ED 320

Course ID:

Course Code & Number

ED 320

Course Title

Exploratory Data Analysis for Learn. En.

Level

BS

Credit Hours/ ECTS Credits

(2+0+0) 2 TEDU Credits, 3 ECTS Credits

Year of Study:

Semester:

Type of Course:

Elective

Mode of Delivery:

Face-to-face

Language of Instruction:

English

Pre-requisite / Co-requisite:

Catalog Description

Use of Exploratory Data Analysis in educational setting. Hidden patterns in educational datasets. Background of Educational Data Mining and Learning Analytics approaches. Use of software for visualizing data. Data sources in online learning. Exploratory data analysis using various software packages.

Course Objectives

This course aims to introduce the Exploratory Data Analysis (EDA) approach which is known as a practical way to understand concepts, patterns, and useful information in large datasets and irregular datasets such as data gathered online learning environments. The content of the course is designed to provide a basic level theoretical background about data modelling approaches, Educational Data Mining, and Learning Analytics fields where the richness of data can be explored in many techniques. The goal of this course is help students to be competent at exploring, visualizing, and reporting educational data with a variety of software and tools by considering the theory lies behind EDA.  

Software Usage

Tableau, SPSS, RapidMiner

Course Learning Outcomes

1. discover hidden patterns and spot anomalies in irregular datasets,
2. explain the background of Educational Data Mining and Learning Analytics approaches,
3. choose the right data analysis approach for educational datasets,
4. demonstrate competent skills in data visualization techniques, 
5. perform data analysis using various software packages,
6. present educational data using graphical and numerical techniques.

Learning Activities and Teaching Methods:

Telling/Explaining Reading Problem Solving Collaborating Case Study/Scenarion Analysis Oral Presentations/Reports Guest Speakers Hands-on Activities

Assessment Methods and Criteria:

Quiz Case Studies / Homework Presentation (Oral/Poster) Performance Project (Written, Oral)

Assessment Methods and Criteria Others:

Design Content

Recommended Reading

Required Reading

Grading

Learning Activities and Teaching Methods Others:

Course Coordinator:

Dr. Hilal Seda Yıldız

Student Workload:

Workload	Hrs
Course Readings	10
Hands-on Work	28
Exams/Quizzes	4
Report on a Topic	4
Case Study Analysis	8
Oral Presentation	4

Course & Program Learning Outcome Matching: