Wednesday June 24: Gabriel Smith and Warren Code

Our LAVA meeting this week (Wednesday June 24, 3pm PT, on Zoom) will be led by Gabriel Smith and Warren Code from the Faculty of Science. They will be presenting “Difficulty and Gender Analysis of Course Grades: A Dashboard”

From Gabriel and Warren:

Inspired by the work of the SEISMIC group (https://sites.google.com/umich.edu/SEISMIC/working-groups/key-projects) on demographic effects in introductory STEM courses, we have developed a dashboard (in R Shiny) to explore course performance in introductory courses in the Faculty of Science relative to grade averages over other courses taken, both in general for a course to give a sense of the relative “difficulty” of a course, and also with respect to the gender of the students.  With data drawn from 1st and 2nd year Science courses across a sample of recent years, we will provide a live demonstration of our dashboard (with obfuscated course names) which has a “macro” level to show patterns in disciplines or types (lecture vs. lab) of courses and a “micro” level to drill down into one chosen course at a time.  We will discuss our choices in visualization and modelling in adapting the approach of the SEISMIC group, and offer a brief overview of trends we have observed so far.

If you would like to attend but are not part of the LAVA emailing list, please contact Alison Myers (alison.myers@sauder.ubc.a) for the Zoom information. You can also request that Alison add you to the LAVA mailing list, where we share information about upcoming LAVA sessions.

Wednesday June 10: Fabian Froehlich

Our LAVA meeting this week (Wednesday June 10, 3pm PT, on Zoom) will be led by Fabian Froehlich.

Fabian Froehlich joined the Faculty of Education as a graduate student in 2018. Specializing in Media & Technology Education Studies Fabian’s research focuses on inclusive instructional design through educational technology. He is a SOTL-specialist (Scholarship of Teaching and Learning) at the Centre of Teaching and Learning Technology and worked for the Learning Analytics Team of UBC as a videographer.

From Fabian:

The presentation summarizes findings of my master-thesis: social network analysis as a progressive tool for learning analytics. In order to investigate the following research question: “Do students presented with social network analysis data on online course discussions adjust their engagement behavior?”. A quasi-experiment was conducted relying on an embedded mixed-method research design. Students (n=18) participated in three online discussions. Two online discussions allowed students to access social network analysis visualizations through Threadz, a Canvas plugin. The overall inquiry focuses on how this exposure of learning analytics data might influence the students. 

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If you would like to attend but are not part of the LAVA emailing list, please contact Alison Myers (alison.myers@sauder.ubc.a) for the Zoom information. You can also request that Alison add you to the LAVA mailing list, where we share information about upcoming LAVA sessions.

 

Wednesday May 27, 2020: Craig Thompson

Next week (Wednesday May 27th, 3pm), Craig Thompson from the Learning Analytics Team will be leading our LAVA session,  where he will share some recent work done to analyze course enrolment patterns.

We will be meeting in a Zoom meeting room. For more information and access to the Zoom meeting, please contact Alison Myers

From Craig:

The aim of the work was to uncover groupings of courses that students commonly took while pursuing an undergraduate Biology degree at UBC. This analysis will be used to inform curricular review, for example by highlighting unofficial specializations that students are choosing, which could become officially supported and recognized degree variants. This presentation will include an overview of relevant data mining theory and algorithms, as well as specific findings from applying these methods to course enrolment data at UBC.

Tuesday Mar 10, 2020: Stoo Sepp

This week’s LAVA session will be led by Stoo Sepp (Manager, Learning Design in the Faculty of Education’s Educational Technology Support team). The focus of the session will be on Interaction Treatments.  Stoo will be providing an overview and leading a discussion on how Learning Analytics and Learning Design theory can be advanced by this idea.

