At Blackboard, we want to make educational data science accessible to those who are in a position to put its results into practice. It is therefore with great excitement that we announce the latest release of Analytics for Learn (A4L). With the addition of rubric support on the one hand, and more granular tool detail on the other, A4L has become more than an industry-leading LMS reporting environment. With the ability to correlate specific tool use to well-defined learning outcomes, A4L has become the first true learning analytics workbench for higher education.
The rough ability to discover patterns between student activity and grades is helpful as we seek to identify struggling students and support their successful progression through to earning a degree. For faculty and advisors this is very important information indeed, and its use in support of intensive advising practices has seen incredible results. But grades are a rather blunt instrument when it comes to understanding effective teaching practices. A final grade is only one small measure how much a student has learned, and how prepared that student is to master content in future courses.
Rubrics are powerful. They foster equity by promoting consistent grading practices. They are also formative pedagogical tools. They render grades meaningful by making areas of strength and weakness explicit for students. With Rubric support in A4L, faculty developers are in a better position to encourage the use of rubrics as a best practice among instructors. Institutional researchers can easily monitor and report on progress toward the achievement of broad institutional learning priorities. Curriculum designers can ensure that programs are achieving what they are designed to achieve. Advisors can more easily guide students to take advantage of the right academic support services. In short, with access to rubrics, we can finally start to bridge the divide between conversations about student progression and educational quality.
With the release of 4.3.5, we have also added more granular tool detail. What this means is that users have access, not just to activity information by tool type (i.e., content, tools, assessments), but also to information about how students and faculty are engaging with specific pieces of content, particular tools, and individual assessments. In addition to usage data about native features in Learn, A4L users now also have access to that same data for mobile interactions and third party tools, as well.
Early in my learning analytics journey, one of my projects involved trying to understand how specific tool use patterns correlated with course pass rates. In order to do this work, I had to manually query several transactional tables within Blackboard Learn and do extensive cleaning before I was even able to start analyzing the data. When all of this work was complete, however, I was able to make several really interesting observations about how the LMS was being used at my institution. I was able to determine that rates of Blackboard Learn utilization were far higher than anyone had originally thought. I was able to discover third-party tools in which we were investing a non-trivial amount of money, but that were only being used by a handful of faculty members. From the perspective of instructional design, I found that faculty who made use of internal course links saw pass rates that were on average 4 points higher than those who did not, and courses that made use of the grade center saw a nearly 2 point increase in pass rates compared to those that did not.
This kind of work is important for establishing edtech ROI, and incredibly helpful for instructional designers and faculty developers looking for evidence in support of assumptions about best practices. Until now, however, it has been a challenge to gain access to the kinds of granular data necessary to support and scale this work more broadly. With A4L 4.3.5, institutional researchers, deans, instructional designers, faculty developers, and researchers alike have easy access to valid data about the online teaching and learning experience, and I am incredibly excited to see what institutions are going to discover as a result.
With the addition of rubric data and granular tool detail to what was already the most comprehensive LMS data model available, we at Blackboard are excited to give practitioners access to information in a way that was previously only available to researchers. We are automating the data collection and cleaning process so that researchers and practitioners can easily get to the real work of analysis, sense-making, and action. With A4L 4.3.5, users have data that is at once more granular, and more meaningful thanks to rubric support. With this we are making educational data science more accessible to a broader population of expert practitioners, fueling discovery, encouraging learning analytics research, and scaling innovation.