Have you ever wanted to explore the UBC Course Calendar data? Did you know that Canvas has an API that allows you to access and explore your learning data with code? Please join us for the UBC Learning Analytics Hackathon November 2 – 3 that seeks to explore ways of using and integrating the Course Calendar and Canvas data to benefit students.
Participants will work on proposing, designing, and building applications and dashboards that could be used to improve student learning and experiences. Innovative proposals or designs could become part of Learning Analytics at UBC.
For the first time, we will provide access to the UBC Course Calendar data, which includes descriptions, pre/co-requisites, the number of student registrations, instructor names, buildings/rooms, and more, for every course offered at UBC from 2014-2018.
We had a blast at UBC’s 5th Canvas API and Learning Analytics Hackathon that was co-organized by LAVA, UBC LA Project and CAPICO. This two-day event took place from 4:00 PM–7:00 PM on Friday, March 29 and from 9:00 AM–7:00 PM on Saturday, March 30. Participants formed teams and worked towards planning, designing, and building applications and dashboards aimed at improving student learning and experiences. With over 100 participants attending, the event was sold-out.
Day 1 started with a design thinking workshop, walking all the participants through the process of problem definition and ideation to come up with their own solutions. On day 2, two tracks were offered – beginner and advanced – which allowed students with different skill levels to participate and engage in a series of workshops that walked them through the process of retrieving, cleaning, and then visualizing learning data from Canvas API. Students got a chance to learn how to present and sell their ideas and continued to hack their way. The hackathon was concluded by teams presenting their ideas, projects, and learnings along the way.
We would like to invite you to the 4th UBC Learning Analytics Hackathon, which will take place on October 27-28, 2018. This event is organized by UBC Learning Analytics Pilot, LAVA and CAPICO.
Did you know that Canvas includes an API for accessing and modifying your learning data in your own programs and scripts? Have you ever wondered if you could build a Canvas app that improves your own learning? This fall, the hackathon will explore how the Canvas API can be used to improve student learning and experiences.
This event brings together students, researchers, faculty, staff, and any other interested individuals to get hands-on experience with analyzing and working with the Canvas API. During this two-day hackathon, participants will form teams, work with Canvas’s REST API, design and build apps and dashboards, and then show off what they accomplished at the end of the weekend with a brief presentation. Prizes and awards will be given out for interesting projects.
We are going to have a very informal LAVA meeting this week where we will be: debriefing on the Hackathon (what were some of the student output, what went well, what didn’t), discussing the schedule for the summer, and we would like to hear about potential meeting topics from you!
If you can’t join, send an email to Alison. – any thoughts on the hackathon? What should the summer look like (more or less meetings, new schedule)? What is something you are working on or would be willing to lead a meeting on?
Please join us for the 3rd UBC Learning Analytics Hackathon on March 10th and 11. The Hackathon is organized by UBC Learning Analytics Pilot and LAVA.
“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.
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 (please register before March 7th).
Workshops (No experience is necessary!)
*Design Track *
Designing Learning Analytics for Students
Intro to Data Visualization with Tableau
Data Wrangling with R.
Advanced Data Visualization with Jupyter and Python“
Alison is a Research Analyst at UBC Sauder School of Business. Working on a variety of data projects, she has been using Tableau for creating interactive dashboards and visualizations.
” In this meeting, I will be reviewing some of the functionality of Tableau for those of you interested in seeing how it works/diving a bit deeper into the capabilities. I’m not going to start from ground 0 (download tableau, import data, drag and drop functionality). If you have any specific questions of “how can I do this”, or “I tried to do this but couldn’t” then please send those along to me and I will try to make sure to answer them, or to provide you with some resources that will be useful.
Hopefully, the other Tableau experts (Sanam Shirazi, Leah Macfadyen) will also be joining us, and I expect them to jump in and correct me in my examples, or offer their alternatives. I’ll try to review some basics about calculated fields, fixed calculations, special data type functionality, parameters and some principles about visualization. Hopefully, I will introduce enough vocabulary for you to have something to Google later rather than go into depth in any specific area. One useful thing to remember – it’s really easy to do one thing in about 10 different ways, so your experience may be different than mine!
If you don’t have it installed – download a free trial here. If you’ve never used Tableau, there are some introductory materials available to you on this page as well. I would start here for anyone with no experience. Or, just download and begin to play.”
Alison will be running a Tableau workshop in the Learning Analytics & Open Data Hackathon 3.0. If you are interested in participating, sign up here for the upcoming hackathon.
Few people have expressed interest in running a Hackathon this year. So in the next LAVA meeting, we will be discussing as a group what Hackathon 3.0 could look like. Alison is a Research Analyst in the UBC Sauder School of Business who helped organize Hackathon 2.0 at UBC last year. She writes:
“For those unaware, we have run 2 hackathons in the past. The first we had researchers with datasets/their own questions, the second we had a single dataset (MOOC data) with a range of questions. Groups were formed, data was hacked, and results were presented. In the past we have used “Hackathon” somewhat more loosely than you may have seen elsewhere, where we haven’t actually required those attending to have any previous experience.
We will quickly review what we have done in the past and then it will be an open discussion.
If you have interested in planning a Hackathon – what would you like it to look like? If you have participated in a LAVA Hackathon (or other) – what did you like, what would you change?”
“My interest in having a hackathon was to bring people together who either want to learn more about doing data analysis, know about data analysis but want to bring into a new context, which is learning analytics, or don’t know anything about either of those things but are generally interested,” said Alison Myers, data analytics specialist at the UBC Sauder School of Business.
This year the hackathon began with a series of workshops on visual analytics, temporal data analysis and statistics using the R software. The idea was to support participants in expanding their knowledge base and making first steps with their analyses.
