The monitoring of one's own learning progress is a key process in models of self-regulated learning and a key predictor of self-regulated learning and academic success. Judgments of learning (JOLs) are an established measure for assessing people's monitoring of learning and have been found to predict learners' subsequent performance as well as effort regulation. However, most studies have been conducted in laboratory settings, involving relatively artificial learning materials and low-stakes tests. We evaluate the predictive validity of JOLs for learning performance and effort regulation in an ecologically valid learning environment by requesting aggregate JOLs in an intelligent tutoring system. 90 German university students used an intelligent tutoring system that provided practice exercises for self-regulated preparation for a statistics exam over the course of a semester. Aggregate JOLs for each chapter of the statistics course were assessed once per week (279 assessments in total). Dependent variables were learning performance as well as absolute and relative learning effort for each chapter, derived from the intelligent tutoring system's log files. JOLs significantly predicted learning performance ( β = 0.20, p < .001) and effort regulation ( β absolute = −0.12, p < .001, β relative = −0.07, p = .002). The present research demonstrates that JOLs have predictive power in real-world learning. It thus bridges the gap between experimental cognitive research and applied educational research on metamemory and self-regulation. • Judgments of learning (JOLs) are predictions of one's own future performance. • Little is known about JOL accuracy in ecologically valid learning environments. • We examined JOL accuracy in an intelligent tutoring system used for exam preparation. • JOLs predicted effort regulation and performance during exam preparation.

Research has highlighted the relevance of affective processes for learning but often relies on self-reports without accounting for inter- and intraindividual dynamics. Multimodal research allows for a more comprehensive assessment of these dynamics. Most multimodal studies, however, rely on lab contexts, reducing ecological validity of results. The present study aims to address this gap. We examine situated inter- and intraindividual variability in concurrent activity emotions and affective activation and their interrelations with learning behaviour (prompt compliance) and domain knowledge after learning in an intelligent tutoring system. The sample consisted of 83 students ( M age = 15.52, SD = 1.90; girls = 56.6 %) from four secondary schools. We combined logfile data (prompt compliance), with electrodermal activity assessment (affective activation), standardized tests data (domain knowledge), and two types of self-report data: experience-sampling to capture concurrent activity emotions over time and topic-related emotions. Two-level dynamic structural equation models were applied. Results revealed concurrent emotions and prompt compliance to be self-predictive over time. Topic-related boredom and confusion were linked to concurrent boredom and confusion. Person-level affective activation negatively predicted person-level domain knowledge after learning, suggesting high activation may deplete cognitive resources. Topic-related boredom and confusion positively predicted increases in aggregated affective activation, suggesting these emotions drive activation. The study shows that once an emotion is established, it can persist for the learning session and that especially initial boredom can be detrimental for following learning processes and that particularly affective activation can deplete learning performance (domain knowledge after learning). • Assessed multimodal data while learning with ITS in ecological classrooms. • Examined affective dynamics, behavior, and domain knowledge after learning. • Concurrent emotions and prompt compliance were self-predictive. • Initial boredom and confusion shaped affective activation. • Affective activation related to lower domain knowledge after learning.

School systems are facing challenges in providing children with reading competence necessary for participating in a digital society while meeting the needs of an increasingly diverse student population. Educational inequalities persist, and are intensified by the digital divide, which shapes access to, use of, and benefit from digital media. We examine how students' socioeconomic background influences their access to and use of digital media, and how these factors relate to their reading development in a reading intervention, implemented in analog and digital contexts. Participants were 249 elementary students. The study involved a six-month reading intervention in three elementary schools. Classes were assigned to analog, digital, or control conditions. The intervention combined fluency practice, collaborative comprehension tasks, and creative activities. Pre- and post-test measures, including tests and questionnaires, were employed. Data were analyzed using a multi-step design incorporating descriptive tables, regression, and fixed-effects panel models. Overall intervention effects on reading competence were modest. The analog intervention showed small positive effects, while the digital format produced less consistent results. Effectiveness varies by school context, parental education, and language background. High levels of digital media use at home correlated with lower reading competence. The success of reading interventions depends less on the medium than on scaffolding, teacher implementation, and alignment with students’ starting skills. Students from highly educated families tended to benefit more, while digital familiarity alone did not confer an advantage. These results highlight the need for context-sensitive designs that address inequalities in both analog and digital learning environments. • Socioeconomic background influences access, use, and benefits of digital media. • Analog intervention showed modest positive effects on reading competence. • High digital media use at home was linked to lower reading skills. • Intervention effectiveness varied strongly by school context and resources.