Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | EğITIM TEKNOLOJILERI TEZLI YüKSEK LISANS | ||||
| 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 enable students to use artificial intelligence and machine learning methods in related problems in the field of educational sciences by providing students with the basic knowledge and skills related to the application area of artificial intelligence. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Can explain the basic concepts of artificial intelligence and machine learning. |
PO-1 Has theoretical and applied knowledge about educational technologies. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 Uses the knowledge gained by following national and international research and innovations in educational technologies in his/her professional and academic life with her theoretical and applied studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to the teaching profession and educational technologies and existing resources effectively. PO-9 Integrates knowledge and skills in educational technologies with different disciplines. |
Examination |
| LO-2 | Knows the approaches and methods used in machine learning. |
PO-1 Has theoretical and applied knowledge about educational technologies. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 Uses the knowledge gained by following national and international research and innovations in educational technologies in his/her professional and academic life with her theoretical and applied studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to the teaching profession and educational technologies and existing resources effectively. PO-9 Integrates knowledge and skills in educational technologies with different disciplines. |
Examination |
| LO-3 | Can apply machine learning methods through a software. |
PO-1 Has theoretical and applied knowledge about educational technologies. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 Uses the knowledge gained by following national and international research and innovations in educational technologies in his/her professional and academic life with her theoretical and applied studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to the teaching profession and educational technologies and existing resources effectively. PO-9 Integrates knowledge and skills in educational technologies with different disciplines. |
Examination Term Paper |
| LO-4 | Can interpret patterns in big data by revealing them with machine learning techniques. |
PO-1 Has theoretical and applied knowledge about educational technologies. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 Uses the knowledge gained by following national and international research and innovations in educational technologies in his/her professional and academic life with her theoretical and applied studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to the teaching profession and educational technologies and existing resources effectively. PO-9 Integrates knowledge and skills in educational technologies with different disciplines. |
Examination Term Paper |
| LO-5 | Can develop machine learning application for solving a problem in the educational sciences. |
PO-1 Has theoretical and applied knowledge about educational technologies. PO-2 Designs creative, original and innovative technology-supported learning environments to enhance learning. PO-3 Uses the knowledge gained by following national and international research and innovations in educational technologies in his/her professional and academic life with her theoretical and applied studies. PO-4 Designs learning-teaching environments suitable for individual differences by using methods and techniques related to the teaching profession and educational technologies and existing resources effectively. PO-9 Integrates knowledge and skills in educational technologies with different disciplines. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| This course includes educational data mining, machine learning, artificial intelligence, libraries that can be used in machine learning, machine learning methods, supervised learning, unsupervised learning, semi-supervised learning, machine learning algorithms, artificial intelligence studies at national and international level in the educational context and a sample research. It includes topics about how artificial intelligence algorithms can be used within the framework of the problem. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Fundamentals of artificial intelligence and machine learning | Lecture, Question and Answer, Discussion |
| 2 | Approaches and methods used in machine learning | Lecture, Question and Answer, Discussion |
| 3 | Software to be used in machine learning and python programming language | Lecture, Question and Answer, Discussion, Individual Study Method |
| 4 | Python programming applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 5 | Supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 6 | Supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 7 | Supervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 8 | mid-term exam | |
| 9 | Unsupervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 10 | Unsupervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 11 | Unsupervised learning applications | Lecture, Question and Answer, Discussion, Individual Study Method |
| 12 | Semi-supervised learning application | Lecture, Question and Answer, Discussion, Individual Study Method |
| 13 | Semi-supervised learning application | Lecture, Question and Answer, Discussion, Individual Study Method |
| 14 | Machine learning applications in solving problems in the educational sciences | Lecture, Question and Answer, Discussion, Individual Study Method |
| 15 | Machine learning applications in solving problems in the educational sciences | Lecture, Question and Answer, Discussion, Individual Study Method |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Aydoğdu, Ş. (2020). Algoritma ve Programlama. Pegem Akademi: Ankara. | |
| 2 | Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.". | |
| 3 | Alpaydın, E. (2013). Yapay öğrenme. Boğaziçi Üniversitesi Yayınevi. | |
| 4 | Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University. | |
| 5 | Güyer, T., Yurdugül, H., Yıldırım, S. (2020). Eğitsel Veri Madenciliği ve Öğretnme Analitikleri. Anı Yayıncılık: Ankara. | |
| Required Course instruments and materials | ||
| textbook, laptop | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 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 | 4 | 10 | 40 |
| b) Search in internet/Library | 0 | ||
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 4 | 11 | 44 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 4 | 7 | 28 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 4 | 7 | 28 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 184 | ||