Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Slides:

Advertisements

Similar presentations

Feature Engineering Studio November 11, Poster Session Features What features did each of you create after the poster session? Who did the ideas.

Advertisements

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Discovery with Models Week 8 Video 1. Discovery with Models: The Big Idea  A model of a phenomenon is developed  Via  Prediction  Clustering  Knowledge.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 April 11, 2012.

Course Introduction (Lecture #1) ENGR 107 – Intro to Engineering The slides included herein were taken from the materials accompanying Engineering Fundamentals.

Object-Oriented Enterprise Application Development Course Introduction.

Feature Engineering Studio February 23, Let’s start by discussing the HW.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 January 12, 2012.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Lecture 1 Page 1 CS 239, Fall 2010 Introduction CS 239 Advanced Topics in Computer Security Peter Reiher September 23, 2010.

Political Research and Statistics 8/26/2013. Readings Bring your cd's and a flash drive to class on Thursday Pollack Textbook – Introduction – Ch: 10.

Feature Engineering Studio September 23, Welcome to Mucking Around Day.

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 March 4, 2013.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Feature Engineering Studio September 9, Welcome to Problem Proposal Day Rules for Presenters Rules for the Rest of the Class.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 April 26, 2012.

Quantitative Methods in Geography Geography 391. Introductions and Questions What (and when) was the last math class you had? Have you had statistics.

Feature Engineering Studio September 23, Let’s start by discussing the HW.

MRKT 370 – PRINCIPLES OF MARKETING FALL 2003 Dr. Ugur Yucelt Office Phone: Class Time: MW: 12:30-1:45 Office hours: MW:2:00-6:00 pm

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Feature Engineering Studio October 14, Iterative Feature Refinement.

Learning Analytics: Process & Theory March 24, 2014.

Feature Engineering Studio March 1, Let’s start by discussing the HW.

Feature Engineering Studio September 30, Quick Note Please me for appointments rather than just showing up at my office – I’m always glad.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 February 27, 2013.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Feature Engineering Studio September 9, Welcome to Feature Engineering Studio Design studio-style course teaching how to distill and engineer features.

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 March 6, 2013.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Physics of Animation (Art/Physics 123) Prof. Alejandro Garcia Fall 2009 Class is fully enrolled and I am not allowed to add students. Sorry.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Feature Engineering Studio February 2, Welcome to Problem Proposal Day Rules for Presenters Rules for the Rest of the Class.

Feature Engineering Studio April 13, Friend Features Who managed to find a friend with relevant background expertise?

HFT 2401 Hospitality Financial Accounting Fall 2008 Course Overview.

Computer Networks CNT5106C

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Today is Monday, September 21, 2015 Third period books.  You will need your: ◦ Writing utensil ◦ Planner and ◦ Language Arts journal ◦ SpringBoard book.

NPQ Instructor II. Introduction  The NPQ Instructor II program is designed for individuals that are interested in the Fire and Emergency Services Training.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

MAT 279 Data Communication and the Internet Prof. Shamik Sengupta Office 4210 N Fall 2010.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Course Overview Stephen M. Thebaut, Ph.D. University of Florida Software Engineering.

Earth Educators’ Rendezvous Workshop Leader Webinar Introduction Workshop Design Best Practices Utilizing the Web Tools Evaluation Instruments David McConnell,

Open House September 17,  SJFC -Elementary Education and Special Education  Currently working on my MA in Teaching Foundations at Fairfield University.

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 February 25, 2013.

Computer Network Fundamentals CNT4007C

Computer Networks CNT5106C

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Big Data, Education, and Society

Core Methods in Educational Data Mining

Computer Networks CNT5106C

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Physics of Animation (Art/Physics 123)

Core Methods in Educational Data Mining

ELT & Vocabulary Learning

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Presentation transcript:

Core Methods in Educational Data Mining HUDK4050 Fall 2014

Demo of using Java

Activity

Second task Break into *different* 3-4 person groups than last time No overlap allowed

Second task Let’s take a quick look at homework C2

Second task Make up features for Assignment C2 You need to – Come up with a new feature – Justify how you can would it from the data set – Justify why it would work

I need a volunteer

Your task is to write down the features suggested And the counts for thumbs up/thumbs down

Now… Each group needs to read their favorite feature to the class and justify it Who thinks this feature will improve prediction of off-task behavior? Who doesn’t? Thumbs up, thumbs down!

Questions or comments?

Special Request Bring a print-out of your Assignment C2 solution to class on the day it’s due – Next Tuesday

Textbook

Automated Feature Generation What are the advantages of automated feature generation, as compared to feature engineering? What are the disadvantages?

Automated Feature Selection What are the advantages of automated feature selection, as compared to having a domain expert decide? (as in Sao Pedro paper from Monday) What are the disadvantages?

A connection to make

Correlation filtering Eliminating collinearity in statistics In this case, increasing interpretability and reducing over-fitting go together – At least to some positive degree

Outer-loop forward selection What are the advantages and disadvantages to doing this?

Knowledge Engineering What is knowledge engineering?

Knowledge Engineering What is the difference between knowledge engineering and EDM?

Knowledge Engineering What is the difference between good knowledge engineering and bad knowledge engineering?

Knowledge Engineering What is the difference between (good) knowledge engineering and EDM? What are the advantages and disadvantages of each?

How can they be integrated?

FCBF: What Variables will be kept? (Cutoff = 0.65) What variables emerge from this table? GHIJKL Predicted G H I J K.5.65 L.42

Other questions, comments, concerns about textbook?

If you enjoyed today’s class… Next fall, I’ll be offering a Feature Engineering Design Studio course… Learn the feature engineering process in detail Create a model important to your research Submit a journal paper

Special Session Thursday 9/24 3pm-430pm, Grace Dodge Hall 545 An Inappropriately Brief Introduction to Frequentist Statistics

What if you can’t attend? me; I will send you the slides

Should you attend? Not mandatory Not necessary if you’ve taken a stats class that covers topics like Z, F, and Chi-squared tests

Next Class Tuesday, September 29 Advanced Detector Evaluation and Validation Baker, R.S. (2015) Big Data and Education. Ch. 2, V5, V6. Rosenthal, R., Rosnow, R.L. (1991) Essentials of Behavioral Research: Methods and Data Analysis, 2nd edition. Ch. 22: Meta-Analysis. Rupp, A.A., Gushta, M., Mislevy, R.J., Shaffer, D.W. (2010) Evidence-Centered Design of Epistemic Games: Measurement Principles for Complex Learning Environments.The Journal of Technology, Learning, and Assessment, 8 (4), 4-47.

The End