Support educators to interpret qualitative and quantitative data to inform their decisions and support individual student learning.

Once again I will turn to learning analytics and the role of student data in teaching and learning.  In the November 2021 post Learning Analytics in Higher Education: What’s Working I provide a high level overview of the role that student data can play in course design and instruction at the postsecondary level.  In the May 2021 post Using Canvas Analytics to Support Student Success I take a look at a specific learning analytics tool available within the Canvas LMS to help instructors better understand what kind of data is available to them and how that might translate to decisions about teaching and learning. Learning analytics allow instructors to consider whole-class data to observe trends and group needs in a particular course.  Learning analytics also help instructors differentiate instructional tactics for individual students as needed, especially students who are in need of additional support.  In the case of Canvas Analytics, it’s also possible to make learning analytic information viewable to students so that they are empowered to track their own progress and engagement in a course.

Additionally, in the post Did It Work!? A Brief Look at Professional Development Evaluation in Higher Education & Beyond I explore what meaningful professional development looks like for educators in a higher education context and how to consider “measuring” the impact of professional learning.  Action research projects surface as authentically meaningful professional learning endeavors, wherein practitioners are conducting authentic research in their community based on an issue of relevant concern.  Action research requires data collection, and in order to conduct these kinds of projects, educators must learn to interpret qualitative and quantitative data in support of student learning.  Furthermore, this post discusses the role of student performance in evaluating professional development initiatives, and to what extent quantitative student performance data (i.e. grades) should be considered in relation to other, more qualitative factors such as changed beliefs or behaviors.  

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