In addition to compiling introductory resources for those getting started in learning analytics (LA 101) and showcasing the great work by our own LEARN team (Published Research) we wanted to share some reflections on resources each of us have found especially valuable in our pursuit of LA. Keep reading to find out more about the ideas that have inspired us over the years!
My recommendation is Learning Analytics Should Not Promote One Size Fits All by Gašević and colleagues. I really like the way they used a careful research design to generate empirical evidence for an ongoing debate in the field in their paper. Their work shows so clearly why we need to understand the pedagogical design of a learning environment in order to develop useful analytics for it (rather than imagining that we can create one ideal set of analytics that will be useful everywhere).
I love the design research in The Classroom as a Dashboard by Holstein and collaborators that combines learning analytics with human-computer interaction in the K-12 classroom. Mixed-reality smart glasses make a human-AI hybrid superteacher. Now we just need source information to move beyond tutoring system data!
I like Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data by Echeverria and colleagues because it embodies what Learning Analytics is for me: using varied technological means to capture data from an educational activity, analyzing this data to estimate stakeholder-generated pedagogically-relevant constructs, and finally using human-computer interaction approaches to provide actionable feedback that help students and teachers reflect on the activity.
The literature review paper, Awareness is Not Enough, from Jivet and colleagues is a great starting point to understand the state-of-art in student-facing dashboard design! Unlike previous reviews that focus mainly on the technical aspects of student-facing dashboards, such as data sources, they dive deeper to provide a comprehensive summary of the key educational concepts underlying the design of the dashboards.
The Linking Learning Behavior Analytics and Learning Science Concepts paper written by Sedrakyan and colleagues that links self-regulated concepts to learning analytics design is my favorite. It demonstrates a great example of how learning sciences can meet with analytics theoretically and empirically.
I recommend Working Together in Learning Analytics Towards the Co-creation of Value by Dollinger and colleagues. While there has been a lot of talk about the importance of human-centered design in Learning Analytics, this inspiring paper shares one of the few examples of a years-long, large-scale project where the feedback and ideas of a learning community were incorporated into the design of a tool.
I like the review in The Journey of Learning Analytics by Joksimovic and colleagues as it not only provides an easy introduction to the field of learning analytics but also provides a summary of its developments and promising future directions for research. I recommend this to a beginner researcher in the field to get a sense of the multidisciplinary nature of the field and to help identify their own areas of interest within it.
Take a look at Exploratory Versus Explanatory Visual Learning Analytics, it gives a fresh perspective on LA and educational dashboards theory! Echeverria and colleagues make the case for exploratory-focused data dashboards, made available by utilizing data-storytelling techniques, and argue that dashboard designers and researchers need to go beyond UX to include issues of data interpretation.
In What’s the Problem with Learning Analytics?, Neil Selwyn takes a critical perspective on the implementation of Learning Analytics. He explores how Learning Analytics (LA) is shaped by broad socio-cultural and political factors and suggests changes in the ways LA is implemented to mitigate ineffective or potentially harmful uses of LA.