Basic Information

Course Code:

Course Name:

Language of Instruction:

Turkish

Course Type:

Class

Course Level:

Doctorate

ECTS:

30.00

Instructor:

Prof. Dr. İbrahim Sani MERT

Course Objectives

The aim of this course is to enable doctoral students to advance their dissertation studies by conducting data collection, analysis, and interpretation processes in accordance with scientific standards. The course is designed to enhance students’ ability to integrate theoretical frameworks with empirical research, analyze research findings, and generate academic contributions.Additionally, the course aims to ensure that students make systematic progress toward completing their doctoral dissertations and are able to present their work at the level of academic publication quality.

Course Content

This course focuses on the implementation and completion stages of the doctoral dissertation process. The content includes conducting data collection processes, performing quantitative and/or qualitative data analysis, interpreting findings, presenting theoretical and practical contributions, advancing the dissertation writing process, and preparing academic publications.Within the scope of the course, students develop their dissertations under the supervision of their advisors, present their findings in an academic format, and refine their work based on continuous feedback.

Prerequisites / Corequisites

This course is designed for doctoral students who have advanced to the dissertation stage and whose dissertation proposals have been approved. Students are expected to have sufficient knowledge of research methods and statistical analysis.

Course Books / Materials / Recommended Resources

Main References:Creswell, J. W. – Research DesignHair, J. F. et al. – Multivariate Data AnalysisRecommended Readings:Recent academic journal articles in the fieldInternational peer-reviewed journalsResources on academic writing and publication processesThese materials are intended to support students in effectively conducting scientific research and producing academic contributions.

Learning Outcomes

Code	Description
LO1	Students apply appropriate data collection and analysis methods in accordance with scientific research standards.
LO2	Students analyze and interpret research findings by linking them to the theoretical framework.
LO3	Students develop and evaluate theoretical and practical contributions derived from their research.
LO4	tudents prepare their doctoral dissertations in accordance with academic writing standards and present them in scholarly settings.

Weekly Course Content

Week	Content
1	Evaluation of current progress and development of a work plan.
2	Clarification of data collection methods and procedures.
3	Execution of fieldwork and data collection.
4	Data cleaning and preliminary analysis techniques.
5	Application of statistical analysis methods.
6	Use of qualitative techniques (coding, thematic analysis).
7	Initial presentation of research findings.
8	Evaluation of dissertation progress and feedback.
9	Linking findings to the theoretical framework.
10	Structuring theoretical and practical contributions.
11	Writing findings and discussion sections.
12	Improving coherence and academic writing.
13	Article writing and journal selection processes.
14	Comprehensive presentation of dissertation findings.
15	Overall evaluation and feedback for completion.

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	1	0.00	0.00
Practice	1	40.00	40.00
Laboratory	1	0.00	0.00
Pre-Class Individual Study	1	0.00	0.00
Post-Class Individual Study	1	0.00	0.00
Post-Prectice Individual Study	1	20.00	20.00
Midterm Exam/Preparation	1	0.00	0.00
Final Exam/Preparation	1	0.00	0.00
Quiz Preparation	1	0.00	0.00
Homework	1	0.00	0.00
Research Presentation	5	10.00	50.00
Seminar	1	0.00	0.00
Field Study	1	40.00	40.00
Workshop	1	0.00	0.00
Total Workload (Hours)			150
ECTS Credit (Workload / 25)			5

Assessment

#	Assessment Type	Contribution (%)
1	Practice	%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	On-site learning, supervised clinical practice (hands-on training)	Performing clinical procedures under supervision, patient management, and clinical decision-making	Clinical environment, dental units, clinical instruments and materials
2	Review / Survey Work	Research – lifelong learning, writing, reading

Academic Integrity and Artificial Intelligence

Full compliance with academic integrity principles is required. All academic work must be original, and all sources must be properly cited.Artificial intelligence tools may be used only for supportive purposes (e.g., literature review, data analysis support, writing suggestions). The direct use of AI-generated content without proper acknowledgment is considered a violation of academic ethics. Students are expected to clearly disclose the use of AI tools and assume full academic responsibility for their work.

Sustainable Development Goals