Our results suggest that maternal-infant demographic and clinical factors and household neighborhood socioeconomic characteristics were associated with provision of maternal BM at 30 postnatal days to VLBW infants. Identification of these factors allows providers to anticipate mothers' needs and develop tailored interventions designed to improve rates of prolonged maternal BM provision and infant outcomes.

Computational thinking (CT) is being recognized as a critical component of student success in the digital era. Many contend that integrating CT into core curricula is the surest method for providing all students with access to CT. However, the CT community lacks an agreed-upon conceptualization of CT that would facilitate this integration, and little effort has been made to critically analyze and synthesize research on CT/content integration (CTCI). Conflicting CT conceptualizations and little understanding of evidence-based strategies for CTCI could result in significant barriers to increasing students’ access to CT. To address these concerns, we analyzed 80 studies on CT education, focusing on both the CT conceptualizations guiding current CT education research and evidence-based strategies for CTCI. Our review highlights the code-centric nature of CT education and reveals significant gaps in our understanding of CTCI and CT professional development for teachers. Based on these findings, we propose an approach to operationalizing CT that promotes students’ participation in CT, present promising methods for infusing content with CT, and discuss future directions for CT education research.

Teachers’ ability to self-regulate their own learning is closely related to their competency to enhance self-regulated learning (SRL) in their students. Accordingly, there is emerging research for the design of teacher dashboards that empower instructors by providing access to quantifiable evidence of student performance and SRL processes. Typically, they capture evidence of student learning and performance to be visualized through activity traces (e.g., bar charts showing correct and incorrect response rates, etc.) and SRL data (e.g., eye-tracking on content, log files capturing feature selection, etc.) in order to provide teachers with monitoring and instructional tools. Critics of the current research on dashboards used in conjunction with advanced learning technologies (ALTs) such as simulations, intelligent tutoring systems, and serious games, argue that the state of the field is immature and has 1) focused only on exploratory or proof-of-concept projects, 2) investigated data visualizations of performance metrics or simplistic learning behaviors, and 3) neglected most theoretical aspects of SRL including teachers’ general lack of understanding their’s students’ SRL. Additionally, the work is mostly anecdotal, lacks methodological rigor, and does not collect critical process data (e.g. frequency, duration, timing, or fluctuations of cognitive, affective, metacognitive, and motivational (CAMM) SRL processes) during learning with ALTs used in the classroom. No known research in the areas of learning analytics, teacher dashboards, or teachers’ perceptions of students’ SRL and CAMM engagement has systematically and simultaneously examined the deployment, temporal unfolding, regulation, and impact of all these key processes during complex learning. In this manuscript, we 1) review the current state of ALTs designed using SRL theoretical frameworks and the current state of teacher dashboard design and research, 2) report the important design features and elements within intelligent dashboards that provide teachers with real-time data visualizations of their students’ SRL processes and engagement while using ALTs in classrooms, as revealed from the analysis of surveys and focus groups with teachers, and 3) propose a conceptual system design for integrating reinforcement learning into a teacher dashboard to help guide the utilization of multimodal data collected on students’ and teachers’ CAMM SRL processes during complex learning.

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