Weeks 12 & 13 11/11/2015-11/25/2015

In the past 2 weeks I have done several things. I attended a webinar on the Tapia conference. I have been looking in depth trying to understand the machine learning experiments ran regarding expertise, class recommender, and class recommender using pupil excitation. I am waiting to receive a copy of a fellow student's project to see what has all been done.

I have also gone to a meeting regarding the funding for our ACM-W group. We received a good amount from the university. We are also getting things in order for the Hour of Code celebration and our table for the organizational fair in January.

Week 13: 11/17/2015-11/25/2015

This past week I worked on the issues wrong with the new feature we were adding to iTrace. The feature allows us to gather eye-tracking data on different parts of the Eclipse Workbench.

We were having trouble with the Project Explorer part gobbling up gazes that should be assigned to a StyledText part. I believe the Eclipse UI is not set up the way we expected it to be or it could be an issue with the threading/architecture of iTrace.

That was all that I could deduce in the past week and I will continue to look into these issues in the coming weeks.
We are continuing on with a study using iTrace without this added feature. The data we collect during this study will add to our data analysis for CREU.

Week 12: 11/11/2015-11/17/2015

This past week I finished lab 4a from the Edx labs. In the lab, I learned how to construct and evaluate regression machine learning models using Azure ML and R.
More specifically, I trained and evaluated a nonlinear regression model which produces improved predictions of building energy efficiency.

I also spent some time setting up the SERESL lab for testing of a new feature we added for iTrace. The feature allows us to gather eye-tracking data on different parts of the Eclipse Workbench that was not available in iTrace before. Preliminary testing revealed issues in the new feature that will be addressed in the coming week.

Week 11: 11/4/2015-11/11/2015

This past week I worked on a template for our poster proposal for Tapia 2016. We will discuss a few preliminary machine learning experiments we performed on the data. We will discuss the data and its acquisition, the methodology for predicting expertise and difficulty, and the preliminary results. We will also have a small literature review and we will have an introduction that includes our hypotheses for data analysis.

Week 11: 11/4/2015-11/11/2015

This week I began to try and understand the machine learning experiments ran on the data sets we will use for the Tapia paper. I am trying to get a good grasp on what the different methods are. 

I started to get prepared for my meeting with the student government association to get funding for our ACM-W group. We hope to participate in the Hour of Code during the week of December 14.

I have also submitted the rest of my paperwork for graduate school. 

Week 9 &10 10/20/2015-11/4/2015

Over these two weeks, I completed lab 4a. I learned how to construct and evaluate regression machine learning models. Regression is a fundamental machine learning method. Regression enables you to predict values of a label variable given data from the past. Regression, like classification, is a supervised machine learning technique, wherein models are trained from labeled cases.  

I also completed  lab 4b in Edx. It demonstrated how to train and evaluate a classification model. Classification is fundamental in machine learning. Classification models enable you to predict classes or categories of a label value. 

I submitted the seed fund application for ACM-W. I also sent in the application for funding through YSU. 

Week 10: 10/27/2015-11/4/2015

This past week I worked on another Edx lab, Lab 4a. The goal of the lab is to learn more about using regression on data. I trained and evaluated a nonlinear regression model which produces improved predictions of building energy efficiency.

I also spent some time researching the topic of regression to get a better feel for how it would apply to our data.

I participated in the ACM-ICPC programming competition this past weekend. My team was able to solve 2 problems and placed 26th in the region.