Hackathon participants were given data from two UBC Massive Open Online Courses: one course focused on Chinese philosophy, the other focused on the science behind climate change. Data included event-trace data, student demographics, discussion-forum posts, attitude surveys, and summative data, enabling a broad range of analytical approaches. Participants formed groups depending on their expertise and interests.
“I have done research on learning data in the past. Patrick is doing computer science so he knows more about coding. And Vesta has experience with visual analytics,” explained Mario Cimet, a student studying Cognitive Systems at UBC, about his team.
The event was an opportunity for like-minded people to meet and share their passion for data analysis. The hackathon was also aimed at raising the profile and visibility of learning analytics. Learning research data can give instructors feedback about their teaching approaches and resources, and how they’re working in their classrooms. It can inform departments about why certain classes are more popular than others and thus support planning at the program level.
“Learning analytics is using evidence about learners to improve the process [of teaching],” said Cimet. “I think it’s important because any decision that you make that is going to deal with their education, you should do it with as much evidence as possible. You should do based on facts.”
Here are three examples of what the participants were able to achieve during the hackathon weekend:
Course tree, where circle size shows either activity level across all learners, time spent per learner, or frequency of this being the last visited page of the course: http://link.landfood.ubc.ca/courseTree/ by Anh Nguyen, Shirley Lin and Justin Lee
Another course tree, where the width of a line represents the movement from node to node, the size of the circle is the number of unique learners, and the colour of a circle shows whether this was a student’s last activity in the course: http://static.useit.today/ubcxhack.html by Patrick Coleman, Mario Cimet and Vesta Sahatciu
Reflections on the hackathon experience by data presenter/scientist/instructor Dr. Megan Barker
I’d been looking forward to the hackathon for a few months, and it completely blew away my expectations! As a data presenter at the hackathon, my role was to share ideas and data from my collaborative project characterizing classroom practices in UBC biology – all in the hopes of tempting data-savvy hackers to play with the data for the weekend. In my research project team, we currently have pedagogical expertise but are sorely lacking skills in visual and data analysis. This hackathon was a perfect opportunity for us to share the dataset with analysts and students looking for real educational data to work with. The event was a smash success: we built teams, worked on real projects together, and had tangible successes by the end.
In our research, my colleagues and I ask the basic question:
We approach this by observing and collecting data from many classes in our department…..Read the full article.
In October 2015 the Learning Analytics Visual Analytics (LAVA) group held the first ever learning analytics hackathon at UBC. During the two-day event, more than 70 participants with a wide range of backgrounds and expertise applied a variety of approaches to analyzing learning-related data. Some used classroom observation data to better understand how learning unfolds, while others used data from a learning management system to identify patterns in how learners use available materials.
According to Leah Macfadyen, program director of Evaluation and Learning Analytics at the Faculty of Arts, the idea for the hackathon came about in one of the group’s weekly meetings, after a member brought in a large data set and asked for help. The group had a lot of fun tossing around ideas about how to analyze the data and how to best present the results. The outcome was so successful that the group wondered, “Why not make it bigger? Why not have a hackathon?”
Macfadyen and other event organizers were pleasantly surprised with the large turnout. “People are interested in doing this?” asked Megan Barker, a Science Teaching and Learning Post-Doctoral Fellow with the Carl Wieman Science Education Initiative and data presenter at the hackathon. “We had no idea. We thought maybe a couple of people would find this interesting. Then all of the sudden our registration was full and we had people on the waitlist.”
The event began with five researchers from across UBC pitching learning data sets to participants. Each data set captured a different aspect of learning, for example, student interactions with video content and moment-by-moment actions in a virtual physics lab. Data presenters then challenged participants to find the story behind the data.
The hackathon attracted undergraduate and graduate students as well as faculty, staff and professionals. Participants formed groups depending on which of the five data sets they chose to work on as well as their disciplinary area of expertise and preferred analytics approach. “We wanted to bring together people who were interested in working with data. If they didn’t have the same background it’d be even better. They could learn from each other,” said Macfadyen.
Tyler Robb-Smith is a student at the British Columbia Institute of Technology and has a background in Nanohydrodynamics. He came to the event to meet people and build on his data analysis skills. “It’s interesting looking at different perspectives. It’s interesting how everyone has an area of expertise, and added together it made [the process] quite easy,” he said. “You are able to go a lot further in a project that individually would take you a lot longer.”
The event was an opportunity for like-minded people to meet and share their passion for data analysis as well as learn about LAVA. The group started with Macfadyen and a few students who were interested in working with learning research data, but it quickly grew. It wasn’t long before other faculty and staff started asking to join the weekly meetings.
“A lot of people didn’t know then and still don’t know that this type of research actually exists at UBC. Learning analytics is a new field,” said Macfadyen. According to her, the university already has a lot of learning and teaching data available, for example, from student registration systems and course evaluation systems. “These goldmines of potential insight are just sitting around and they could and should be used to inform decisions about planning throughout departments,” she added.
That’s why the event was also aimed at raising the profile and visibility of this emerging field and showing what can come out of this type of analysis. Learning research data can tell instructors about what the most effective teaching methods are and how they’re working in their classrooms. It can inform departments about why certain classes are more popular than others. It can help instructors plan for courses.
“We really wanted to raise awareness about how data can be used to improve teaching and learning practices,” said Ido Roll, senior manager for Research and Evaluation in the Centre for Teaching, Learning, and Technology and one of the event’s organizers. “The hackathon was a great way to combine interesting questions about how people learn, large data sets and a group of eager and motivated experts.”
This year’s event was a success. At the end of the hackathon nine participant-led groups presented their research findings. Several of these projects have become full-scale research projects following the event. According to Roll and Macfadyen, the most common question from participants was how soon would there be another hackathon.