GMS 2002 - Kitchen Practices II - Course Information

Basic Information

Course Code:

GMS 2002

Course Name:

Kitchen Practices II

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

5.00

Instructor:

Doç. Dr. OĞUZ DOĞAN

Course Objectives

Students learn selection of adequate cooking technique, food preparation, effective recipe usage and mise-en-place in respect to timing throughout this course. Kitchen practices about stocks, bakery and basic cooking techniques are performed. The course provides information about how attentive and delicious food preparation and serving to real customers is conducted. The course covers the preparation and presentation of garde manger - hot cooking practices in details.

Course Content

Basic cooking and cutting techniques, vegetables, meat, poultry and fish preparation and basic stocks, making of soups and sauces.

Prerequisites / Corequisites

GMS 1005 Food Safety and Hygiene and GMS 1002 Introduction to Kitchen Practices

Course Books / Materials / Recommended Resources

None.

Learning Outcomes

Code	Description
LO1	Interpret how stocks are widely used in kitchen applications.
LO2	Explain how the preparation and cooking processes of food can be organized in terms of time.
LO3	Know dry food and can interpret usage patterns.
LO4	Can cook meat, poultry and fish products according to the desired cutting and cooking techniques.
LO5	Plan the preparation and presentation of hot and cold foods.

Weekly Course Content

Week	Content
1	Introduction
2	Pickling Techniques
3	Preparing and presenting olive oil dishes
4	Preparing and presenting methods for mezes
5	Salad
6	Salting and Curing methods
7	Midterm Exam
8	Hors D’oeuvres, Canapés
9	Sandwich Making Methods
10	Cold Savoury Mousses
11	Paté and Terrine Methods
12	Aspic Preparation
13	Cold Buffet Presentation
14	Presentation of Projects
15	End-of-Term Evaluation

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	15	4.00	60.00
Practice	15	4.00	60.00
Midterm Exam/Preparation	6	1.50	9.00
Final Exam/Preparation	14	1.50	21.00
Total Workload (Hours)			150
ECTS Credit (Workload / 25)			5

Assessment

#	Assessment Type	Contribution (%)
1	Midterm Exam	%40
2	Final Exam	%60
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11
PO-12
PO-13
PO-14

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Lecture (expository teaching), interactive discussion	Listening and taking notes.	Standard classroom technologies, multimedia tools (projector, computer, digital presentations)
2	Demonstration	Listening and comprehension, processing observations/situations	Presentation content
3	Group Work	Students determine the slope and cross-sectional geometric characteristics of a parabolic irrigation canal on campus, plot the flow rate curve at different sections of the canal, and submit a report.	Land surveying instruments
4	Laboratory	Listening, note-taking, conceptual analysis, critical thinking, questioning, and participation in discussions.	Special equipment

Academic Integrity and Artificial Intelligence

Academic integrity is a fundamental principle of this course. Students are expected to produce original work in all assignments and academic activities. Plagiarism, fabrication or falsification of data, the use of others’ ideas, thoughts, or texts without proper citation, and presenting a work while concealing the contribution of others are considered ethical violations. Within the scope of this course, artificial intelligence tools may be used as supportive resources in the learning process. Such use requires responsibility and transparency. Artificial intelligence may be utilized for idea development, clarification of concepts, language editing, or technical support. However, content generated by artificial intelligence may not be copied and presented as the student’s own academic work. Any use of artificial intelligence must be clearly acknowledged, and the final work must reflect the student’s own ideas, reasoning, academic knowledge, and effort. Failure to disclose the use of artificial intelligence or the detection of ethical violations will be evaluated in accordance with the relevant university regulations.

Sustainable Development Goals