Information Package / Course Catalogue
| Psychology | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Engineering | |||||
| Course Code | IE 100 | |||||
| Course Title in English | Introduction to Industrial Engineering | |||||
| Course Title in Turkish | Endüstri Mühendisliğine Giriş | |||||
| Language of Instruction | EN | |||||
| Type of Course | Flipped Classroom,Lecture | |||||
| Level of Course | Introductory | |||||
| Semester | Fall | |||||
| Contact Hours per Week |
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| Estimated Student Workload | 86 hours per semester | |||||
| Number of Credits | 3 ECTS | |||||
| Grading Mode | Standard Letter Grade | |||||
| Pre-requisites | None | |||||
| Expected Prior Knowledge | none | |||||
| Co-requisites | None | |||||
| Registration Restrictions | Only undergraduate students | |||||
| Overall Educational Objective | To learn the profession, main topics, and approaches of industrial engineering. | |||||
| Course Description | This course provides an introduction to fundamental concepts & approaches of industrial engineering. Following topics are covered: definition & history of industrial engineering; definition & history of operations research (OR); components of an OR model; constructing OR models and categories of OR techniques; differences between linear & nonlinear programming; sample space & events of experiments; mutually exclusive & collectively exhaustive events; conditional probability; independent events; law of total probability; probability distributions; simple linear regression; problem situation; decision trees; charts & diagrams; Markov chains; EOQ models; Bayesian networks; ethical concepts in industrial engineering; contemporary issues in industrial engineering. | |||||
| Course Description in Turkish | Bu ders, endüstri mühendisliğinin esas kavramları ve yaklaşımları hakkında bilgi veren bir giriş dersidir. Ders, şu konu başlıklarını içermektedir: endüstri mühendisliğinin tanımı & tarihi; yöneylem araştırmasının (YA) tanımı ve tarihi; bir YA modelinin bileşenleri; YA modeli kurma ve YA tekniklerinin sınıfları;doğrusal ve doğrusal olmayan programlama farkları, deneylerin örnek uzayları & olayları; karşılıklı dışlamalı & birlikte kapsayıcı olaylar; koşullu olasılık; bağımsız olaylar; toplam olasılık yasası; olasılık dağılımları; basit doğrusal regresyon; problem durumları; tablo & şemalar; Markov zincirleri; ekonomik sipariş miktarı modelleri; Bayes ağları; endüstri mühendisliğinde etik kavramlar; endüstri mühendisliğinde çağdaş konular. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) recognize the Industrial Engineering program and its continuous improvement; 2) explain the concepts of IE/operations research (OR) principles; 3) analyze a situation and use suitable tools & techniques of IE/OR to solve the problems; 4) model engineering problems and apply basic solution methods to these models; 5) recognize the contemporary issues and application areas of Industrial Engineering 6) explain the professional and ethical responsibilities of an industrial engineer. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||||
| 6) Internalization and dissemination of professional ethical standards. | ||||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | HANDE KÜÇÜKAYDIN , March 2024 |
| Course Coordinator | HANDE KÜÇÜKAYDIN |
| Semester | Fall |
| Name of Instructor | Asst. Prof. Dr. HANDE KÜÇÜKAYDIN |
Course Contents
| Week | Subject |
| 1) | Definition, history & main topics of industrial engineering |
| 2) | Industrial engineering program & its continuous improvement |
| 3) | Definition & history of operations research (OR), components of an OR model, constructing OR models and categories of OR techniques |
| 4) | Linear functions, linear equalities & inequalities, linear & nonlinear programming and their differences |
| 5) | Ethical concepts in industrial engineering |
| 6) | Sample space & events of experiments, mutually exclusive & collectively exhaustive events, probability calculus including conditional probability & independent events |
| 7) | Law of total probability, probability distributions |
| 8) | Problem situation, decision problems, and decision trees |
| 9) | Markov chains |
| 10) | Economic order quantity models |
| 11) | Bayesian networks |
| 12) | Simple linear regression |
| 13) | Charts and diagrams |
| 14) | Contemporary issues in industrial engineering |
| 15) | Final Exam/Project/Presentation period |
| 16) | Final Exam/Project/Presentation period |
| Required/Recommended Readings | none | |||||||||||||||||||||
| Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | |||||||||||||||||||||
| Homework and Projects | • Two quizzes • Two essays regarding the ethical concepts and contemporary issues in industrial engineering | |||||||||||||||||||||
| Laboratory Work | none | |||||||||||||||||||||
| Computer Use | MS Excel | |||||||||||||||||||||
| Other Activities | Flipped classroom practice with graded participation | |||||||||||||||||||||
| Assessment Methods |
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| Course Administration |
hande.kucukaydin@mef.edu.tr 212 3953631 Instructor’s -office and phone number: 5th floor, 212 3953631 -office hours: TBA -email address: hande.kucukaydin@mef.edu.tr Exams: Closed book and closed notes. Rules for attendance: YÖK regulations. You are responsible for the announcements made in class. Rules for late submission of assignments: Essay deadlines are always extendable up to 72 hours, with submissions late for (0,24] hours receive 70% of the credit they get, (24,48] hours receive 35% , and (48,72] receive 10%. Missing a quiz: Provided that proper documents of excuse are presented, a make-up exam will be given for each missed quiz. Missing a midterm: You are expected to be present without exception and to plan any travel around these dates accordingly. Medical emergencies are of course excluded if accompanied by a doctor’s note. A note indicating that you were seen at the health center on the day of the exam is not a sufficient documentation of medically excused absence from the exam. The note must say that you were medically unable to take the exam. Provided that proper documents of excuse are presented, a make-up exam will be given for each missed midterm. If you fail to take the exam on the assigned day and do not have a valid excuse, you will be given zero (0) on the exam. Employment interviews, employer events, weddings, vacations, etc. are not excused absences. Eligibility to take the final exam: YÖK regulations. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 2 | 42 | |||
| Homework Assignments | 2 | 2 | 5 | 14 | |||
| Quiz(zes) | 2 | 1 | 0.5 | 3 | |||
| Midterm(s) | 1 | 12 | 1 | 13 | |||
| Final Examination | 1 | 14 | 1 | 15 | |||
| Total Workload | 87 | ||||||
| Total Workload/25 | 3.5 | ||||||
| ECTS | 3 | ||||||