Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Slides:

Advertisements

Similar presentations

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Advertisements

Class size: any Time frame: 20 or more minutes Setting: moveable seating required, a lot of space preferable Purpose: introduce students to many of their.

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.

Welcome to Algebra for All Year 2 October 29, 2010.

Problem-based Learning An Introduction. What is PBL? –“the most significant innovation in education for the professions in many years” –now very common.

The Classroom Presenter Project Richard Anderson University of Washington December 5, 2006.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Formal Cooperative Learning: Text Comprehension/Interpretation Karl A. Smith Laura Apol.

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.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

1 Pedagogical implications of mobile technologies Diana Laurillard WLE Symposium on M-Learning 9 February 2007.

Meta-Cognition, Motivation, and Affect PSY504 Spring term, 2011 January 13, 2010.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Charlestown Science Fair Where Can I Get My Research Project Idea? Observe the world around you. Libraries Books 4 th and 5 th grade hallway.

1 Problem Solving using computers Data.. Representation & storage Representation of Numeric data The Binary System.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 April 2, 2012.

The Why, What, When, How, and How- to of Book Clubs

Mean and Standard Deviation of Grouped Data Make a frequency table Compute the midpoint (x) for each class. Count the number of entries in each class (f).

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.

Special Topics in Educational Data Mining HUDK5199 Spring 2013 March 25, 2012.

English III Friday Bellringer: Work on your daily journal writing Entry 1 = Chapter 1 Entry 2 = Chapter 2 Fishbowl Discussion next Common Core Standards.

Change Academy 2011 Liquid Café. Preparation for the Liquid Cafe Identify one practical challenge or burning question that will move your initiative forward.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 March 16, 2012.

Ngram models and the Sparcity problem. The task Find a probability distribution for the current word in a text (utterance, etc.), given what the last.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Formal Cooperative Learning: Text Comprehension/Interpretation Karl A. Smith Laura Apol.

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 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

 Together, my Co-Teaching partner and I have 32 first grade students. All of the students listed below are either 6 or 7 years old.  One child has.

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 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

Core Methods in Educational Data Mining HUDK4050 Fall 2015.

CAP6135: Malware and Software Vulnerability Analysis Paper Presentation and Summary Cliff Zou Spring 2013.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

Core Methods in Educational Data Mining HUDK4050 Fall 2014.

CAP6135: Malware and Software Vulnerability Analysis Paper Presentation and Summary Cliff Zou Spring 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.

Team Contracts We can work together! Copyright © Texas Education Agency, All rights reserved. 1.

TagHelper Track Overview Carolyn Penstein Rosé Carnegie Mellon University Language Technologies Institute & Human-Computer Interaction Institute School.

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Core Methods in Educational Data Mining

Characteristics of a Successful Student

CAP6135: Malware and Software Vulnerability Analysis Paper Presentation and Summary Cliff Zou Spring 2012.

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

Selling Cells Web Quest

Core Methods in Educational Data Mining

OCTOBER 29, 2014 English 3690.

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

Support problem in RapidMiner “Just one little idea on how Rapidminer calculate the support... For Q7, I assumed the max gap as 5.0, and got 53 students who had the sequential rule behavior-ontask - > affect-concentrating at least once. Then I had this number divided by 55, the total number of students, and the result turns out to be , which is almost the same with the support for behavior-ontask - > affect-concentrating in Rapidminer, So I was wondering if Rapidminer, for the GSP operator, would calculate the support on student-level instead of each transaction?”

Implications of student-level calculation of support Weights students equally Treats a behavior that every student engages in once the same as a behavior that every student engages in half the time Advantages? Disadvantages?

Other thoughts or comments?

Assignment C4 What are some interesting rules that people found?

Last thoughts or comments on sequential pattern mining

Text Mining

Prata work

What are the advantages and disadvantages Of looking for counts of words – Also called “bag of words” Of looking for bigrams Of looking for trigrams Of looking for grammatical structures

Semantic Tagging What is it?

WMatrix categories ons/phd2003.pdf ons/phd2003.pdf At the end

What are the advantages and disadvantages Of semantic tagging versus looking for specific words

Questions/Comments?

What measures (of student responses) did Graesser et al. paper use?

Learner Verbosity LSA-based comparison to “good” and “bad” answers Change in degree of goodness of answer

Questions/Comments About Graesser et al. paper?

Assignment C5 Visualization Questions? Bring printouts of your visualization to class

Assignment C6 Final project Questions?

Next Class Monday, December 15: Visualization HM140 Readings Baker, R.S. (2014) Big Data and Education. Ch. 6, V1, V2, V3, V4, V5. Kay, J., Maisonneuve, N., Yacef, K., Reimann, P. (2006) The big five and visualisations of team work activity. Intelligent Tutoring Systems: Proceedings 8th International Conference, ITS 2006, Ritter, S., Harris, T., Nixon, T., Dickinson, D., Murray, R.C., Towle, B. (2009) Reducing the Knowledge Tracing Space. Proceedings of the 2nd International Conference on Educational Data Mining, Martinez, R., Kay, J., Yacef, K. (2011) Visualisations for longitudinal participation, contribution and progress of a collaborative task at the tabletop. International Conference on Computer Supported Collaborative Learning, CSCL 2011,

Also Final Project Presentation Session #1 Monday, 6pm-9pm GD547 Everyone knows what session they are presenting in, right?

The End