerPoint Basics Resource books and lecture notes.
8) Midterm
9) PowerPoint Advanced Features Resource books and lecture notes.
10) Industry–Student Meeting
11) Excel Fundamentals Resource books and lecture notes.
12) Advanced Excel Use-I Resource books and lecture notes.
13) Advanced Excel Use-II Resource books and lecture notes.
14) Integrated Practices and Assessment Resource books and lecture notes.
*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks: Çeşitli makaleler.
References: 1-Kılıç, Kadir ve diğerleri. (2022). Temel Bilgi Teknolojileri. Ankara: Palme Yayınevi.
2-Atatürk Üniversitesi. (Güncel). Temel Bilgi Teknolojileri I. Açık Ders Malzemeleri.
3-Yılmazel, Özgür (Ed.). (2014). Temel Bilgi Teknolojileri-I. Eskişehir: Anadolu Üniversitesi Açıköğretim Fakültesi Yayınları.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)

1

2

3

4

5

6

7

Program Öğrenme Çıktıları (PÖÇ)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Programme Learning Outcomes Contribution Level (from 1 to 5)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results. 1
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language. 1
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data. 1
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations. 1
5) They apply natural language processing techniques to text data and develop basic NLP-based applications. 1
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools. 5
7) It creates data-driven decision models using decision support systems. 1
8) It develops optimization models and produces solutions for industrial and sectoral problems. 1
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility. 3
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Teaching and Learning Methods defined at the Programme Level
Teaching and Learning Methods Defined for the Course
Lectures
Case Study
Problem Solving
Demonstration
Views
Laboratory
Homework
Project Preparation
Individual and Group Work
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Aassessment and evaluation Methods defined at the Programme Level
Assessment and Evaluation Methods defined for the Course
Midterm
Quiz
Homework Evaluation
Practice Exam
Active Participation in Class

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods # of practice per semester Level of Contribution
Quizzes 1 % 15.00
Midterms 1 % 35.00
Semester Final Exam 1 % 50.00
Total % 100
PERCENTAGE OF SEMESTER WORK % 50
PERCENTAGE OF FINAL WORK % 50
Total % 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities # of Activities per semester Duration (hour) Total Workload
Course 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 1.5 21
Presentations / Seminar 0 0 0
Project 1 16 16
Homework Assignments 0 0 0
Total Workload of Teaching & Learning Activities - - 79
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities # of Activities per semester Duration (hour) Total Workload
Quizzes 0 0 0
Midterms 1 30 30
Semester Final Exam 1 44 44
Total Workload of Assesment & Evaluation Activities - - 74
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) 153
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) 6