As digital assessment and learning environments become increasingly prevalent in education, it is crucial that they meet the needs of neurodivergent and low-performing students. Maximizing the achievement of all students requires a personalized, adaptive, and inclusive environment. In addition to machine-driven personalization, learner-driven personalization through choice has a potential to enhance learner performance, engagement, and motivation (Brod et al., 2023). Although empirical studies have shown that choice can improve learning outcomes (Patall et al., 2017), memory retention (Murty et al., 2015), and learner engagement and motivation (Patall, 2013), the boundary conditions of learner choice are unclear, particularly among neurodivergent and low-performing students.
In this talk, we first discuss how we can give learners more control over their digital learning experience (e.g., the user interface, input methods, adaptivity, lesson content, and automated recommendations). Subsequently, we present our empirical design to test the effectiveness of these changes in a real-world classroom setting. Brod, G., Kucirkova, N., Shepherd, J., Jolles, D., & Molenaar, I. (2023). Agency in educational technology: Interdisciplinary perspectives and implications for learning design. Educational Psychology Review, 35(1), 25.
Murty, V. P., DuBrow, S., & Davachi, L. (2015). The simple act of choosing influences declarative memory. Journal of Neuroscience, 35(16), 6255–6264.
Patall, E. A. (2013). Constructing motivation through choice, interest, and interestingness. Journal of Educational Psychology, 105, 522–534.
Patall, E. A., Vasquez, A. C., Steingut, R. R., Trimble, S. S., & Pituch, K. A. (2017). Supporting and thwarting autonomy in the high school science classroom. Cognition and Instruction, 35, 337–362.