| School/Faculty/Institute |
Graduate School |
| Course Code |
CSE 611 |
| Course Title in English |
Research Methods and Ethics |
| Course Title in Turkish |
Araştırma Yöntemleri ve Etik |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Select |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
188 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
None |
| Co-requisites |
None |
| Registration Restrictions |
Only Doctorate Students |
| Overall Educational Objective |
To learn advanced data analysis, evaluation, and interpretation methods and how to construct and implement scientific research methods. |
| Course Description |
This course examines the research process (problem identification, data collection, data analysis and interpretation of results), reviews major scientific research methods (experimental method, descriptive method, etc.) learn the techniques of constructing, conceptualizing, functionalizing, data collection, data analysis, data evaluation/interpretation and report writing. |
| Course Description in Turkish |
Bu derste araştırma sürecini (sorun belirleme, veri toplama, veri analizi ve sonuçları yorumlama) inceler, belli başlı bilimsel araştırma yöntemlerini (deneysel yöntem, betimleme yöntemi vd.) gözden geçirir ve belirli bir konu hakkında araştırma yapabilmeleri için gereken araştırma sorusu bulma, denence (hipotez)kurma, kavramsallaştırma, işlevselleştirme, veri toplama, veri analizi, verileri değerlendirme/yorumlama ve rapor yazma tekniklerini öğrenir. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Validated through surveys and different techniques in preparing the large data set
2) Applying advanced classification and clustering methods to predict future trend in large data sets
3) Evaluating the performance of data science methods
4) Design and build hybrid data processing methods
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
|
|
| |
Program Outcomes and Competences |
Level |
Assessed by |
| Prepared by and Date |
, |
| Course Coordinator |
ADEM KARAHOCA |
| Semester |
Fall |
| Name of Instructor |
Assoc. Prof. Dr. DİLEK KARAHOCA |
Course Contents
| Week |
Subject |
| 1) |
Introduction to Scientific Research Techniques |
| 2) |
Major Research Methods |
| 3) |
Sampling Techniques |
| 4) |
Data Collection Techniques |
| 5) |
Questionnaires |
| 6) |
Measurement in Research Techniques |
| 7) |
Experimental method |
| 8) |
Description Method |
| 9) |
Hypothesizing |
| 10) |
Qualitative Studies -1 |
| 11) |
Nitel Çalışmalar -2 |
| 12) |
Ethical Approaches |
| 13) |
Ethical Violations and Plagiarism |
| 14) |
Bibliography Preparation |
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
| Required/Recommended Readings | Babbie, E. (2006). The practice of social research. 11th ed. Belmont, CA: Wadsworth.
Creswell, J.W. (2009). Research design: Quantitative, qualitative, and mixed methods approaches. 3rd ed. Thousand Oaks, CA: Sage.
|
| Teaching Methods | Flipped learning is used as an instructional strategy. Students work individually for applied assignments.
|
| Homework and Projects | Ödev |
| Laboratory Work | None |
| Computer Use | Required |
| Other Activities | None |
| Assessment Methods |
| Assessment Tools |
Count |
Weight |
| Homework Assignments |
1 |
% 25 |
| Project |
1 |
% 25 |
| Midterm(s) |
1 |
% 50 |
| TOTAL |
% 100 |
|
| Course Administration |
Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54. |
Information Package / Course Catalogue