Educational Data Mining/ Learning Analytics Organizer: Aybuke Gul Turker

Join us on LinkedIn http://bit.ly/1LE677b

New  York  City’s  own  data  science  community  conference  

October  2  2015  10am  –  6pm  Google,  NYC  (111  8th  Avenue)  

 Free  conference  

Visit  us  at  www.datapoint.nyc                    or                  Follow  us  @datapointnyc  Registra8on  is  now  open.  Very  limited  spots.  

Featured  speakers  include  :    Murli  Buluswar  (Chief  Science  officer  at  AIG),  Catherine  Williams  (Chief  Data  ScienQst  at  AppNexus),  

Slav  Petrov  (Research  ScienQst  at  Google),  Amen  Ra  Mashariki  (Chief  AnalyQcs  Officer,  City  of  NewYork)  

Going from Raw Data to Impactful Predictions Henri Dwyer Data Scientist

Dataiku is the developer of Data Science Studio Our mission: provide companies with the tools they need to carry out data driven projects more efficiently

Recent use of Data Science Studio worldwide

Dataiku in a few words

A bit of history…

January 2013 Incorporation of Dataiku

February 2014 Version 1.0 of DSS 10 clients including Parkeon and PagesJaunes

January 2015 20 employees, 30 clients First investment Version 1.4 of DSS

April 2015 Version 2.0 of DSS Office opened in New York

December 2015 50 employees 80 clients

hTps://courses.edx.org/courses/course-­‐v1:TeachersCollegeX+BDE1x+2T2015  

The  emerging  research  communiQes  in  educaQonal  data  mining  and  learning  analyQcs  are  developing  methods  for  mining  and  modeling  the  increasing  amounts  of  fine-­‐grained  data  becoming  available  about  learners.  In  this  class,  you  will  learn  about  these  methods,  and  their  strengths  and  weaknesses  for  different  applicaQons.  

Applica;ons  

Have you used python before? Do you know what machine learning is? Have fit a random forest?

About the Audience Quick Survey

Making Predictions Introduction

Making Predictions Introduction

Tumor  Malignancy  

On-Task Behavior Classroom activities and off-task behavior in elementary school children

Unsupervised Learning Basic Concepts

Learning  Latent  Variables  Clustering  

Latent  Variable  

ObservaQon  

ObservaQon  

Bayesian Knowledge Tracing

Learned   Not  Learned  

1  

QuesQon  1  Correct  

P(T)  

QuesQon  1  Incorrect  

P(G)  P(S)  

Bayesian Knowledge Tracing

Bayesian Knowledge Tracing

Behavior and Affect Clustering

•  Godwin, K.E., Almeda, M.V., Petroccia, M., Baker, R.S., Fisher, A.V. (2013) Classroom activities and off-task behavior in elementary school children. Poster paper. Proceedings of the Annual Meeting of the Cognitive Science Society, 2428-2433.

•  Pardos, Z.A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2013) Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge, 117-124.

•  Baker, R.S.J.d., Moore, G., Wagner, A., Kalka, J., Karabinos, M., Ashe, C., Yaron, D. (2011) The Dynamics Between Student Affect and Behavior Occuring Outside of Educational Software. Proceedings of the 4th bi‐annual International Conference on Affective Computing and Intelligent Interaction.

Sources BDE1x Big Data in Education

Download  DSS  for  free  at  hTp://dataiku.com