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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS ISÖ 5111 ARTIFICIAL INTELLIGENCE APPLICATIONS IN EDUCATION ELECTIVE 2 1 0 8

Offered By

Primary Teacher Education

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR NILÜFER ATMAN USLU

Offered to

Primary Teacher Education

Course Objective

Understanding and applying basic concepts about learning analytics and machine learning in education

Learning Outcomes of the Course Unit

1   Ability to understand the basic concepts of educational data mining and learning analytics 2   Ability to perform data pre-processing processes in educational data mining 3   Understand the basic concepts of basic machine learning algorithms in educational data mining 4   Ability to apply basic machine learning algorithms in educational data mining

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Educational Data Mining 2 Learning Analytics 3 Tools That Can Be Used in Learning Analytics and Educational Data Mining 4 Data Preprocessing in Educational Data Mining 5 Classification Methods-Naive Bayes 6 Classification Methods-KNN 7 Classification Methods-Decision Trees 8 Midterm 9 Classification Methods-Regression Analysis 10 Classification Methods-Logistic Regression Analysis 11 Clustering Analyzes - K-Means Clustering 12 Clustering Analyzes - Hierarchical Clustering 13 Learning Analytics and Educational Data Mining Sample Applications 14 Learning Analytics and Educational Data Mining Sample Applications 15 Final

Recomended or Required Reading

Güyer, T., Yurdugül, H., & Yıldırım, I. S. (2020). Eğitsel Veri Madenciliği Ve Öğrenme Analitikleri

Planned Learning Activities and Teaching Methods

In this course, the technologies used in learning analytics and educational data mining will then be discussed with the students.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTEG MIDTERM GRADE 2 FCG FINAL COURSE GRADE 3 FCG FINAL COURSE GRADE MTEG * 0.40 + FCG * 0.60 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

atmanuslu@gmail.com

Office Hours

Wednesday 10:00-12:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 7 3 21 Tutorials 7 3 21 Preparations before/after weekly lectures 14 5 70 Preparation for midterm exam 7 2 14 Preparation for final exam 7 2 14 Preparing assignments 14 2 28 Preparing presentations 14 2 28 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 PO.14 PO.15 PO.16 LO.1 5 4 5 5 5 5 4 4 5 5 5 LO.2 5 5 5 5 5 5 5 5 4 4 5 5 5 LO.3 5 5 4 5 5 5 5 5 5 5 4 4 5 5 5 5 LO.4 5 5 4 5 5 5 5 5 5 5 4 4 5 5 5 5