Session Description: What gets measured gets improved. We are tracking activity like never before using wearables to measure steps and sleep. This data leads to greater awareness, which, in turn, perpetuates positive changes in user behavior. What if we applied this same concept by measuring activity in the classroom? Research shows that the more a student is engaged in class, the better he or she does in the course. Hear how behavioral data from the classroom, like learning management system engagement during the first weeks of class, note-taking and reviewing recorded lectures, can offer real-time insights about student success and transform the teaching and learning experience.
Panel Speakers: Travis Thompson – University of South Florida, Jenna Talbots – Whitehead Advisors, Kristin Eshleman – Davidson College, Mark Milliron – Civitas Learning, Fred Singer – echo360
What can higher ed learn from wearables?
- Last leg of the journey… data analysis and revealing insight
- Ability to analyze large streams of data
- Realizing simple basic things from large amounts of data
- Giving data to users for the benefit of the user
- When people interact with their own data they change their behavior
- People wear Fitbit for the data – get hooked
- Using data to chart the way forward
- Getting data to the people in the way that is useful to them
- The experts are “us” if we have the data
- Education is far behind in data gathering when compared to other industries eg. health care, athletics, insurance, finance, etc.
- In the classroom, can we track what is happening there? Yes we have grades, but what about the information DURING class?
- Data is there to empower faculty not to replace them…
- Data can provide self-awareness and intrinsic motivation
- Fitbit gives data about the environment, in the classroom what about the environment (lighting, classroom space, temperature, active learning interactions, etc.) – how can we understand student engagement more fully with data?
What are we learning from data?
- Meaningful stories about data – move the needle, they are compelling as a continuous improvement movement
- Putting the data in terms familiar to users – this engages them, especially if there are incentives
- Retention – these data are needed BEFORE the semester is over BEFORE it’s too late
- Early detection is helpful
- In class, asking in realtime questions
- Engagement in class via active learning resulted in lower video lecture watching, why is this… unknown as of yet
- Awareness allows the change in behavior
- Engagement data outpaces demographic data quickly – what students DO is far more predicative than WHO they are…
- Data: 1) relative variables (usage vs other users), 2) consistency (when a pattern is broken there is an alert), 3) Min/Max, 4) Averages across user activity
- Pinpoint data is helpful as far as the basics (logging into LMS), but beyond that we need more sophisticated analysis – pinpoint is still valuable as you can see heatmaps of engagement – you can see “cramming” behaviors for example…
- Nudge campaigns can help with variables of data
- If learning is a change agent, why aren’t we also learning along side students using data
- Most faculty don’t have access to the data they need about their students
- The data about learning is signal processing – in a face to face small classroom this is done extremely well by faculty (body language, eye contact, in realtime)
- With data and predictive modeling and simple nudge campaigns (you have done great so far, you can do it, we are here to help!)
- Analytics don’t tell you why but the signal is there and it can start the conversation and social engagement between faculty an students
- Micronarratives can tell you a lot and give you insight of the “weak” signals – with the goal of helping students to thrive and feel connected
- The integration of a variety of sources provides the strongest light of data
- Design thinking is helpful in how to manage the data to interact with the users
- Bridging the people and technology under a shared why is key
- Extractive data is important, but this is our students data – so how can we go where they are and find out what matters to them and how can we give back to them – to help them
- Let’s use all of our sensors (data gather devices) to begin a movement of infrastructure of analytics to create student success scientist…
What about you? Use the information in front of you to start!