Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students’ SRL while they learn about the human circulatory system. MetaTutor’s architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners’ cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

Cognitive fatigue emerges in wide-ranging tasks and domains, but traditional vigilance tasks provide a well-studied context in which to explore the mechanisms underlying it. Though a variety of experimental methodologies have been used to investigate cognitive fatigue in vigilance, relatively little research has utilized electroencephalography (EEG), specifically event-related potentials (ERPs), to explore the nature of cognitive fatigue, also known as the vigilance decrement. Moreover, much of the research that has been done on vigilance and ERPs uses non-traditional vigilance paradigms, limiting generalizability to the established body of behavioral results and corresponding theories. In this study, we address concerns with prior research by (1) investigating the vigilance decrement using a well-established visual vigilance task, (2) utilizing a task designed to attenuate possible confounding ERP components present within a vigilance paradigm, and (3) informing our interpretations with recent findings from ERP research. We averaged data across electrodes located over the frontal, central, and parietal scalp. Then, we generated waveforms locked to the onset of critical low-frequency or non-critical high-frequency events during a 40 min task that was segregated into time blocks for data analysis. There were three primary findings from the analyses of these data. First, mean amplitude of N1 was greater during later blocks for both low-frequency and high-frequency events, a contradictory finding compared to past visual vigilance studies that is further discussed with respect to current interpretations of the N1 in visual attention tasks. Second, P3b mean amplitude following low-frequency events was reduced during later blocks, with a later onset latency. Third and finally, the decrease in P3b amplitude correlated with individual differences in the magnitude of the vigilance decrement, assessed using d′. The results provide evidence for degradations of cognitive processing efficiency brought on by extended time on task, leading to delayed processing and decreased discriminability of critical stimuli from non-critical stimuli. These conclusions are discussed in the context of the vigilance decrement and corresponding theoretical accounts.

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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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