Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Spring Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | EĞİTİMDE YAPAY ZEKA TEKNOLOJİLERİ(TEZLİ YÜKSEK LİSANS ÖNERİLEN) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | ŞEYHMUS AYDOĞDU (saydogdu@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to teach students the artificial intelligence (AI) techniques used in the development of personalized learning methods. Students will acquire the knowledge and skills required to create learning experiences tailored to the needs of learners by utilizing AI algorithms and methods. The course will cover how AI can be applied in education to predict student performance, personalize learning pathways, and make data-driven decisions. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | By analyzing learning data, they can track student performance, create personalized learning paths, and develop student profiles. |
PO-1 Has theoretical and practical knowledge about artificial intelligence technologies in education. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 To be able to use the knowledge gained by following national and international researches and innovations in artificial intelligence technologies in education in professional and academic life with theoretical and practical studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to artificial intelligence technologies in teaching profession and education and existing resources effectively. PO-9 Integrates different disciplines with artificial intelligence technologies in education. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively and consciously in learning and teaching environments. PO-12 Effectively apply machine learning, deep learning and big data analytics methods in educational contexts. |
Examination Term Paper |
| LO-2 | Using machine learning techniques, they can make personalized predictions in education and develop intelligent education systems and adaptive learning methods. |
PO-1 Has theoretical and practical knowledge about artificial intelligence technologies in education. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 To be able to use the knowledge gained by following national and international researches and innovations in artificial intelligence technologies in education in professional and academic life with theoretical and practical studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to artificial intelligence technologies in teaching profession and education and existing resources effectively. PO-9 Integrates different disciplines with artificial intelligence technologies in education. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively and consciously in learning and teaching environments. PO-12 Effectively apply machine learning, deep learning and big data analytics methods in educational contexts. |
Examination Term Paper |
| LO-3 | They can design and implement artificial intelligence-powered personal learning assistants and create student-specific educational support systems. |
PO-1 Has theoretical and practical knowledge about artificial intelligence technologies in education. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 To be able to use the knowledge gained by following national and international researches and innovations in artificial intelligence technologies in education in professional and academic life with theoretical and practical studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to artificial intelligence technologies in teaching profession and education and existing resources effectively. PO-9 Integrates different disciplines with artificial intelligence technologies in education. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively and consciously in learning and teaching environments. PO-12 Effectively apply machine learning, deep learning and big data analytics methods in educational contexts. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Introduction and Personalized Learning, Fundamentals of Artificial Intelligence and Its Use in Education, Learning Data and Student Profiling, Personalized Learning Pathways, Intelligent Educational Systems and Adaptive Learning, Machine Learning and Personalized Predictions in Education, Artificial Intelligence and Personal Learning Assistants in Education, The Future of Personalized Education with Artificial Intelligence | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction and Individualized Instruction | Lecture, Question and Answer, Discussion |
| 2 | Training Data and Student Profiling | Lecture, Question and Answer, Discussion, Individual Study Method |
| 3 | Personalized Learning Paths | Lecture, Question and Answer, Discussion, Individual Study Method |
| 4 | Smart Education Systems and Adaptive Learning | Lecture, Question and Answer, Discussion, Individual Study Method |
| 5 | Machine Learning and Personalized Predictions in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 6 | Machine Learning and Personalized Predictions in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 7 | Deep Learning and Personalized Content in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 8 | mid-term exam | |
| 9 | Deep Learning and Personalized Content in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 10 | Artificial Intelligence and Personal Learning Assistants in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 11 | Artificial Intelligence and Personal Learning Assistants in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 12 | AI-Powered Automatic Evaluation and Feedback | Lecture, Question and Answer, Discussion, Individual Study Method |
| 13 | AI-Powered Automatic Evaluation and Feedback | Lecture, Question and Answer, Discussion, Individual Study Method |
| 14 | The Future of Personalized Education with Artificial Intelligence | Lecture, Question and Answer, Discussion, Individual Study Method |
| 15 | The Future of Personalized Education with Artificial Intelligence | Lecture, Question and Answer, Discussion, Individual Study Method |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Artificial Intelligence in Education: Promises and Implications for Teaching and Learning | |
| 2 | Personalized Learning: A Guide for Engaging Students with Technology | |
| 3 | Learning with Artificial Intelligence: What Teachers Need to Know | |
| Required Course instruments and materials | ||
| Textbook, Laptop | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 2 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | 16 | 1 | 30 |
| 9.Project | |||
| final exam | 16 | 1 | 30 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 3 | 14 | 42 |
| b) Search in internet/Library | 3 | 14 | 42 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 2 | 14 | 28 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 3 | 7 | 21 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 0 | ||
| final exam | 3 | 1 | 3 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||