I started to learn R at the HarvardX data science series at edX.

Here is the list of the courses

1. R Basics
2. Visualization
3. Probability
4. Inference and Modeling
5. Productivity Tools
6. Wrangling
7. Linear Regression
8. Machine Learning
9. Capstone

Each course provides the direct interaction in DataCamp.
I took the course as an audit member, which is free.
But, DataCamp gave me the certificate of the course accomplishment when I finished the drills of the course.
I finished the course 1, 2, 3, and 5. I will follow up the rest of the courses.

This is the extra document of Rafa's course : rafalab.github.io/dsbook/
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I delivered the conceptual change and complex system in the BEP541 class.
The powerpoint file was just made 3 minutes before starting a class.
It is really difficult for me to do the project-level assignment.
I knew that I need to start earlier than usual, but I am not motivated when the due is far from now.

We are running MATLAB session in a class

eegh - command to show which commands run in the EEGLAB GUI - so we can extract command lines from GUI interactions

I created the R scripts for the exercises in the textbook, which was additional goal in a class. 
I finally made it! 

here is the link : github.com/jkim205/BER540_Spring19

It will be updated as soon as possible after every class is delivered. 

One good thing is to get a chance to teach a R tutorial session. It will come true!

CSCL (Computer Supported Collaborate Learning)
- Issue - Digital Divide will fall behind. 
             - The role of computer in collaborate learning = replacement? or supplementary?

Collaborate Learning Perspectives
- as-a-window
- for-distal-outcomes
- for proximal-outcomes
- as-a-learning
What is the drawback of collaborate learning?
Is it applicable in a competitive environment?

Collaborate vs. Cooperative
- Work together, vs. split the work.
The drawback regarded as a drawback of collaborate learning is actually of the cooperative learning

This class is continous with Foundations of EdNeuro class of Fall semester.
The same people are gathered to focus on the methodologies of EEG/ERP, fNIRS, and fMRI in detail.
I am excited!

Introduction 
- It is important to have a hobby during ph.d life.

The interpretation of statistics is a goal to learn this semester!

I miss the quant I class. Actually I intended to take this course last semester, but misunderstanding of the course title makes me take another class BER600. It was really helpful to understand how the research goes on, but it was tough for a first-semester international student. 

During 15 weeks, I will learn about the learning science.
The textbook is "The Cambridge Handbook of the learning sciences" 2nd. edition.

One thing I made a mistake was to select the first edition of a textbook and did my first homework with this.
The chapter structure was totally different from the 2nd edition. So, I read the wrong parts of the book!
I just am unable to join the first class discussion, and I barely understand the first class, as well as the lecture speed was fast.

BEP 670 - Computational Methods of EdNeuro by Dr. Firat Soylu
BEP 541 - Learning and Cognition by Dr. Laura Morett
BER 540 - Quant I by Dr. Youn-jeng Choi
BEP 690 - Non-dissertation research
​BEP 600 - Reading in Ed Psych. (Brownbag)