Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology, the Netherlands. Her research interests include the analysis and interpretation of (sequences of) online and offline learning behavior to improve learning and teaching (learning analytics). In her research, she combines data-driven approaches (such as machine learning and statistical modeling), with theory and human-centered design. Some of the topics she is working on, include:

• Modeling student behavior: How can we model student behavior (e.g., clickstreams in learning management systems, keystrokes during a writing task) and student characteristics (e.g., motivation, self-regulated learning skills)? How does student behavior evolve over time? And how can we use this to improve learning/teaching?
• Explainable Artificial Intelligence: in our lifes we utilize many tools that apply artificial intelligence to predict certain outcomes or to recommend a specific choice. How do users (and especially lay users) understand these models? How can we explain the mechanisms behind these models, such that they are transparent, but do not overwhelm?
• Data dashboards: how can we visualize data about users (e.g., students), which can help them to improve their behavior? How do people intereact with these dashboards? And how can we make sure the visualizations are actionable?
• Keystroke logging as analytics tool: How can we relate keystroke data to cognitive processes, such as writing processes or affect, such as stress? 

Rianne Conijn received her MSc (cum laude) in Human-Technology Interaction from Eindhoven University of Technology in 2015. She received her joint PhD (cum laude) from Antwerp University, Department of Management, and Tilburg University, Department of Cognitive Science and Artificial Intelligence in 2020. She is currently working as a tenure-tracked assistant professor in the Human-Technology Interaction group.

Rianne currently works on a NWO Veni project "Human-Centered AI in education" where she designs and evaluates explanations specifically tailored to students to improve human-AI collaboration in higher education.