From Stoo:

First proposed in 1989, Interaction Treatments refer to the types of interactions that typically occur in technology-enabled learning environments. Applying this concept to the field of learning analytics, we can extend it to incorporate more contemporary theorizing around types of interactions, while refocusing on the ‘L’ in Learning Analytics. In this session, a brief overview of the concept of Interaction Treatments will be presented, along with types of learning analytics used to inform pedagogical action. Finally we’ll have a discussion about how these concepts relate to instructor intention and the design of learning experiences.

Tuesday Feb 25th, 2020: LA Hackathon Feedback

For this session, we are asking for feedback on the upcoming Learning Analytics Hackathon. We will walk through what the approach is this year and what has been developed so far. We will also ask for the group’s feedback! If you want to take a look at the github repo which will be the home-base for the hackathon, please do! The overall goal for the hackathon is for students with any level of programming experience to be able to participate and learn, so it will be useful for us to have some fresh eyes from people with various levels of experience as we make our final adjustments ahead of the hackathon.

More information about the hackathon can also be found on the registration page.

Tuesday Feb 11th, 2020: Emma Novotny

Emma Novotny is the Design Manager from UBC Faculty of Arts. In this session, she will be leading a visualization feedback session for the group!

If you have a visualization that you think could be improved, that you would like the eye of a graphic designer on, or would otherwise like feedback from Emma please submit it to LAVA before the end of day on Monday February 3rd . With the visualization, please include:

  1. The context – what project is this for?
  2. What is the goal of the visualization?
  3. Who is the audience?
  4. How will it be used?
  5. Any other detail you think is important for feedback

If you don’t have your own visualization to show but know of one that you would like to see improved send that! Emma will choose 1-3 of the visualizations and spend time on the 11th talking about her thoughts on how to improve the visualization/ what she might change/other feedback.

Tuesday Jan 28th, 2020: Intermediate Tableau

Alison Myers 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 session she will be presenting some tips and tricks and a closer to intermediate level demonstration of Tableau. From Alison:

“I will be presenting some tips and tricks and a closer to intermediate level demonstration of Tableau. In only an hour we won’t be able to go into too much detail, so if you are a beginner (or not a Tableau user at all) I think you will still get something out of this session, and some of the things I will talk about, I wish I had discovered earlier in my Tableau journey. The tips and tricks will be the “little things” that I often do when I am working on a Tableau project to keep myself organized (and sane). I will spend most of the time talking about things that I do when I’m building visualizations that either make things easier (for me), or take advantage of thinking about Tableau in a different way. This will likely include:

  • Calculated fields that you would likely find in all of my dashboards
  • Thinking about tableau sheets in X and Y: https://www.flerlagetwins.com/2017/11/beyond-show-me-part-1-its-all-about-x-y_46.html (this blog is very well done and really changed how I think about building visualizations in Tableau)
  • Table calculations (although, I’ll be honest, I only have a basic understanding of this myself, so we’ll see how brushed up I can get before Tuesday)

I will briefly introduce some of the topics and include resources, and others I will go more into depth during the hour. As always, there is generally no one way to use Tableau, so this demonstration is from my experience only, and feedback is always welcome. I will try to share a workbook that you can all download to follow along (or that will at least have the same dataset)”

Tuesday Jan 14th, 2020: Warren Code

As Associate Director of  Skylight, The Science Centre for Learning and Teaching , Warren Code, has has been involved with a range of teaching and learning projects across the Faculty of Science, with a particular focus on the Carl Wieman Science Education Initiative and other faculty/campus-wide initiatives.  A substantial part of his work is advising and professional development for their department-based Science Education Specialists, evaluating impact on students and faculty, and connecting with people from UBC and other institutions interested in the accomplishments in teaching and learning within the Faculty of Science. He has also been connected to some analytics projects, including analysis of student response/log data in WeBWorK. In this session, Warren will give an update on the current state of Learning Analytics in the Faculty of Science:

“I’ll be presenting an update on learning analytics in the Faculty of Science: some notable recent projects, and a variety of ideas people at Science would like to explore with the right kinds approaches, partnerships, and support.  I’ll be interested in hearing your experiences in any similar types of projects and any recommendations you may have as to how we might proceed.  You will not need to prepare anything in advance.”

