ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
EDT5008 | Advanced Instructional Design | Spring | 3 | 0 | 3 | 12 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. TUFAN ADIGÜZEL |
Recommended Optional Program Components: | None |
Course Objectives: | The overall course objectives are to: -Identify factors that must be incorporated into instructional design processes and products to be consistent with various learning theories (such as behaviorism, Gagne’s theory of instruction, constructivism, motivational theory…etc.) -Analyze a design problem based on various theories. -Analyze instructional materials to identify characteristics representative of particular theories. -Apply the Rapid-prototyping strategy. |
The students who have succeeded in this course; 1. to be able to discuss basic assumptions, concepts, and principles of different paradigms of learning, including foundational theories, behavioral psychology, cognitive information processing, developmental theories, motivational theory, and theories of instruction. 2. to be able to compare and contrast theories within and across paradigms for strengths, weaknesses, and applicability 3. to be able to determine the implications of theory for instructional design 4. to be able to formulate and revise personal theories of learning and determine implications 5. to be able to articulate changes in personal epistemology over the course 6. to be able to analyze a design problem based on various theories 7. to be able to identify factors that must be incorporated into instructional design processes and products to be consistent with selected theory 8. to be able to analyze current instructional design model to determine which models are most consistent with which theories. 9. to be able to use rapid-prototyping as a method in instructional design |
Bu ders öğretimsel tasarımda temel öğrenme teorilerinin (Davranışçı yaklaşım, sistem teorisi, iletişim teorisi, öğrenme teorileri, & öğretim teorileri) uygulamalı olarak teknoloji temelli öğrenme materyallerinde incelenmesini ve kullanılmasını amaçlamaktadır. |
Week | Subject | Related Preparation |
1) | Introduction to course and overview | |
2) | Introduction to the learning theories | |
3) | Gagne’s Nine Event of Instruction & Davranışçı Yaklaşım | |
4) | Presentations on Behaviorism | |
5) | Cognitive Information Processing | |
6) | Presentations on Cognitive Information Processing | |
7) | Meaningful Learning & Schema Theory | |
8) | Presentations on Meaningful Learning & Schema Theory | |
9) | Constructivism | |
10) | Presentations on Constructivism | |
11) | Rapid prototyping | |
12) | Presentations on Rapid Prototyping | |
13) | Motivational Theory | |
14) | Presentations on Motivational Theory |
Course Notes / Textbooks: | Driscoll, M. P. (2004). Psychology of Learning for Instruction. 3rd Edition. Boston: Allyn & Bacon. Ertmer & Quinn. (2007). The ID Casebook: Case Studies in Instructional Design. 3rd ed/ Pearson. |
References: | - |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 14 | % 10 |
Homework Assignments | 2 | % 20 |
Presentation | 6 | % 30 |
Project | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Presentations / Seminar | 6 | 10 | 60 |
Project | 1 | 60 | 60 |
Homework Assignments | 1 | 30 | 30 |
Total Workload | 192 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | |
2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | |
3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | |
4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | |
5) | Select and use modern techniques and tools necessary for engineering applications. | |
6) | Design and conduct experiments, collect data, and analyse and interpret results. | |
7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
8) | Access information via conducting literature research, using databases and other resources | |
9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
13) | Have a sense of professional and ethical responsibility. | |
14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |