Educational Data Mining and Social Network Analysis

Course Code:

CET 492

Course Period:

Autumn

Course Type:

Area Elective

P:

3

Lab:

0

Credits:

3

ECTS:

5

Course Language:

İngilizce

Course Objectives:

This course will use social network analysis, both its theory and computational tools, to make sense of the social and information networks that have been fueled and rendered accessible by the internet. In this course you will learn about the structure and evolution of networks, drawing on knowledge from Educational data. Online interactive demonstrations and hands-on analysis of real-world data sets will focus on a range of tasks: from identifying important nodes in the network, to detecting communities, to tracing information diffusion and opinion formation.

Course Content:

Visualization in Educational Environments, Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments, A Data Repository for the EDM Community: The PSLC DataShop, Classifiers for Educational Data Mining, Clustering Educational Data, Association Rule Mining in Learning Management Systems

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Discussion, 4: Self Study, 5: Modeling

Course Evaluation Methods:

A: Testing, C: Homework, D: Portfolio, E: Project

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
1. Describes the features of the social networks and data mining	1,2,3,6	1,2,3	A,C
2. Students will able to create and explain social network analysis	1,2,3,6	2,4	C,D,E
3. Students will able to explain and apply educational data mining and visualization.	1,2,3,6	1,2,3,4	C,D,E
4. Students will able to describe classifiers for educational data mining	1,2,3,6	1,2,3,4	C,D,E
5. Students will able to understand Association Rule Mining in Learning Management Systems	1,2,3,6	1,2,3,4	C, D, E

Course Flow

Week	Topics	Study Materials
1	Data Mining- Educational Data Mining, Learning Analytics
2	Starting with Data, Data analytics tools
3	Basics of social network analysis, define and visualize
4	Sense making of social network analysis for the study of learning
5	Prediction Modeling
6	Feature engineering and behaviour detection
7	Midterm
8	Student presentations
9	Knowledge inference and knowledge structures
10	Relationship mining
11	Text Mining
12	Visualisation of Data
13	Clustring and factor Analysis
14	Evaluation of the Course

Recommended Sources

Textbook	Handbook of Educational Data Mining Cristóbal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan S.J.d. Baker, Taylor & Francis Group, Handbook of Data-Based Decision Making in Education, Theodore J. Kowalski and Thomas J. Lasley II, 2009 by Routledge.
Additional Resources

Material Sharing

Documents
Assignments	4 homework
Exams	1 mid-term, 1 final

Assessment

IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	1	30
Quizzes	-	-
Assignment	4	30
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

COURSE CATEGORY

Expertise/Field Courses

Course’s Contribution to Program

No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	Know instructional technologies and materials and how to implement these in courses; take role in applications such as educational software, e-learning, distance education and support other people around in need.					X
2	Use process of design, planning, implementation and managing educational technologies efficient; design and prepare needed products, changes and updates examining these processes.					X
3	Organize suitable technology supported education environments taking individual, social, cultural differences of students into consideration and special interest and needs.				X
4	Have efficient usage skill for information and communication technologies within and out instruction process.		X
5	Design and develop technology supported instructional materials to fulfill instruction needs.		X
6	Can define needed software and input-output devices for set up optimum computer system.			X
7	Can plan, implement and manage learning-teaching process.	X
8	Can develop and implement projects with knowing the processes of project development.		X
9	Have information about computer software and hardware in level of teaching computer and information and communication technologies courses and supporting other teachers; use informatics and communication technologies (European Computer Driving License, Advanced Level).		X
10	Have enough knowledge, skill and competence about teaching profession.
11	Can use current information communication technologies (software-hardware) and integrate them to learning-teaching processes.
12	Have high level knowledge and can put in force learning teaching approaches, theories, strategies, methods and technics.	X

ECTS

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	9	1	9
Mid-terms	1	3	3
Assignments	4	4	16
Final examination	1	40	40
Total Work Load			116
Total Work Load / 25 (h)			4,64
ECTS Credit of the Course			5

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