Tuesday Dec 17, 2019: New Analytics in Canvas

In this session, we will take a look at Canvas’s New Analytics tool.

Currently, “New Analytics” can be enabled on a per-course basis as a beta feature on UBC’s installation of Canvas. Instructure intends to disable “Old Analytics” and cutover to “New Analytics” in their March 2020 release.

LA Project team has started a mission to review the features of New Analytics, in comparison to Old Analytics. In this session, Dennis Foung, Support Analyst from the project, will walk us through an overview of the identified differences.

This will be followed by a group discussion around some of the outstanding issues with New Analytics. In preparation for the transition, UBC is working on a change management plan. Any feedback from the community around issues that require prompt attention or escalation to Instructure would help institution in this process.

 

Tuesday Nov 5, 2019: Leah Macfadyen

Exploring the data from CLAS/WeVu: A video annotation platform for learning (Session 1 of 2)

Leah Macfadyen is a tenure track Instructor in the Department of Language and Literacy Education, UBC Faculty of Education and Associate Director of and Instructor in the UBC Master of Educational Technology Program. Leah will be leading one of two LAVA sessions exploring CLAS/WeVu and the data generated from it.

“In this introductory session, I will demonstrate and discuss how I have used a collaborative video annotation tool with students in an online course. CLAS (the Collaborative Lecture Annotation System) – now available as WeVu to users outside UBC, was developed in the UBC Faculty of Arts, and is actively in use in various UBC faculties and elsewhere to support learner engagement with video (as well as audio and now image) resources in a variety of ways: whole-class annotation of lecture videos, one to one instructor feedback on learner ‘performance’ of different kinds, asynchronous group ‘video seminars’ and more. CLAS/WeVu offers learners additional tools to support self-regulated learning, offers educators tools to gather and give richer feedback, and – I would argue – can support more effective social presence in virtual learning environments. 

CLAS/WeVu collects some data that might be usefully explored to reveal learner usage patterns. I will review what data is currently available, and share some sample data (and a data dictionary) with you. So far, little use has been been made of this data to give instructors or learners richer feedback. 

I invite you to join in a small collective exploration of the data (either independently or working with others), experiment with analyses or visualizations that might be useful, and to return to LAVA on Tuesday November 19th to demo what you have achieved. Recommendations for future improvements in data collection and data structure will also be welcome.”

A few published studies investigating CLAS data:

  • Mirriahi, N., Jovanovic, J., Dawson, S., Gašević, D., & Pardo, A. (2018). Identifying engagement patterns with video annotation activities: A case study in professional development. Australasian Journal of Educational Technology, 34(1). https://doi.org/10.14742/ajet.3207
  • Mirriahi, N., Liaqat, D., Dawson, S. et al. (2016). Uncovering student learning profiles with a video annotation tool: reflective learning with and without instructional norms. Education Tech Research Dev 64: 1083. https://doi.org/10.1007/s11423-016-9449-2
  • Pardo, A, Mirriahi, N, Dawson, S, Zhao, Y, Zhao, A & Gasevic, D (2015). Identifying Learning Strategies Associated with Active use of Video Annotation Software. in Proceedings of the 5th International Conference on Learning Analytics & Knowledge (LAK 2015). ACM Press, New York, NY, USA, pp. 255-259. https://doi.org/10.1145/2723576.2723611
  • Gašević, D., Mirriahi,N. & Dawson, S. (2014). Analytics of the effects of video use and instruction to support reflective learning. Proceedings of the fourth international conference on learning analytics and Knowledge. http://www.sfu.ca/~dgasevic/papers_shared/lak_clas14.pdf
  • Mirriah, N. & Dawson, S. (2013). The pairing of lecture recording data with assessment scores: a method of discovering pedagogical impact. Proceedings of the Third International Conference on Learning Analytics and Knowledge.doi>10.1145/2460296.2460331