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 | 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 objective of this course is to enable the understanding of learning processes in educational environments through the collection, analysis, and interpretation of data produced within these environments. The course aims to provide students with knowledge about the basic concepts, methods, and applications of educational data mining and learning analytics, and to equip them with the skills to evaluate, improve, and personalize learning processes using these techniques. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Can explain the basic concepts in educational data mining and learning analytics and discuss how these techniques are used in education. |
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-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. |
Examination Term Paper |
| LO-2 | Can analyze training data using different data mining methods (classification, clustering, regression, etc.) and make data-based decisions to improve learning processes. |
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-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. |
Examination Term Paper |
| LO-3 | Using learning analytics, you can assess student performance, develop personalized learning strategies, and predict student success. |
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-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. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Introduction and Basic Concepts, Educational Data Mining Methods, Learning Analytics and Applications, Data-Driven Decision Making in Education, Educational Technologies and Tools, The Future of Learning Analytics, Ethics and Security, Applied Studies and Case Analyses | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction and Basic Concepts | Lecture, Question and Answer, Discussion |
| 2 | Educational Data and Data Sources | Lecture, Question and Answer, Discussion |
| 3 | Data Cleaning and Preparation | Lecture, Question and Answer, Discussion, Individual Study Method |
| 4 | Data Mining Methods: Fundamentals | Lecture, Question and Answer, Discussion, Individual Study Method |
| 5 | Classification Methods | Lecture, Question and Answer, Discussion, Individual Study Method |
| 6 | Clustering and Regression Methods | Lecture, Question and Answer, Discussion, Individual Study Method |
| 7 | Definition and Types of Learning Analytics | Lecture, Question and Answer, Discussion, Individual Study Method |
| 8 | mid-term exam | |
| 9 | Predicting Student Success | Lecture, Question and Answer, Discussion, Individual Study Method |
| 10 | Data-Driven Decision Making in Education | Lecture, Question and Answer, Discussion, Individual Study Method |
| 11 | Educational Technologies and Tools | Lecture, Question and Answer, Discussion, Individual Study Method |
| 12 | Data-Driven Learning Strategies | Lecture, Question and Answer, Discussion, Individual Study Method |
| 13 | Ethics and Security | Lecture, Question and Answer, Discussion, Individual Study Method |
| 14 | The Future of Learning Analytics | Lecture, Question and Answer, Discussion, Individual Study Method |
| 15 | Final Project Presentations and Evaluation | Lecture, Question and Answer, Discussion, Individual Study Method |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Güyer, T., Yurdugül, H., Yıldırım, S. (2020). Eğitsel Veri Madenciliği ve Öğrenme Analitikleri. Ankara: Anı Yayıncılık. | |
| 2 | The Handbook of Learning Analytics. (https://www.solaresearch.org/publications/hla-22/) | |
| Required Course instruments and materials | ||
| Textbooks, 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 | ||