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 and develops an attitude to use this knowledge responsibly. PO-2 Designs and develops creative, original and innovative technology-supported learning environments to enhance learning. PO-3 He/She effectively uses the knowledge she has gained by following national and international research and innovations in artificial intelligence technologies in education in her professional and academic life through her theoretical and practical studies. PO-4 Designs learning-teaching environments appropriate for individual differences by effectively using existing resources and methods and techniques related to the teaching profession and artificial intelligence technologies in education, and acts with ethical sensitivity in these environments. PO-9 He/she integrates different disciplines with artificial intelligence technologies in education and exhibits an open attitude towards collaboration. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively, consciously and responsibly in learning and teaching environments. PO-12 Uses machine learning, deep learning and big data analytics methods effectively and practically 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 and develops an attitude to use this knowledge responsibly. PO-2 Designs and develops creative, original and innovative technology-supported learning environments to enhance learning. PO-3 He/She effectively uses the knowledge she has gained by following national and international research and innovations in artificial intelligence technologies in education in her professional and academic life through her theoretical and practical studies. PO-4 Designs learning-teaching environments appropriate for individual differences by effectively using existing resources and methods and techniques related to the teaching profession and artificial intelligence technologies in education, and acts with ethical sensitivity in these environments. PO-9 He/she integrates different disciplines with artificial intelligence technologies in education and exhibits an open attitude towards collaboration. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively, consciously and responsibly in learning and teaching environments. PO-12 Uses machine learning, deep learning and big data analytics methods effectively and practically 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 and develops an attitude to use this knowledge responsibly. PO-2 Designs and develops creative, original and innovative technology-supported learning environments to enhance learning. PO-3 He/She effectively uses the knowledge she has gained by following national and international research and innovations in artificial intelligence technologies in education in her professional and academic life through her theoretical and practical studies. PO-4 Designs learning-teaching environments appropriate for individual differences by effectively using existing resources and methods and techniques related to the teaching profession and artificial intelligence technologies in education, and acts with ethical sensitivity in these environments. PO-9 He/she integrates different disciplines with artificial intelligence technologies in education and exhibits an open attitude towards collaboration. PO-10 Design and develop artificial intelligence based applications in educational environments. PO-11 Uses artificial intelligence technologies effectively, consciously and responsibly in learning and teaching environments. PO-12 Uses machine learning, deep learning and big data analytics methods effectively and practically 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 to the Course: Concepts of Personalized Instruction and AI | Lecture, Discussion, Q&A |
| 2 | Learning Data, Student Models, and Profiling | Lecture, Discussion, Individual Study |
| 3 | Personalized Learning Paths and Example Scenarios | Discussion, Group Work, Practice |
| 4 | Adaptive Learning Systems and Decision-Making Processes | Lecture, Practice, Discussion |
| 5 | Basics of Machine Learning and Its Use in Education | Lecture, Q&A, Hands-on Practice |
| 6 | Predicting Student Performance: Machine Learning Applications | Project-Based Learning, Individual Study |
| 7 | Introduction to Deep Learning: Automating Learning Content | Lecture, Discussion, Interactive Practice |
| 8 | mid-term exam | |
| 9 | Personalized Content Creation in Education: Practical Examples | Practice, Group Work |
| 10 | AI-Powered Learning Assistants: Introduction and Analysis | Lecture, Q&A, Case Study |
| 11 | Designing Applications Based on Personalized Assistants | Project Development, Feedback |
| 12 | Automated Feedback Systems and Evaluation Models | Lecture, Practice, Individual Study |
| 13 | Real-World Applications: Case Analyses | Presentation, Discussion, Critical Evaluation |
| 14 | Future of AI in Education: Ethics, Data Security and Trends | Lecture, Discussion, Video Analysis |
| 15 | Term Project Presentations and Overall Evaluation | Presentation, Peer Review, Feedback |
| 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 | ||