BETT London Keynote. Learning analytics: Maximizing data to motivate learners’ behaviour
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BETT London Keynote. Learning analytics: Maximizing data to motivate learners’ behaviour
- 1. Learning analytics: Maximizing data to motivate learners’ behaviour Rick Van Sant Blackboard International
- 2. “Where is the knowledge we have lost in information.” T.S. Elliot
- 3. Four Dimensions of Educational Analytics
- 4. Student Learning • How can I easily find students who are at-risk? • Who are the most innovative instructors? • How are students performing on learning outcomes over time? • Which instructional strategies & tools are being used in courses the most? The least? Which are most effective to enhance student engagement & success?
- 5. Student Learning Accountability • What strategies to improve the quality of course design and instruction result in better student performance and course evaluations? • How many logins, time on task, and other metrics have occurred over time? • What student activities are correlated to desired outcomes, grades and course completion?
- 6. “Given accurate and timely normed feedback, most populations will self correct or improve with little or no direct intervention”
- 10. Forecasting, Risk Analytics Reporting Analytics Optimization Analytics Advanced Data Visualization
- 17. “Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom” Clifford Stoll
- 19. THANK YOU Rick Van Sant, Ph.D. rick.vansant@blackboard.com
Editor's Notes
- Universities use data in a large number of ways. However, the data that ultimately directly impacts the life of a student is primarily contained in these four dimensions: Recruitment, Advision, Retention and of course Student Learning itself.
- So what are some of the common types of questions that educators should be asking…
- One of the most overlooked aspects of analytics is the ability to provide students with normative data on their learning behavior. Grades are only meaningful to students because they have a general knowledge of the normed data of F –A. Sometimes we provide test data with the curve so students can see what their score is relative to the whole class. But what if students had much more specific data on their performance, not just their results. How does a student compare their engagement time vs the class norms. What might a student do if they knew that students who get “X” grade do this much specific reviewing of material?
- What do all behavioral change programs have in common…. Data feedback! This is my current score on the online brain training program Lumosity. When I click on the BPI – I get a score. Did not mean much until I had completed enough games to get a comparison with others in my age group. Ouch !!
- So, let me walk you thought this chart
- Here is the type of data we often get through our standard reporting mechanisms. From this data we can see what there is a downward trend in women, growth in Latino population and relatively steady enrollment by Black students. So what? How is this data helpful? How can this data help the institution to reverse the trend in Female enrollment?
- Reporting Analytics we just saw on the previous slide. Gather enough data and you can begin to forecast and make some risk analysis assessments. This has been around for a while with enrollment projections but is now beginning to emerge relative to predicting student success based on a combination of data points (ASU e-advising). Also emerging are data sets that allow us to look at optimization. What is the optimum course sequence and progression for a particular student?
- Let me show you an example of data visualization… This is a short clip from one of my favorite TED speakers, Hans Roseling.
- (This slide has animation) Now, let me show you an example from Blackboard Learning Analytics This is a Course Activity vs Grade scatter plot. This report is designed to show me “How active are my students, and how does that correlate to their grade performance?” In this report, I’ve told the system to focus on just one course – Ethics in Science & Engineering Each one of these blue dots represents a different student. And we see a red regression line showing the average. ------------------------------------------ (Click for animation – a green highlight box will appear) Most students are in the upper-left of the red line. That means they are putting in about the average amount of work in the course, and performing pretty well (above 85%) ----------------------------------------------- (Click for animation – a yellow highlight box will appear) I have one student all the way on the right. She is putting in a lot of work
- I have one student all the way on the right, that I’ve highlighted in the yellow box. Her name is Marsha Dyess She is putting in a lot of work in the class, and doing decently well at 81% But she isn’t really getting a high return for the amount of work she is putting in. But I really want to focus on the two dots in the bottom-left, circled in the red Let’s zoom in.
- These two students are interacting with the class at an average or below-average rate But they are performing very poorly – they are about to fail out These are my AT RISK students. These students are performing poorly, and are the ones that need our help the most to get back on track and become successful. But I need more information to help them. I can click on the blue dot, representing their name, and get more details about them. Let’s look at one of those two students – Lynne Nishi
- This is the Student at a Glance Report. This report shows me the data that we’ve collected so far about this one student – Lynne Nishi In the upper-left, we see basic information about her status at our university She’s a Mechanical Engineering student, in her 3rd year, in good standing. The colored blue & gold graphs show Lynne’s activity in her courses, compared to other students. We see that Lynne is putting in about slightly more work than her classmates. That’s a good sign. Down below, we see her academic performance at our university. Let’s zoom in on that.
- (this slide has animation) At the bottom of Lynne’s report, I can see detailed information that I’ve collected about her. On this part of the report, I can see her current grades (captured in Blackboard Grade Center) for her current courses And compare them to the grades that she’s received in past terms. Now, I see that in the past, Lynne has been performing relatively well. She’s received A’s, and B’s, and C’s for the most part. And getting about 70% - 85% in most of her classes. That’s not too bad. ---------------- (Click for animation – a red line box will appear) But this semester, I see that Lynne is getting about 60% average in all of her classes. That tells me something is wrong. That tells me that we should talk to Lynne, and see what is going on. See if there is something that we can do, like tutoring, to help Lynne get back on track. That’s the kind of action we want to be able to take by having grade data available to us.