Week 17: 1/18/2017 - 1/25/2017

This week, we discussed the best way to approach the data analysis. I will be using Apache Spark and the command line and running my data projects in the IDE IntelliJ. We weighed the advantages and disadvantages of running Spark through a virtual box, and found using an IDE might be the best way to go. 

I helped to give a demonstration about our eye tracking equipment and software last Friday with one of the graduate CS students at YSU to several groups of high school students. We showed them the interesting aspects of eye tracking as well as the advantages it brings to our field. The students enjoyed the Asteroids game where each time a user starts the game, they have to pop incoming asteroids using only their gaze. We also demonstrated using dual screens how the eye tracker records fixations of any user, and we asked some of the students to find different values on the YSU website. 

I gathered two students in the CIS department to participate in our study. This Friday, my first volunteer will be coming in to the Empirical studies lab. I hope to gain at least 4-6 more students throughout the semester so that we can continue to analyze fresh data. 

Next week, I will be finishing up my poster for the Ohio Celebration of Women in Computing based on the analysis of our first set of data. I will be attending the conference on February 24-25 with Dr. Sharif, Dr. Lazar, Jenna and a few other students in the computer science department. I'm excited to attend the talks (as it's my first technical seminar that isn't local) and I can't wait to hear from women with experience in the software engineering field!  What an exciting time to be a woman!  I will be sure to post photos from the seminar upon my return. 

Weeks 16 & 17: 1/11/2017 - 1/25/2017

These past two initial weeks back to school have been hectic, so I decided to combine the posts for the last two weeks together. In addition to beginning data analysis on our Stack Overflow project, I have been working on a separate data analysis to expand upon our expertise prediction analysis from CREU 15-16.  This analysis looks at the ABB eye-tracking data we collected in a different fashion than machine learning prediction algorithms. I used sequential analysis techniques from an R package to look at the data as sequences, so the order of what participants looked at is taken into consideration. Initial results show significant difference in the gaze patterns of novices versus expert. The most challenging part of my analyses was dealing with how large the sequence data is for eye-tracking data. I had to manipulate the R package I was working with to make some of the analyses work with such large data.

We intend to submit these results to a journal and to the Mining Software Engineering Repositories conference (MSR). So, this past two weeks I created and presented a presentation about the analyses I performed to get an idea of what to submit to MSR. I also moved the beginnings of our journal paper to Overleaf in order to write the paper collaboratively.

This past weekend I participated in a 4 day mathematical modeling competition called COMAP. I worked with two other students to model the merging of cars following a toll booth barrier. We wrote and submitted a report detailing our model.

Week 16: 1/11/2017 - 1/18/2017

It's the start of the Spring semester and we are working on the second half of our project. We are continuing to collect data. This week, we discussed our upcoming trip in February to present at the conference for Ohio Celebration of Women in Computing in Huron, OH. I'm working on creating a poster about the results of our eye tracking data in reference to participant tag scores. I reviewed the results with Jenna, Dr. Sharif and Dr. Lazar, and I am working on a short presentation about the goal of our project. 

I am going to run the data from the research paper 'Predict Closed Questions on Stack Overflow' into Apache Spark and compare the results of the original to what I find. This will be my first time using Apache Spark with the command line and I'm looking forward to learning more about machine learning algorithms. 

Week 15: 12/06/2016 - 12/13/2016

I have concluded my contribution to the study in regards to how developers read & comprehend stack overflow questions for tag assignment. I went in with the objectives of figuring out where developers focus most and what were the most valuable areas of interest (AOIs). What I discovered is that often time, fixation count and fixation duration distributions correlated among AOIs I defined. I found that when questions get more complex, participants (especially those with more experience programming) spend more time on code and less time on title. I found that overall those with more experience use the code more and more to assign tags while those with less experience might rely on plain text such as title and description to assign tags. Keywords were an important feature in the questions for users as they fixated on them early and often revisited. I hope this will be useful in creating a weighting system for tag prediction as my team moves on to that. My complete study presentation and write up can be found on the Research page of our website.

Tomorrow is graduation for me. I had a really great time working on this project and working in the field of eye-tracking was very interesting for me. I am excited to move into my career and take all this valuable knowledge with me.

Week 15: 12/06/2016 - 12/13/2016

This past week has been really hectic with finals and graduate school applications. I worked on and completed my end of the year report for the CREU program. I also refreshed my memory on Spark Machine Learning, through a tutorial Dr. Lazar suggested we try. I looked over my notes from my summer internship where I learned how to use Spark Machine Learning for the first time. The Spark Programming Guide offers a more comprehensive tutorial on Spark ML, which I spent time looking over. It is easy to get up and running very quickly with Spark and it is comprehensive in the machine learning algorithms that are available. Spark will be an excellent tool for our data analytics phase.

I also finished seven graduate school applications this week! I am looking forward to getting feedback in late February, early March.

Week 14: 11/29/2016 - 12/06/2016

This week I attended the MLH Local Hack Day at YSU on Saturday. Jenna gave an interactive talk on setting up Spark, along with a Scala code tutorial of some simple loops and functions. I was a little intimidated by the syntax, but I think over time I will adjust to the language. I viewed her powerpoint presentation on running Tweet data through Spark, but we are meeting up on Friday to continue the learning process. We now have 6 participants in our study and will soon be moving on to the machine learning phase of our project. I am eager to continue moving forward with this phase as I continue to learn more in my field. 

Week 14: 11/29/2016 - 12/06/2016

This week I concluded the data collection phase. I was able to capture a varying range of people in regards to C/C++ experience. All the participants were YSU students, the majors that participated were Computer Science and Electrical Engineering. While I was hoping to get other majors from the CSIS department to compare gaze-data I think having 2 majors will be enough to compare. The process went smoothly, I was able to keep all the collected gaze-data and I also learned a lot from even moderating. After just visually analyzing (i.e. looking over gaze-data representations, no tools) I can already determine a few trends. For example, those who have less experience with C/C++ use the title and question text more to assign tags, especially for the more complicated tasks, versus using the code. It also seems that those with more C/C++ experience were better able to assign tags that apply more to the question solution versus obvious things found directly in the text/code, this was expected. I plan to incorporate simple observations like this, as I think they are useful in interpreting how tags were selected. In this upcoming week I will do the following:
1. Analyze data as a whole - use Tobii to look into fixation count, duration count, and time to first fixation. I hope to compare how people considered oracle (positive) tags vs the distractors (negative) in coming to their tag selections.
2. Compare data from different levels of experience - consider how people came to the correct/incorrect conclusions based on their experience levels and try to determine common trends.

Furthermore, I want to use the gathered data to determine keywords that should award higher weights to suggested tags. I think this is something that will be helpful, especially in the future when applying the machine learning algorithms.