COURSE UNIT TITLE

: BIG DATA AND SMART LEARNING ENVIRONMENTS IN EDUCATION

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MBD 6000 BIG DATA AND SMART LEARNING ENVIRONMENTS 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
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 aim of this course is to introduce students to the fundamental concepts of big data and their applications in education, while also highlighting the importance of data analytics and personalized learning. Additionally, it seeks to enable students to evaluate the integration of big data and intelligent systems in education and analyze the impact of these technologies, fostering a conscious approach to ethical issues

Learning Outcomes of the Course Unit

1   Define the basic concepts of big data and their applications in education and understand the importance of data analytics and personalized learning processes
2   Assess the integration of big data and smart systems in education and analyze how the technologies contribute to education and training processes
3   Discuss ethical issues related to the use of big data and smart systems, develop a data security and privacy conscious approach

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic concepts of big data
2 Using data analytics in education
3 Big data for personalized learning
4 Big data in assessing learning outcomes
5 Fundamentals of intelligent systems
6 Internet of things
7 Digital twin
8 Cobots
9 Midterm exam
10 Edge computing
11 Blockchain
12 Integration of big data and intelligent systems in education
13 Ethical issues in using big data and intelligent systems
14 Collaborative project presentations and feedback
15 Collaborative project presentations and feedback

Recomended or Required Reading

Darshan Singh, A., Raghunathan, S., Robeck, E., & Sharma, B. (Eds.). (2018). Cases on smart learning environments. IGI Global.
Chang, M., & Li, Y. (Eds.). (2015). Smart learning environments. Springer Berlin Heidelberg.

Planned Learning Activities and Teaching Methods

Discussion, collaborative 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

Midterm Exam, Project report 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
Deparment of Primary Education
atmanuslu@gmail.com
nilufer.atmanuslu@deu.edu.tr

Office Hours

Tuesday 13:00-13:40

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 5 5
Group homework preperation 13 4 52
Preparing presentations 1 5 5
Midterm 1 2 2
TOTAL WORKLOAD (hours) 103

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15PO.16
LO.1222
LO.2332
LO.3532