COURSE UNIT TITLE

: DATA SCIENCE AND LEARNING ANALYTICS IN EDUCATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MBD 6002 DATA SCIENCE AND LEARNING ANALYTICS IN EDUCATION ELECTIVE 2 0 0 4

Offered By

Buca Faculty Of Education

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR NILÜFER ATMAN USLU

Offered to

Music Teacher Education
Turkish Language Teacher Education
Computer and Instructional Technologies Teacher Education
Chemistry Teacher Education
Biology Teacher Education
Turkish Language and Literature Teacher Education
Geography Teacher Education
Physics Teacher Education
Special Teacher Education
ELEMENTARY MATHEMATICS TEACHER EDUCATION
PRE - SCHOOL TEACHER EDUCATION
Mathematics Teacher Education
Elementary Teacher Education
FINE ARTS TEACHER EDUCATION
Guidance and Psychological Counseling
Social Studies Teacher Education
History Teacher Education
Science Teacher Education

Course Objective

The Data Science in Education and Learning Analytics course in education aims to develop an in-depth understanding of data analysis and student behavior to improve educational processes. This course aims to provide pre-service teachers with data-based decision-making skills and a perspective to improve learning experiences.

Learning Outcomes of the Course Unit

1   Understanding data science and its applications in education
2   Gaining skills in data collection, analysis and visualization
3   Developing an understanding of data security and ethical principles
4   Performing and presenting data analysis in real-life scenarios

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic concepts related to data science
2 Using data in education
3 Types of data and data collection methods
4 Types of data related to learning analytics
5 Basic statistical methods
6 Data visualization
7 Learning analytics tools and applications
8 Data security and ethical issues
9 Midterm exam
10 Monitoring and analyzing student achievement
11 Learning analytics in real-life scenarios
12 Data analysis and visualization with case studies
13 Data analysis and visualization with case studies
14 Project presentations and feedback
15 Project presentations and feedback

Recomended or Required Reading

Güyer, T., Yurdugül, H., & Yıldırım, I. S. (2020). Eğitsel veri madenciliği ve öğrenme analitikleri

Planned Learning Activities and Teaching Methods

Discussion, team-based work, lecture, collaborative learning, active learning, question-answer

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 VZ Midterm
2 FN Semester final exam
3 BNS BNS Student examVZ * 0.40 + Student examFN * 0.60
4 BUT Make-up note
5 BBN End of make-up grade Student examVZ * 0.40 + Student examBUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term, Assignment and Presentation

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc. Prof. Nilüfer ATMAN USLU
Dokuz Eylül University
Buca Faculty of Education
Deparment of Primary Education
atmanuslu@gmail.com
nilufer.atmanuslu@deu.edu.tr

Office Hours

Tuesday 11:00-11:40

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 13 1 13
Preparation for midterm exam 1 4 4
Preparing presentations 1 4 4
Preparing assignments 13 3 39
Midterm 1 2 2
TOTAL WORKLOAD (hours) 88

Contribution of Learning Outcomes to Programme Outcomes

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CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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