Learning Analytics & Open Data Hackathon 3.0

Event Date & Time

Event Description

Please note: Thank you for your interest.  Unfortunately, we are at capacity for this event and new registrations will be added to a waitlist.  

Please join us for the 3rd UBC Learning Analytics Hackathon on March 10th and 11.

The Hackathon seeks to explore in what ways learning data can be used to benefit students. Participants will have access to open learning data sets and will work towards developing, designing, and proposing tools, reports, and visualizations that could be used to improve student learning and experiences. Innovative proposals or designs could become part of the UBC’s Learning Analytics pilot.

During this two-day hackathon, participants will form teams, work on an open data set, propose and design visualizations and dashboards and then show off what they accomplished at the end of the weekend with a brief presentation.

This year we will work with open data that includes course based activities. Ever wondered what patterns can be determined from how students are interacting and engaging with course materials and resources and how that information could be used to improve learning? Interested in learning what course data looks like? Do you have some ideas for analysis or applications that might give insight into this data? Can you think of a way to appropriately visualize or act upon this data?

If you have the desire to explore educational data, or to even just learn more about learning analytics, data analysis and/or visualization then we are looking for you to join us! This event brings together students, researchers, faculty, staff, and any other interested individuals to get hands-on experience with analyzing and working with learning data.

The hackathon is free but registration is required. We will provide data, space, mentoring, breakfasts, and lunches. No prior experience is necessary.

Workshops

No experience is necessary and we meant it! Mini-workshops will be offered for anyone who feels like they might need a little inspiration/instruction before diving into the data. Workshops will include:

Design Track (No experience necessary, this track is aimed at those new to learning analytics):

• Designing Learning Analytics for Students
  Learning Analytics can be a useful tool for understanding course engagement and activity and this information can be used to improve instructional design and student learning. In this session we will review research and analytics examples from available tools.  Participants will explore the types and kinds of questions that could be answered by learning data and engage in a design process to develop ideas, apps, visualizations, or prototypes that could improve learning or the student experience. Facilitators: Ido Roll, Craig Thompson, Alison Myers, David Laing, Sanam Shirazi
• Intro to Data Visualization with Tableau:
  In this session we will be exploring and visualizing data using Tableau. We will review some basic principles of data visualization and the basic functionality of Tableau. At the end of this session we will have begun using the hackathon data and you should be able to begin using Tableau to generate and answer questions. For this workshop, you will need a laptop and the ability to download Tableau (see below for more info on how to access Tableau).  Facilitator: Alison Myers, Research Analyst, UBC Sauder Learning Services, LAVA
• Exploring How Analytics Can Be Used to Improve Teaching & Learning:
  Learning Analytics can be used for better insight into, and assessment of, effective online teaching and design methods as well as identifying specific areas of challenge and success for students. This session will provide an overview of UBC’s learning analytics pilot and engage participants in a discussion about how new and emerging tools, such as Canvas Analytics, could be used to improve teaching and learning at UBC.

Development Track (aimed for those with some experience working with data):

• Data Wrangling with R.
  One of the most important skills in learning analytics is to be able to manipulate – or “wrangle” – a dataset so that you can ask new questions of it. For example, you may wish to filter your rows to keep only events that occurred during a particular time period, or to group by assignment ID and compute the mean grade achieved. This workshop will cover the basics of data wrangling in the programming language R. For this workshop, you will need a laptop and the ability to install R and RStudio (see below for more info). Facilitator: David Laing, Data Scientist, Centre for Teaching, Learning & Technology
• Advanced Data Visualization with Python and Jupyter Notebooks
  Python is a popular programming language for data science and the Jupyter notebook is a web-application for writing Python code in the browser along with text, images, and mathematical formulae to add narrative to our data analysis. In this session we will work in Jupyter notebooks and explore pandas and matplotlib, the essential Python packages for importing, cleaning, manipulating and visualizing data. Participants are strongly encouraged to bring either (1) a laptop with Python and Jupyter installed (see https://www.anaconda.com/download/) or (2) any device equipped with a browser and keyboard and login to https://ubc.syzygy.ca with CWL credentials. Facilitator: Patrick Walls, Instructor, UBC Department of Mathematics

Schedule

New: A Program Schedule with Room Locations Can Be Downloaded Here

Day 1: March 10th

• 9:00am – Doors open – David Lam Building, Sauder Learning Labs (DL005)
• 9:30am – Welcome and Introduction – Leonard S. Klinck Building Room 201 (LSK201)
• 9:45am – Getting Going with the Data: Overview of the data
• 10:00am – Workshops or hack time: join a workshop or form teams and begin hacking
  • Design Track: Designing Learning Analytics for Students
  • Development Track: Data Wrangling with R
• 12:00pm – Lunch (provided)
• 1:00pm – Workshops or hack time
  • Design Track: Data Visualization 101 using Tableau
  • Development Track: Advanced Data Visualization Using Python & Jupyter
• 5:00pm – Informal check-ins (5 minutes per group). We have the rooms until 7pm, but will start wrapping up at 5pm (although teams can continue working on their own if they’d like!)

Day 2: March 11th

• 9:00am – Doors Open, Begin Hacking
• 12:00pm – Lunch (provided).
• 1:00pm – Team wrap-up and presentation planning should begin
• 3:00pm – TEAM PRESENTATIONS
• 4:00pm – Event wrap up and prized
• 5:00pm – End of the Hackathon

More Event Details

What do you need to bring?

• A coffee mug! (and/or water bottle)
• Laptop and power cables.

The Data

We will be working with the following open learning data sets, which can be downloaded before the hackathon:

• https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1XORAL
• https://analyse.kmi.open.ac.uk/open_dataset

Things you might want to do beforehand?

• If you plan to use Tableau, you can download a trial of Tableau: Tableau Trial. (Students! You can register for a FREE 1-year license of Tableau Desktop: Student Trial).
• If you plan to use R, you can download R here, (http://cran.stat.sfu.ca/) and you can download R Studio here.
  
• If you plan to use Python and Jupyter notebooks, you should install the Anaconda distribution of Python 3 and Jupyter: https://www.anaconda.com/download/

Time, Date, Location

• Date: March 10th & 11th, 2018
• Time: 9am to 5pm
• Location: Sauder Learning Labs, David Lam DL005

The Hackathon is organized by:

• UBC Learning Analytics Pilot (http://lthub.ubc.ca/projects/learning-analytics/)
• Learning Analytics, Visual Analytics (https://blogs.ubc.ca/lava/about-lava/).

Want to Learn More:

Read and watch about last year’s Hackathon here.

Please also feel free to contact Alison Myers (alison.myers (at) sauder.ubc.ca) or Will Engle (will.engle (at) ubc.ca) with any questions!

Please note: Thank you for your interest.  Unfortunately, we are at capacity for these events and new registrations will be added to a waitlist.