| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CET4112 | Artificial Intelligence Practices in Education | Fall | 2 | 0 | 2 | 4 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi YAVUZ SAMUR |
| Course Objectives: | The purpose of this course is to introduce the fundamentals and types of artificial intelligent, expert systems, machine learning, big educational data, learning analytics, designing of adaptive learning systems, designing of adaptive testing systems, designing of recommender systems, designing of decision-support systems. |
|
The students who have succeeded in this course; Describes relation between intelligence and artificial intelligence Describes components of artificial intelligence Defining data sources of artificial intelligence Designing a expert system in education Developing algorithms of artificial intelligence Apply the methods of machine learning Describes educational big data Designing learning analytics |
| Intelligence and its properties, basics of artificial intelligence (AI), history of AI; current status of AI and using of AI in education; expert systems (ES); using ES in education, component of ES, designing of ES, recommender systems, decision-support systems, intelligent tutoring systems; educational big data; learning analytics, pedagogical agents; adaptive learning systems, adaptive testing systems. |
| Week | Subject | Related Preparation | |
| 1) | Intelligence and artificial intelligence: Basic concepts | ||
| 2) | History of artificial intelligence | ||
| 3) | Components, and types of artificial intelligence | ||
| 4) | Expert systems | ||
| 5) | Machine learning | ||
| 6) | Algorithm of artificial intelligence (classification) | ||
| 7) | Algorithm of artificial intelligence (prediction) | ||
| 8) | Midterm | ||
| 9) | Algorithm of artificial intelligence (description) | ||
| 10) | Recommender, and decision-support systems | ||
| 11) | Educational big data | ||
| 12) | Learning analytics | ||
| 13) | Applications of pedagogical agents | ||
| 14) | Adaptive learning systems | ||
| Course Notes: | Ders Kitapları: ElAtia, S., & Ipperciel, D. (Eds.). (2016). Data mining and learning analytics: applications in educational research. John Wiley & Sons. Forbus, K. D., & Feltovich, P. J. (2001). Smart machines in education: the coming revolution in educational technology. MIT Press. Looi, C. K., McCalla, G., & Bredeweg, B. (Eds.). (2005). Artificial intelligence in education: supporting learning through intelligent and socially informed technology (Vol. 125). IOS Press. Montebello, M. (2018). AI Injected e-Learning. Springer. |
| References: | YOK |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 14 | % 5 |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 4 | % 10 |
| Presentation | % 0 | |
| Project | 2 | % 15 |
| Seminar | % 0 | |
| Midterms | 1 | % 20 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 50 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 35 | |
| PERCENTAGE OF FINAL WORK | % 65 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 2 | 28 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 0 | 0 | 0 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 2 | 15 | 30 |
| Homework Assignments | 4 | 2 | 8 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 1 | 10 | 10 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 25 | 25 |
| Total Workload | 101 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |