This chapter addresses how an attention-management system can provide personalized support for self-regulated learning and what the effects of this support are on learning. An attention-management system can provide personalized support by capturing and interpretating information from the student's environment. A framework is proposed that will interpret the information and provide dynamic scaffolding for the learner. The essential elements are diagnosing, calibrating and fading scaffolds to the context of the learner. An intervention model supports self-regulated learning processes. In two studies, we have found evidence that an attention-management system can effectively give form to dynamic scaffolding. Dynamic scaffolding has a small- to medium-sized effect on students' performance and a small effect on students' metacognitive knowledge acquisition.
Molenaar, I., van Boxtel, C., Sleegers, P., & Roda, C. (2011). Attention management for self-regulated learning: AtGentSchool. In C. Roda (Ed.), Human Attention in Digital Environments (pp. 259-280). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511974519.011