Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Slides:

Advertisements

Similar presentations

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Advertisements

CSE 5522: Survey of Artificial Intelligence II: Advanced Techniques Instructor: Alan Ritter TA: Fan Yang.

Administrative This was the last homework for the semester (yay!) First run of games: October 16 nd in class –Short document describing: goal + operational.

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

Data Synchronization and Grain-Sizes Week 3 Video 2.

Ground Truth for Behavior Detection Week 3 Video 1.

HFIP Regional Ensemble Call Audio = Passcode = # 16 September UTC.

CSE 590ST Statistical Methods in Computer Science Instructor: Pedro Domingos.

CIS 410/510 Probabilistic Methods for Artificial Intelligence Instructor: Daniel Lowd.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

CSE 515 Statistical Methods in Computer Science Instructor: Pedro Domingos.

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

Classifiers, Part 3 Week 1, Video 5 Classification  There is something you want to predict (“the label”)  The thing you want to predict is categorical.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Diagnostic Metrics, Part 1 Week 2 Video 2. Different Methods, Different Measures  Today we’ll focus on metrics for classifiers  Later this week we’ll.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Case Study – San Pedro Week 1, Video 6. Case Study of Classification  San Pedro, M.O.Z., Baker, R.S.J.d., Bowers, A.J., Heffernan, N.T. (2013) Predicting.

Math Practice #2 Reason Abstractly and Quantitatively.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Anomaly detection with Bayesian networks Website: John Sandiford.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 February 13, 2012.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

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

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Artificial Intelligence Programming in Prolog Lecture 1: An Introduction 23/09/04.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

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

Learning Analytics: Process & Theory March 24, 2014.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

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

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.

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.

1 Intro to Artificial Intelligence COURSE # CSC384H1F Fall 2008 Sonya Allin Note: many slides drawn from/inspired by Andrew Moore’s lectures at CMU and.

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.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

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 February 1, 2012.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Special Topics in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

CSE 515 Statistical Methods in Computer Science

Core Methods in Educational Data Mining

Learning Analytics: Process & Theory

Algebra 2 Ch.3 Notes Page 15 P Solving Systems Algebraically.

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Presentation transcript:

Core Methods in Educational Data Mining HUDK4050 Fall 2014

The Homework Let’s go over the homework

Was it harder or easier than basic homework 1?

Let’s go over some of the solutions you handed in…. I will call on a small number of you

Let’s go over some of the solutions you handed in…. If I call on you, please come up and discuss – What you did If you’re not the first person I call, please focus on how your solution differed from previous students – How well it worked If you’re in the audience, please ask questions – But be nice…

Anyone else? Does anyone else in the audience have – Something clever they did and want to share? – Something clever they didn’t do but want to discuss? – A concern about how to do this right?

What mattered? What could you do to get better model performance? (Without cheating)

Questions? Comments? Concerns?

Textbook/Readings

What is a behavior detector?

What are some of the methods for collecting ground truth for complex behavior?

What are their advantages and disadvantages?

What are some indicators of ground truth for student success?

What are their advantages and disadvantages?

Thoughts on the San Pedro et al. case study?

Thoughts on the Sao Pedro et al. paper?

Thoughts on the Baker et al. paper?

Grain-sizes Which grain-size(s) were the detection focus for each paper/case study?

Grain-sizes What are the advantages and disadvantages of working at these different grain-size(s)? – Student-level – Action-level – Observation-level – Problem/Activity-level – Day/Session-level – Lesson-level

Why… Should we not expect (or want) Detectors with Kappa = 0.75 For models built with training labels with inter-rater reliability Kappa = 0.62?

Other questions, comments, concerns about lectures?

Basic HW 2

Take a couple of models Apply some standard metrics for them

Questions about Basic HW 2?

Questions? Concerns?

Other questions or comments?

Next Class Wednesday, September 24 Diagnostic Metrics Baker, R.S. (2014) Big Data and Education. Ch. 2, V1, V2, V3, V4. Fogarty, J., Baker, R., Hudson, S. (2005) Case Studies in the use of ROC Curve Analysis for Sensor-Based Estimates in Human Computer Interaction. Proceedings of Graphics Interface (GI 2005), Russell, S., Norvig, P. (2010) Artificial Intelligence: A Modern Approach. Ch. 20: Learning Probabilistic Models. Basic HW 2 due

Special Session Friday

The End